%% Genetic Programming Bibliography %%$Revision: 1.8264 $ $Date: 2025/03/23 13:22:39 $ %%Created by W.B.Langdon cs.ucl.ac.nl January 1995 %%Based on J.Koza's GP bibliography of 14 March 1994 %% To add references to your papers see %% ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/biblio/ %%optional @Proceedings{toc:2011:cec, key = "CEC 2011", title = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", month = jun, DOI = "doi:10.1109/CEC.2011.5949582", notes = "Also known as \cite{5949582}", } @Proceedings{cover:2010:MECHATRONIKA, key = "MECHATRONIKA, 2010", title = "13th International Symposium MECHATRONIKA, 2010", year = "2010", month = jun, URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5521207", notes = "Also known as \cite{5521207}", } @Article{tagkey1997126, title = "Genetic programming: Proceedings of the first annual conference 1996 : Edited by John R. Koza, David E. Goldberg, David B. Fogel and Rick L. Riolo. MIT Press, Cambridge, MA. (1996). 568 pages. \$75.00", journal = "Computers \& Mathematics with Applications", volume = "33", number = "5", pages = "126--127", year = "1997", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(97)00025-4", URL = "http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-D/2/23afe396341b39baf74fcd29db315b46", key = "tagkey1997126", notes = "No author given. Contents listing of \cite{koza:gp96}", } @Article{tagkey1997129, title = "Advances in genetic programming, volume 2 : Edited by Peter Angeline and Kenneth Kinnear, Jr. MIT Press, Cambridge, MA. (1996). 538 pages. \$50.00", journal = "Computers \& Mathematics with Applications", volume = "33", number = "5", pages = "129", year = "1997", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(97)82933-1", URL = "http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-T/2/4d3bcc2dda31e9aca679eba60ff95a3a", key = "tagkey1997129", size = "0.5 pages", notes = "Contents listing of \cite{book:1996:aigp2}. No author given. To get, try other articles on page 129", } @Article{tagkey1999291, title = "Advances in genetic programming, volume III : Edited by Lee Spector, William B. Langdon, Una-May O'Reilly and Peter J. Angeline. MIT Press, Cambridge, MA. (1999). 476 pages. \$55.00", journal = "Computers \& Mathematics with Applications", volume = "38", number = "11-12", pages = "291--291", year = "1999", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(99)91267-1", URL = "http://www.sciencedirect.com/science/article/B6TYJ-48778B1-3H/2/1d6f4728f10e14a24f4f28189d15f818", key = "tagkey1999291", notes = "Contents listing of \cite{spector:1999:aigp3}. No author given.", } @Article{tagkey1999132, title = "Genetic programming and data structures: Genetic programming + data STRUCTURES = automatic programming! : By W. B. Langdon. Kluwer Academic Publishers, Boston, MA. (1998). 278 pages. \$125.00. NLG 285.00, GBP 85.00", journal = "Computers \& Mathematics with Applications", volume = "37", number = "3", pages = "132--132", year = "1999", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(99)90375-9", DOI = "doi:10.1016/S0898-1221(99)90239-0", URL = "http://www.sciencedirect.com/science/article/B6TYJ-489YTT5-2T/2/13179f12104abafe66b36e402ef358d9", key = "tagkey1999132", notes = "Contents listing of \cite{langdon:book}. No author given.", } @Article{tagkey1995115, title = "Genetic programming II: Automatic discovery of reusable programs : By John R. Koza. MIT Press, Cambridge, MA. (1994). 746 pages. \$45.00", journal = "Computers \& Mathematics with Applications", volume = "29", number = "3", pages = "115--115", year = "1995", ISSN = "0898-1221", DOI = "doi:10.1016/0898-1221(95)90099-3", URL = "http://www.sciencedirect.com/science/article/B6TYJ-48F4PJH-H/2/bd467ac24453cb0b3f9dbbf15075bedb", key = "tagkey1995115", notes = "Contents listing of \cite{koza:gp2}. No author given.", } @Article{tagkey1999282, title = "Evolutionary algorithms in engineering and computer science: Recent advances in genetic algorithms, evolution strategies, evolutionary programming, genetic programming and industrial applications : Edited by K. Miettinen, P. Neittaanmaki, M. M. Makela and J. Periaux. John Wiley \& Sons, Ltd., Chichester. (1999). pounds60.00", journal = "Computers \& Mathematics with Applications", volume = "38", number = "11-12", pages = "282--282", year = "1999", ISSN = "0898-1221", DOI = "doi:10.1016/S0898-1221(99)91189-6", URL = "http://www.sciencedirect.com/science/article/B6TYJ-48778B1-24/2/ee28594e33abf3bd7c4a9fc997b98492", key = "tagkey1999282", } @Article{tagkey2002475, title = "Automated generation of robust error recovery logic in assembly systems using genetic programming : Cem M. Baydar, Kazuhiro Saitou, v20, n1, 2001, pp55-68", journal = "Journal of Manufacturing Systems", volume = "21", number = "6", pages = "475--476", year = "2002", ISSN = "0278-6125", DOI = "doi:10.1016/S0278-6125(02)80094-2", URL = "http://www.sciencedirect.com/science/article/B6VJD-4920DSC-1N/2/93bf79c7eb0d6ad94d169ed1b37ec77f", key = "tagkey2002475", notes = "Abstract of \cite{Baydar200155}", } @Misc{2018:MITtechreview, title = "Intelligent Machines Evolutionary algorithm outperforms deep-learning machines at video games", howpublished = "MIT Technolgy Review", key = "MIT Technolgy Review", year = "2018", month = "18 " # jul, keywords = "genetic algorithms, genetic programming", broken = "https://www.technologyreview.com/s/611568", size = "9 pages", abstract = "Neural networks have garnered all the headlines, but a much more powerful approach is waiting in the wings. by Emerging Technology from the arXiv July 18, 2018 Summary of https://arxiv.org/pdf/1806.05695 See instead \cite{Wilson:2018:GECCO}", notes = "No author given. another bit of electonic detritus?", } @Article{Sapienz:2018:sigevolution, title = "Evolutionary Algorithms for Software Testing in {Facebook}", journal = "SIGEVOlution", key = "SIGEVOlution", year = "2018", volume = "11", number = "2", pages = "7", month = "12 " # jul, keywords = "genetic algorithms, genetic programming, SBSE, mobile computing, smart phone", ISSN = "1931-8499", video_url = "https://www.youtube.com/watch?v=j3eV8NiWLg4", URL = "http://www.sigevolution.org/issues/SIGEVOlution1102.pdf", DOI = "doi:10.1145/3264700.3264702", size = "1 page", abstract = "Sapienz is an approach to Android testing that uses multi-objective evolutionary algorithms to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. It is in production now helping to improve the quality of Facebook software!", notes = "Yue Jia, Mark Harman, Ke Mao. Cites ISSTA 2016 \cite{mao:sapienz:16} https://doi.org/10.1145/2931037.2931054 https://github.com/Rhapsod/sapienz Sapienz Prototype (Out-of-date) MaJiCKe Sapienz Multiobjective Automated Android Testing", } @Misc{glasilo_1_16_ang, title = "Store Steel 165 years", howpublished = "Internal information magazine", key = "Store Steel", keywords = "genetic algorithms, genetic programming", URL = "http://www.store-steel.si/Data/InterniInformativniCasopis/glasilo_1_16_ang.pdf", size = "16 pages", notes = "a few paragraphs on Miha Kovacic. No author given.", } @Article{ababsa:2018:IJAC, author = "Tarek Ababsa and Noureddine Djedl and Yves Duthen", title = "Genetic programming-based self-reconfiguration planning for metamorphic robot", journal = "International Journal of Automation and Computing", year = "2018", volume = "15", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11633-016-1049-4", DOI = "doi:10.1007/s11633-016-1049-4", } @InProceedings{Ababsa:2022:ISNIB, author = "Tarek Ababsa", booktitle = "2022 International Symposium on iNnovative Informatics of Biskra (ISNIB)", title = "A {SIMD} Interpreter for Linear Genetic Programming", year = "2022", abstract = "Genetic programming (GP) has been applied as an automatic programming tool to solve various kinds of problems by genetically breeding a population of computer programs using biologically inspired operations. However, it is well known as a computationally demanding approach with a significant potential of parallelization. In this paper, we emphasize parallelizing the evaluation of genetic programs on Graphics Processing Unit (GPU). We used a compact representation for genotypes. This representation is a memory-efficient method that allows efficient evaluation of programs. Our implementation clearly distinguishes between an individual's genotype and phenotype. Thus, the individuals are represented as linear entities (arrays of 32 bits integers) that are decoded and expressed just like nonlinear entities (trees).", keywords = "genetic algorithms, genetic programming, linear genetic programming, GPU, Graphics, Automatic programming, Sociology, Graphics processing units, Arrays, Statistics, Parallel Processing, GPGPU, symbolic regression", DOI = "doi:10.1109/ISNIB57382.2022.10075819", month = dec, notes = "Also known as \cite{10075819}", } @InProceedings{Abarghouei:2009:SOCPAR, author = "Amir Atapour Abarghouei and Afshin Ghanizadeh and Saman Sinaie and Siti Mariyam Shamsuddin", title = "A Survey of Pattern Recognition Applications in Cancer Diagnosis", booktitle = "International Conference of Soft Computing and Pattern Recognition, SOCPAR '09", year = "2009", month = dec, pages = "448--453", keywords = "genetic algorithms, genetic programming, artificial neural networks, cancer diagnosis, image processing, medical images, pattern recognition applications, wavelet analysis, cancer, medical image processing, pattern recognition", DOI = "doi:10.1109/SoCPaR.2009.93", abstract = "In this paper, some of the image processing and pattern recognition methods that have been used on medical images for cancer diagnosis are reviewed. Previous studies on Artificial Neural Networks, Genetic Programming, and Wavelet Analysis are described with their working process and advantages. The definition of each method is provided in this study, and the acknowledgment is granted for previous related research activities.", notes = "Also known as \cite{5368648}", } @Article{ABBA:2020:JH, author = "S. I. Abba and Sinan Jasim Hadi and Saad Sh. Sammen and Sinan Q. Salih and R. A. Abdulkadir and Quoc Bao Pham and Zaher Mundher Yaseen", title = "Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination", journal = "Journal of Hydrology", volume = "587", pages = "124974", year = "2020", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2020.124974", URL = "http://www.sciencedirect.com/science/article/pii/S0022169420304340", keywords = "genetic algorithms, genetic programming, Water quality index, Watershed management, Extreme Gradient Boosting, Extreme Learning Machine, Kinta River", abstract = "Anthropogenic activities affect the water bodies and result in a drastic reduction of river water quality (WQ). The development of a reliable intelligent model for evaluating the suitability of water remains a challenging task facing hydro-environmental engineers. The current study is investigated the applicability of Extreme Gradient Boosting (XGB) and Genetic Programming (GP) in obtaining feature importance, and then abstracted input variables were imposed into the predictive model (the Extreme Learning Machine (ELM)) for the prediction of water quality index (WQI). The stand-alone modeling schema is compared with the proposed hybrid models where the optimum variables are supplied into the GP, XGB, linear regression (LR), stepwise linear regression (SWLR) and ELM models. The WQ data is obtained from the Department of Environment (DoE) (Malaysia), and results are evaluated in terms of determination coefficient (R2) and root mean square error (RMSE). The results demonstrated that the hybrid GPELM and XGBELM models outperformed the standalone GP, XGB, and ELM models for the prediction of WQI at Kinta River basin. A comparison of the hybrid models showed that the predictive skill of GPELM (RMSE = 3.441 training and RMSE = 3.484 testing) over XGBELM improving the accuracy by decreasing the values of RMSE by 5percent and 9percent for training and testing, respectively with regards to XGBELM (RMSE = 3.606 training and RMSE = 3.816 testing). Although regressions are often proposed as reference models (LR and SWLR), when combined with computational intelligence, they still provide satisfactory results in this study. The proposed hybrid GPELM and XGBELM models have improved the prediction accuracy with minimum number of input variables and can therefore serve as reliable predictive tools for WQI at Kinta River basin", } @Article{journals/iajit/AbbasiSA14, author = "Almas Abbasi and Woo Chaw Seng and Imran Shafiq Ahmad", title = "Multi Block based Image Watermarking in Wavelet Domain Using Genetic Programming", journal = "The International Arab Journal of Information Technology", year = "2014", number = "6", volume = "11", pages = "582--589", keywords = "genetic algorithms, genetic programming, Robust watermark, wavelet domain, digital watermarking, HVS", ISSN = "1683-3198", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/iajit/iajit11.html#AbbasiSA14", broken = "http://ccis2k.org/iajit/?option=com_content&task=blogcategory&id=94&Itemid=364", URL = "https://iajit.org/PDF/vol.11,no.6/6348.pdf", URL = "https://www.semanticscholar.org/paper/Multi-block-based-image-watermarking-in-wavelet-do-Abbasi-Seng/f7172a8a0b6d15ddedf81fc5a98117ff2078a89c", size = "8 pages", abstract = "The increased use of the Internet in sharing and distribution of digital data makes it is very difficult to maintain copyright and ownership of data. Digital watermarking offers a method for authentication and copyright protection. We propose a blind, still image, Genetic Programming (GP) based robust watermark scheme for copyright protection. In this scheme, pseudorandom sequence of real number is used as watermark. It is embedded into perceptually significant blocks of vertical and horizontal sub-band in wavelet domain to achieve robustness. GP is used to structure the watermark for improved imperceptibility by considering the Human Visual System (HVS) characteristics such as luminance sensitivity and self and neighbourhood contrast masking. We also present a GP function which determines the optimal watermark strength for selected coefficients irrespective of the block size. Watermark detection is performed using correlation. Our experiments show that in proposed scheme the watermark resists image processing attack, noise attack, geometric attack and cascading attack. We compare our proposed technique with other two genetic perceptual model based techniques. Comparison results show that our multiblock based technique is approximately 5percent, and 23percent more robust, then the other two compared techniques.", } @InProceedings{DBLP:conf/ssci/AbbasiAW21, author = "Muhammad Shabbir Abbasi and Harith Al-Sahaf and Ian Welch", title = "Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming", booktitle = "IEEE Symposium Series on Computational Intelligence, SSCI 2021", pages = "1--8", publisher = "IEEE", year = "2021", month = dec # " 5-7", address = "Orlando, FL, USA", keywords = "genetic algorithms, genetic programming Symbolic regression, ransomware, malice scoring", isbn13 = "978-1-7281-9049-5", timestamp = "Thu, 03 Feb 2022 09:28:31 +0100", biburl = "https://dblp.org/rec/conf/ssci/AbbasiAW21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1109/SSCI50451.2021.9660009", DOI = "doi:10.1109/SSCI50451.2021.9660009", size = "8 pages", abstract = "Malice or severity scoring models are a technique for detection of maliciousness. A few ransom-ware detection studies use malice scoring models for detection of ransomware-like behaviour. These models rely on the weighted sum of some manually chosen features and their weights by a domain expert. To automate the modelling of malice scoring for ransomware detection, we propose a method based on Genetic Programming (GP) that automatically evolves a behavior-based malice scoring model by selecting appropriate features and functions from the input feature and operator sets. The experimental results show that the best-evolved model correctly assigned a malice score, below the threshold value to over 85percent of the unseen goodware instances, and over the threshold value to more than 99percent of the unseen ransomware instances.", } @Article{Abbaspour:2013:WSE, author = "Akram Abbaspour and Davood Farsadizadeh and Mohammad Ali Ghorbani", title = "Estimation of hydraulic jump on corrugated bed using artificial neural networks and genetic programming", journal = "Water Science and Engineering", volume = "6", number = "2", pages = "189--198", year = "2013", ISSN = "1674-2370", DOI = "doi:10.3882/j.issn.1674-2370.2013.02.007", URL = "http://www.sciencedirect.com/science/article/pii/S1674237015302362", abstract = "Artificial neural networks (ANNs) and genetic programming (GP) have recently been used for the estimation of hydraulic data. In this study, they were used as alternative tools to estimate the characteristics of hydraulic jumps, such as the free surface location and energy dissipation. The dimensionless hydraulic parameters, including jump depth, jump length, and energy dissipation, were determined as functions of the Froude number and the height and length of corrugations. The estimations of the ANN and GP models were found to be in good agreement with the measured data. The results of the ANN model were compared with those of the GP model, showing that the proposed ANN models are much more accurate than the GP models.", keywords = "genetic algorithms, genetic programming, artificial neural networks, corrugated bed, Froude number, hydraulic jump", } @InProceedings{Abbass:2002:WCCI, publisher_address = "Piscataway, NJ, USA", author = "H. Abbass and N. X. Hoai and R. I. (Bob) McKay", booktitle = "Proceedings, 2002 World Congress on Computational Intelligence", DOI = "doi:10.1109/CEC.2002.1004490", notes = "Refereed International Conference Papers", pages = "1654--1666", publisher = "IEEE Press", title = "AntTAG: A New Method to Compose Computer Programs Using Colonies of Ants", URL = "http://sc.snu.ac.kr/PAPERS/TAGACOcec02.pdf", volume = "2", year = "2002", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "Genetic Programming (GP) plays the primary role for the discovery of programs through evolving the program's set of parse trees. In this paper, we present a new technique for constructing programs through Ant Colony Optimisation (ACO) using the tree adjunct grammar (TAG) formalism. We call the method AntTAG and we show that the results are very promising.", } @InProceedings{abbattista:1999:SAGAACS, author = "Fabio Abbattista and Valeria Carofiglio and Mario Koppen", title = "Scout Algorithms and Genetic Algorithms: A Comparative Study", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "769", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-803.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-803.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{abbod2007, author = "Maysam F. Abbod and M. Mahfouf and D. A. Linkens and C. M. Sellars", title = "Evolutionary Computing for Metals Properties Modelling", booktitle = "THERMEC 2006", year = "2006", volume = "539", pages = "2449--2454", series = "Materials Science Forum", address = "Vancouver", publisher_address = "Switzerland", month = jul # " 4-8", publisher = "Trans Tech Publications", keywords = "genetic algorithms, genetic programming, strain, alloy materials, modeling, material property, stress", ISSN = "1662-9752", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1011.6271", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.6271", URL = "http://www.scientific.net/MSF.539-543.2449.pdf", DOI = "doi:10.4028/www.scientific.net/MSF.539-543.2449", size = "6 pages", abstract = "During the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which uses GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are used as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.", notes = "Published Feb 2007 in Materials Science Forum ?", } @InProceedings{Abbona:2020:CEC, author = "Francesca Abbona and Leonardo Vanneschi and Marco Bona and Mario Giacobini", title = "A {GP} Approach for Precision Farming", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24248", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Cows, Precision Livestock Farming, PLF, Cattle Breeding, Piedmontese Bovines", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185637", size = "8 pages", abstract = "Livestock is increasingly treated not just as food containers, but as animals that can be susceptible to stress and diseases, affecting, therefore, the production of offspring and the performance of the farm. The breeder needs a simple and useful tool to make the best decisions for his farm, as well as being able to objectively check whether the choices and investments made have improved or worsened its performance. The amount of data is huge but often dispersive: it is therefore essential to provide the farmer with a clear and comprehensible solution, that represents an additional investment. This research proposes a genetic programming approach to predict the yearly number of weaned calves per cow of a farm, namely the measure of its performance. To investigate the efficiency of genetic programming in such a problem, a dataset composed by observations on representative Piedmontese breedings was used. The results show that the algorithm is appropriate, and can perform an implicit feature selection, highlighting important variables and leading to simple and interpretable models.", notes = "Cow calves, north itally. ANABORAPI. perinatal mortality death during weaning (60 days). GPlab Matlab. Kruskal-Wallis stats test. Natural v. artificial insemination. 2017, 2018 data. Crossover, mutation, shrink mutaion swap mutation. mydivide Herd size. GP8 comphrensible evolved model. Time between calve birth and next calf birth. Department of Veterinary Sciences, University of Torino. ANABORAPI, Associazione Nazionale Allevatori Bovini Razza Piemontese https://wcci2020.org/ Also known as \cite{9185637}", } @Article{ABBONA:2020:LS, author = "Francesca Abbona and Leonardo Vanneschi and Marco Bona and Mario Giacobini", title = "Towards modelling beef cattle management with Genetic Programming", journal = "Livestock Science", year = "2020", volume = "241", pages = "104205", keywords = "genetic algorithms, genetic programming, Precision livestock farming, Evolutionary algorithms, Machine learning, Cattle breeding, Piemontese bovines", ISSN = "1871-1413", URL = "https://iris.unito.it/retrieve/e27ce430-63b3-2581-e053-d805fe0acbaa/Abbona2020_LS_OA.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S1871141320302481", DOI = "doi:10.1016/j.livsci.2020.104205", abstract = "Among the Italian Piemontese Beef Breedings, the yearly production of calves weaned per cow, that is the calves that survive during the period of 60 days following birth, is identified as the main target expressing the performance of a farm. modeling farm dynamics in order to predict the value of this parameter is a possible solution to investigate and highlight breeding strengths, and to find alternatives to penalizing factors. The identification of such variables is a complex but solvable task, since the amount of recorded data among livestock is nowadays huge and manageable through Machine Learning techniques. Besides, the evaluation of the effectiveness of the type of management allows the breeder to consolidate the ongoing processes or, on the contrary, to adopt new management strategies. To solve this problem, we propose a Genetic Programming approach, a white-box technique suitable for big data management, and with an intrinsic ability to select important variables, providing simple models. The most frequent variables encapsulated in the models built by Genetic Programming are highlighted, and their zoological significance is investigated a posteriori, evaluating the performance of the prediction models. Moreover, two of the final expressions selected only three variables among the 48 given in input, one of which is the best performing among GP models. The expressions were then analyzed in order to propose a zootechnical interpretation of the equations. Comparisons with other common techniques, including also black-box methods, are performed, in order to evaluate the performance of different type of methods in terms of accuracy and generalization ability. The approach entailed constructive and helpful considerations to the addressed task, confirming its key-role in the zootechnical field, especially in the beef breeding management", } @Article{abbona:2022:AS, author = "Francesca Abbona and Leonardo Vanneschi and Mario Giacobini", title = "Towards a Vectorial Approach to Predict Beef Farm Performance", journal = "Applied Sciences", year = "2022", volume = "12", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/3/1137", DOI = "doi:10.3390/app12031137", abstract = "Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.", notes = "also known as \cite{app12031137}", } @InProceedings{aicsu-91:abbot, author = "R. J. Abbott", title = "Niches as a GA divide-and-conquer strategy", booktitle = "Proceedings of the Second Annual AI Symposium for the California State University", year = "1991", editor = "Art Chapman and Leonard Myers", pages = "133--136", publisher = "California State University", keywords = "genetic algorithms, genetic programming", } @InProceedings{abbott:2003:OOGP, author = "Russell J. Abbott", title = "Object-Oriented Genetic Programming, An Initial Implementation", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming, object-oriented, STGP", URL = "http://abbott.calstatela.edu/PapersAndTalks/OOGP.pdf", size = "4 pages", abstract = "This paper describes oogp, an object-oriented genetic programming system. Oogp provides traditional genetic programming capabilities in an object-oriented framework. Among the advantages of object-oriented genetic programming are: (a) strong typing, (b) availability of existing class libraries for inclusion in generated programs, and (c) straightforward extensibility to include features such as iteration as object-oriented methods. Oogp is written in Java and makes extensive use of Java's reflection capabilities. Oogp includes a relatively straightforward but apparently innovative simplification capability.", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ parity Assignment-Stmt, Block-Stmt, If-Stmt, and While-Stmt Automatic simplification {"}A limit may be placed on the number of steps allowed. When exceeded, an ExcessiveStepsException is thrown{"} Iteration cites \cite{HPL-2001-327}.", } @InProceedings{abbott:2003:MLMTA, author = "Russ Abbott and Jiang Guo and Behzad Parviz", title = "Guided Genetic Programming", booktitle = "The 2003 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA'03)", year = "2003", address = "las Vegas", month = "23-26 " # jun, publisher = "CSREA Press", keywords = "genetic algorithms, genetic programming, guided genetic programming", URL = "http://abbott.calstatela.edu/PapersAndTalks/Guided%20Genetic%20Programming.pdf", size = "7 pages", abstract = "We argue that genetic programming has not made good on its promise to generate computer programs automatically. It then describes an approach that would allow that promise to be fulfilled by running a genetic programming engine under human guidance.", notes = "http://www.ashland.edu/~iajwa/conferences/2003/MLMTA/ Oogp Java. Sort (all ArrayList methods and iterate() loop and insertAsc()). Guided GP 3 populations (coevolution) {"}The user contributes...a top-down analysis{"}. {"}Allowing a user to suggest building blocks may be a reasonable compromise{"}. Cites \cite{langdon:book} \cite{HPL-2001-327} \cite{ppsn92:oReilly} \cite{icga93:kinnear}", } @InProceedings{DBLP:conf/icai/AbbottPS04, author = "Russ Abbott and Behzad Parviz and Chengyu Sun", title = "Genetic Programming Reconsidered", year = "2004", pages = "1113--1116", bibsource = "DBLP, http://dblp.uni-trier.de", editor = "Hamid R. Arabnia and Youngsong Mun", publisher = "CSREA Press", booktitle = "Proceedings of the International Conference on Artificial Intelligence, IC-AI '04, Volume 2 {\&} Proceedings of the International Conference on Machine Learning; Models, Technologies {\&} Applications, MLMTA '04", volume = "2", address = "Las Vegas, Nevada, USA", month = jun # " 21-24", keywords = "genetic algorithms, genetic programming, evolutionary pathway, fitness function, teleological evolution, adaptive evolution", ISBN = "1-932415-32-7", URL = "http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf", URL = "https://dblp.org/rec/conf/icai/AbbottPS04.html?view=bibtex", size = "4 pages", abstract = "Even though the Genetic Programming (GP) mechanism is capable of evolving any computable function, the means through which it does so is inherently flawed: the user must provide the GP engine with an evolutionary pathway toward a solution. Hence Genetic Programming is problematic as a mechanism for generating creative solutions to specific problems.", notes = "sort, fitness function cheat ", } @Article{abdel-kader:2022:Infrastructures, author = "Mohamed Y. Abdel-Kader and Ahmed M. Ebid and Kennedy C. Onyelowe and Ibrahim M. Mahdi and Ibrahim Abdel-Rasheed", title = "{(AI)} in Infrastructure Projects-Gap Study", journal = "Infrastructures", year = "2022", volume = "7", number = "10", pages = "Article No. 137", keywords = "genetic algorithms, genetic programming", ISSN = "2412-3811", URL = "https://www.mdpi.com/2412-3811/7/10/137", DOI = "doi:10.3390/infrastructures7100137", abstract = "Infrastructure projects are usually complicated, expensive, long-term mega projects; accordingly, they are the type of projects that most need optimisation in the design, construction and operation stages. A great deal of earlier research was carried out to optimise the performance of infrastructure projects using traditional management techniques. Recently, artificial intelligence (AI) techniques were implemented in infrastructure projects to improve their performance and efficiency due to their ability to deal with fuzzy, incomplete, inaccurate and distorted data. The aim of this research is to collect, classify, analyse and review all of the available previous research related to implementing AI techniques in infrastructure projects to figure out the gaps in the previous studies and the recent trends in this research area. A total of 159 studies were collected since the beginning of the 1990s until the end of 2021. This database was classified based on publishing date, infrastructure subject and the used AI technique. The results of this study show that implementing AI techniques in infrastructure projects is rapidly increasing. They also indicate that transportation is the first and the most AI-using project and that both artificial neural networks (ANN) and particle swarm optimisation (PSO) are the most implemented techniques in infrastructure projects. Finally, the study presented some opportunities for farther research, especially in natural gas projects.", notes = "also known as \cite{infrastructures7100137}", } @Article{ABDELALEEM:2022:tws, author = "Basem H. AbdelAleem and Mohamed K. Ismail and May Haggag and Wael El-Dakhakhni and Assem A. A. Hassan", title = "Interpretable soft computing predictions of elastic shear buckling in tapered steel plate girders", journal = "Thin-Walled Structures", volume = "176", pages = "109313", year = "2022", ISSN = "0263-8231", DOI = "doi:10.1016/j.tws.2022.109313", URL = "https://www.sciencedirect.com/science/article/pii/S026382312200235X", keywords = "genetic algorithms, genetic programming, Data-driven models, Elastic shear buckling strength, Multi-gene genetic programming, Variable importance, Partial dependence plots, Tapered end web panel", abstract = "The complexity of the shear buckling in tapered plate girders has motivated researchers to conduct experimental and numerical investigations to understand the underlying mechanisms controlling such phenomenon, and subsequently develop related design-oriented expressions. However, existing predictive models have been developed and validated using limited datasets and/or traditional regression techniques-restricting both the model utility, when considering a wider range of design parameters, and the model generalizability, due to associated uncertainties. To address these issues, the present study employed a powerful soft computing technique-multi-gene genetic programming (MGGP), to develop design expressions to predict the elastic shear buckling strength of tapered end plate girder web panels. A dataset of 427 experimental and experimentally validated numerical results was used in training, validating, and testing the developed MGGP models. Guided by mechanics and findings from previous studies, the key parameters controlling the strength were identified, and MGGP were employed to reveal the interdependence between such parameters and subsequently develop interpretable predictive models. The prediction accuracy of the developed models was evaluated against that of other existing models using various statistical measures. Several filter and embedded variable importance techniques were used to rank the model input parameters according to their significance in predicting the elastic shear buckling strength. These techniques include the variable importance random forest and the relative influence gradient boosting techniques. Moreover, partial dependence plots were employed to explore the effect of the input variables on the strength. The results obtained from this study demonstrated the robustness of the developed MGGP expression for predicting the elastic shear buckling strength of tapered plate girder end web panel. The developed model also exhibited a superior prediction accuracy and generalizability compared to currently existing ones. Furthermore, the developed partial dependence plots facilitated interpreting the influence of all input variables on the predicted elastic shear buckling strength", } @InProceedings{Abdelaziz:2012:SEMCCO, author = "Almoataz Y. Abdelaziz and S. F. Mekhamer and H. M. Khattab and M. L. A. Badr and Bijaya Ketan Panigrahi", title = "Gene Expression Programming Algorithm for Transient Security Classification", booktitle = "Proceedings of the Third International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012", year = "2012", editor = "Bijaya Ketan Panigrahi and Swagatam Das and Ponnuthurai Nagaratnam Suganthan and Pradipta Kumar Nanda", volume = "7677", series = "Lecture Notes in Computer Science", pages = "406--416", address = "Bhubaneswar, India", month = dec # " 20-22", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-35379-6", DOI = "doi:10.1007/978-3-642-35380-2_48", bibsource = "OAI-PMH server at works.bepress.com", oai = "oai:works.bepress.com:almoataz_abdelaziz-1083", URL = "http://works.bepress.com/almoataz_abdelaziz/42", size = "11 pages", abstract = "In this paper, a gene expression programming (GEP) based algorithm is implemented for power system transient security classification. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology applies the GEP for the first time in transient security assessment and classification problems of power systems. The proposed algorithm is examined using different IEEE standard test systems. Power system three phase short circuit contingency has been used to test the proposed algorithm. The algorithm checks the static security status of the power system then classifies the transient security of the power system as secure or not secure. Performance of the algorithm is compared with other neural network based classification algorithms to show its superiority for transient security classification.", } @Article{Abdelbaky:2018:IJACSA, author = "Ibrahim Z. Abdelbaky and Ahmed F. Al-Sadek and Amr A. Badr", title = "Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors", journal = "International Journal of Advanced Computer Science and Applications", year = "2018", number = "11", volume = "9", pages = "229--235", keywords = "genetic algorithms, genetic programming, cdk inhibitors, random forest classification", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2018.091132", URL = "http://thesai.org/Downloads/Volume9No11/Paper_32-Applying_Machine_Learning_Techniques.pdf", DOI = "doi:10.14569/IJACSA.2018.091132", size = "7 pages", abstract = "The importance of protein kinases made them a target for many drug design studies. They play an essential role in cell cycle development and many other biological processes. Kinases are divided into different subfamilies according to the type and mode of their enzymatic activity. Computational studies targeting kinase inhibitors identification is widely considered for modelling kinase-inhibitor. This modelling is expected to help in solving the selectivity problem arising from the high similarity between kinases and their binding profiles. In this study, we explore the ability of two machine-learning techniques in classifying compounds as inhibitors or non-inhibitors for two members of the cyclin-dependent kinases as a subfamily of protein kinases. Random forest and genetic programming were used to classify CDK5 and CDK2 kinases inhibitors. This classification is based on calculated values of chemical descriptors. In addition, the response of the classifiers to adding prior information about compounds promiscuity was investigated. The results from each classifier for the datasets were analysed by calculating different accuracy measures and metrics. Confusion matrices, accuracy, ROC curves, AUC values, F1 scores, and Matthews correlation, were obtained for the outputs. The analysis of these accuracy measures showed a better performance for the RF classifier in most of the cases. In addition, the results show that promiscuity information improves the classification accuracy, but its significant effect was notably clear with GP classifiers.", } @InProceedings{Abdelbar:aspgp03, author = "Ashraf M. Abdelbar and Sherif Ragab and Sara Mitri", title = "Applying Co-Evolutionary Particle Swam Optimization to the Egyptian Board Game Seega", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "9--15", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "Particle Swarm Optimisation, Co-evolution, Game", ISBN = "0-9751724-0-9", URL = "http://infoscience.epfl.ch/record/90539/", URL = "http://infoscience.epfl.ch/record/90539/files/abdelbar03applyingParticle.pdf", abstract = "Seega is an ancient Egyptian two-phase board game that, in certain aspects, is more difficult than chess. The two-player game is played on either a 5 x 5, 7 x 7, or 9 x 9 board. In the first and more difficult phase of the game, players take turns placing one disk each on the board until the board contains only one empty cell. In the second phase players take turns moving disks of their colour; a disk that becomes surrounded by disks of the opposite color is captured and removed from the board. We have developed a Seega program that employs co-evolutionary particle swarm optimisation in the generation of feature evaluation scores. Two separate swarms are used to evolve White players and Black players, respectively; each particle represents feature weights for use in the position evaluation. Experimental results are presented and the performance of the full game engine is discussed.", notes = "PSO, Not GP \cite{aspgp03}", } @InProceedings{Abdelbari:2017:ICCMS, author = "Hassan Abdelbari and Kamran Shafi", title = "A Genetic Programming Ensemble Method for Learning Dynamical System Models", booktitle = "Proceedings of the 8th International Conference on Computer Modeling and Simulation", year = "2017", pages = "47--51", address = "Canberra, Australia", publisher = "ACM", keywords = "genetic algorithms, genetic programming, complex dynamical systems, modelling and simulation, symbolic regression", isbn13 = "978-1-4503-4816-4", URL = "http://doi.acm.org/10.1145/3036331.3036336", DOI = "doi:10.1145/3036331.3036336", acmid = "3036336", abstract = "Modelling complex dynamical systems plays a crucial role to understand several phenomena in different domains such as physics, engineering, biology and social sciences. In this paper, a genetic programming ensemble method is proposed to learn complex dynamical systems underlying mathematical models, represented as differential equations, from system time series observations. The proposed method relies on decomposing the modelling space based on given variable dependencies. An ensemble of learners is then applied in this decomposed space and their output is combined to generate the final model. Two examples of complex dynamical systems are used to test the performance of the proposed methodology where the standard genetic programming method has struggled to find matching model equations. The empirical results show the effectiveness of the proposed methodology in learning closely matching structure of almost all system equations.", notes = "conf/iccms/AbdelbariS17", } @Article{abdelbari:2019:Systems, author = "Hassan Abdelbari and Kamran Shafi", title = "A System Dynamics Modeling Support System Based on Computational Intelligence", journal = "Systems", year = "2019", volume = "7", number = "4", keywords = "genetic algorithms, genetic programming", ISSN = "2079-8954", URL = "https://www.mdpi.com/2079-8954/7/4/47", DOI = "doi:10.3390/systems7040047", abstract = "System dynamics (SD) is a complex systems modelling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modelling stages, such as the generation of model structure, model calibration and policy optimisation. However, an integrated approach that supports across the board modelling process is still missing. In this paper, a prototype integrated modelling support system is presented for the purpose of supporting the modellers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale.", notes = "also known as \cite{systems7040047}", } @Article{Abdelmalek:2009:JAMDS, title = "Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming", author = "Wafa Abdelmalek and Sana {Ben Hamida} and Fathi Abid", journal = "Journal of Applied Mathematics and Decision Sciences", year = "2009", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming", URL = "http://downloads.hindawi.com/journals/ads/2009/179230.pdf", URL = "http://www.hindawi.com/journals/ads/2009/179230.html", DOI = "doi:10.1155/2009/179230", ISSN = "11739126", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:b3bc3b339d2f713819080ff9b253312a", abstract = "The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S\&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.", notes = "Article ID 179230 1RU: MODESFI, Faculty of Economics and Business, Road of the Airport Km 4, 3018 Sfax, Tunisia 2Laboratory of Intelligent IT Engineering, Higher School of Technology and Computer Science, 2035 Charguia, Tunisia", } @Article{Abdelmutalab:2016:PC, author = "Ameen Abdelmutalab and Khaled Assaleh and Mohamed El-Tarhuni", title = "Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers", journal = "Physical Communication", volume = "21", pages = "10--18", year = "2016", ISSN = "1874-4907", DOI = "doi:10.1016/j.phycom.2016.08.001", URL = "http://www.sciencedirect.com/science/article/pii/S1874490716301094", abstract = "In this paper, a Hierarchical Polynomial (HP) classifier is proposed to automatically classify M-PSK and M-QAM signals in Additive White Gaussian Noise (AWGN) and slow flat fading environments. The system uses higher order cumulants (HOCs) of the received signal to distinguish between the different modulation types. The proposed system divides the overall modulation classification problem into several hierarchical binary sub-classifications. In each binary sub-classification, the HOCs are expanded into a higher dimensional space in which the two classes are linearly separable. It is shown that there is a significant improvement when using the proposed Hierarchical polynomial structure compared to the conventional polynomial classifier. Moreover, simulation results are shown for different block lengths (number of received symbols) and at different SNR values. The proposed system showed an overall improvement in the probability of correct classification that reaches 100percent using only 512 received symbols at 20 dB compared to 98percent and 98.33percent when using more complicated systems like Genetic Programming with KNN classifier (GP-KNN) and Support Vector Machines (SVM) classifiers, respectively.", keywords = "genetic algorithms, genetic programming, Modulation classification, Hierarchical polynomial classifiers, High order cumulants, Adaptive modulation", } @InProceedings{Abdelwhab:2018:SICE, author = "Mohamed Abdelwhab and A. A. Abouelsoud and Ahmed M. R. Fath Elbab", booktitle = "2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)", title = "Tackling Dead End Scenarios by Improving Follow Gap Method with Genetic Programming", year = "2018", pages = "1566--1571", address = "Nara, Japan", abstract = "In this paper the problem of local minimum in obstacle avoidance is solved using improved follow gap method (FGM) through combination with genetic programming (GP). Two stages of controller are proposed and applied on Robotino mobile robot equipped with nine infra-red sensors. The first stage implements FGM when there is a gap between front obstacles whereas the second stage deals with the case of no front gap through the use of GP. Simulation and experimental work prove the effectiveness of the proposed method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.23919/SICE.2018.8492687", month = sep, notes = "Egypt-Japan University of Science and Technology. Also known as \cite{8492687}", } @PhdThesis{AbdGaus:thesis, author = "Yona Falinie {Abd Gaus}", title = "Artificial Intelligence System for Continuous Affect Estimation from Naturalistic Human Expressions", school = "Brunel University", year = "2018", address = "London, UK", month = jan, keywords = "genetic algorithms, genetic programming", URL = "http://bura.brunel.ac.uk/handle/2438/16348", URL = "https://bura.brunel.ac.uk/bitstream/2438/16348/1/FulltextThesis.pdf", size = "165 pages", abstract = "The analysis and automatic affect estimation system from human expression has been acknowledged as an active research topic in computer vision community. Most reported affect recognition systems, however, only consider subjects performing well-defined acted expression, in a very controlled condition, so they are not robust enough for real-life recognition tasks with subject variation, acoustic surrounding and illumination change. In this thesis, an artificial intelligence system is proposed to continuously (represented along a continuum e.g., from -1 to +1) estimate affect behaviour in terms of latent dimensions (e.g., arousal and valence) from naturalistic human expressions. To tackle the issues, feature representation and machine learning strategies are addressed. In feature representation, human expression is represented by modalities such as audio, video, physiological signal and text modality. Hand- crafted features is extracted from each modality per frame, in order to match with consecutive affect label. However, the features extracted maybe missing information due to several factors such as background noise or lighting condition. Haar Wavelet Transform is employed to determine if noise cancellation mechanism in feature space should be considered in the design of affect estimation system. Other than hand-crafted features, deep learning features are also analysed in terms of the layer-wise; convolutional and fully connected layer. Convolutional Neural Network such as AlexNet, VGGFace and ResNet has been selected as deep learning architecture to do feature extraction on top of facial expression images. Then, multimodal fusion scheme is applied by fusing deep learning feature and hand-crafted feature together to improve the performance. In machine learning strategies, two-stage regression approach is introduced. In the first stage, baseline regression methods such as Support Vector Regression are applied to estimate each affect per time. Then in the second stage, subsequent model such as Time Delay Neural Network, Long Short-Term Memory and Kalman Filter is proposed to model the temporal relationships between consecutive estimation of each affect. In doing so, the temporal information employed by a subsequent model is not biased by high variability present in consecutive frame and at the same time, it allows the network to exploit the slow changing dynamic between emotional dynamic more efficiently. Following of two-stage regression approach for unimodal affect analysis, fusion information from different modalities is elaborated. Continuous emotion recognition in-the-wild is leveraged by investigating mathematical modelling for each emotion dimension. Linear Regression, Exponent Weighted Decision Fusion and Multi-Gene Genetic Programming are implemented to quantify the relationship between each modality. In summary, the research work presented in this thesis reveals a fundamental approach to automatically estimate affect value continuously from naturalistic human expression. The proposed system, which consists of feature smoothing, deep learning feature, two-stage regression framework and fusion using mathematical equation between modalities is demonstrated. It offers strong basis towards the development artificial intelligent system on estimation continuous affect estimation, and more broadly towards building a real-time emotion recognition system for human-computer interaction.", notes = "Supervisor: Hongying Meng", } @InProceedings{AbdGaus:2018:ieeeFG, author = "Yona Falinie A. Gaus and Hongying Meng", booktitle = "2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018)", title = "Linear and Non-Linear Multimodal Fusion for Continuous Affect Estimation In-the-Wild", year = "2018", pages = "492--498", abstract = "Automatic continuous affect recognition from multiple modality in the wild is arguably one of the most challenging research areas in affective computing. In addressing this regression problem, the advantages of the each modality, such as audio, video and text, have been frequently explored but in an isolated way. Little attention has been paid so far to quantify the relationship within these modalities. Motivated to leverage the individual advantages of each modality, this study investigates behavioural modelling of continuous affect estimation, in multimodal fusion approaches, using Linear Regression, Exponent Weighted Decision Fusion and Multi-Gene Genetic Programming. The capabilities of each fusion approach are illustrated by applying it to the formulation of affect estimation generated from multiple modality using classical Support Vector Regression. The proposed fusion methods were applied in the public Sentiment Analysis in the Wild (SEWA) multi-modal dataset and the experimental results indicate that employing proper fusion can deliver a significant performance improvement for all affect estimation. The results further show that the proposed systems is competitive or outperform the other state-of-the-art approaches.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FG.2018.00079", month = may, notes = "Also known as \cite{8373872}", } @InProceedings{4983280, author = "I. Abd Latiff and M. O. Tokhi", title = "Fast convergence strategy for Particle Swarm Optimization using spread factor", booktitle = "Evolutionary Computation, 2009. CEC '09. IEEE Congress on", year = "2009", month = may, pages = "2693--2700", keywords = "PSO velocity equation, fast convergence strategy, inertia weight, particle swarm optimization, spread factor, convergence, particle swarm optimisation", DOI = "doi:10.1109/CEC.2009.4983280", notes = "Not on GP", } @Article{journals/nca/AbdolahzareM18, title = "Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing", author = "Zahra Abdolahzare and Saman Abdanan Mehdizadeh", journal = "Neural Computing and Applications", year = "2018", number = "2", volume = "29", pages = "363--375", keywords = "genetic algorithms, genetic programming", bibdate = "2018-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca29.html#AbdolahzareM18", DOI = "doi:10.1007/s00521-016-2450-1", } @Article{Abdollahzadeh:2016:CC, author = "Gholamreza Abdollahzadeh and Ehsan Jahani and Zahra Kashir", title = "Predicting of compressive strength of recycled aggregate concrete by genetic programming", journal = "Computers and Concrete", year = "2016", volume = "18", number = "2", pages = "155--163", month = aug, keywords = "genetic algorithms, genetic programming, gene expression programming, recycled aggregate concrete, silica fume, compressive strength", ISSN = "1598-8198", DOI = "doi:10.12989/CAC.2016.18.2.155", size = "20 pages", abstract = "This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume.", notes = "admin@techno-press.com Department of Civil Engineering, Babol University of Technology, Babol, Iran", } @Article{Abdou200911402, author = "Hussein A. Abdou", title = "Genetic programming for credit scoring: The case of Egyptian public sector banks", journal = "Expert Systems with Applications", volume = "36", number = "9", pages = "11402--11417", year = "2009", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2009.01.076", URL = "http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec", URL = "http://results.ref.ac.uk/Submissions/Output/2691591", keywords = "genetic algorithms, genetic programming, Credit scoring, Weight of evidence, Egyptian public sector banks", abstract = "Credit scoring has been widely investigated in the area of finance, in general, and banking sectors, in particular. Recently, genetic programming (GP) has attracted attention in both academic and empirical fields, especially for credit problems. The primary aim of this paper is to investigate the ability of GP, which was proposed as an extension of genetic algorithms and was inspired by the Darwinian evolution theory, in the analysis of credit scoring models in Egyptian public sector banks. The secondary aim is to compare GP with probit analysis (PA), a successful alternative to logistic regression, and weight of evidence (WOE) measure, the later a neglected technique in published research. Two evaluation criteria are used in this paper, namely, average correct classification (ACC) rate criterion and estimated misclassification cost (EMC) criterion with different misclassification cost (MC) ratios, in order to evaluate the capabilities of the credit scoring models. Results so far revealed that GP has the highest ACC rate and the lowest EMC. However, surprisingly, there is a clear rule for the WOE measure under EMC with higher MC ratios. In addition, an analysis of the dataset using Kohonen maps is undertaken to provide additional visual insights into cluster groupings.", uk_research_excellence_2014 = "D - Journal article", } @PhdThesis{2009AbdouEthosPhD, author = "Hussein Ali Hussein Abdou", title = "Credit Scoring Models for Egyptian Banks: Neural Nets and Genetic Programming versus Conventional Techniques", school = "Plymouth Business School, University of Plymouth", year = "2009", address = "UK", month = apr, keywords = "genetic algorithms, genetic programming", URL = "https://pearl.plymouth.ac.uk/bitstream/handle/10026.1/379/2009AbdouEthosPhD.pdf", URL = "http://hdl.handle.net/10026.1/379", URL = "http://ethos.bl.uk/OrderDetails.do?did=55&uin=uk.bl.ethos.494192", size = "452 pages", abstract = "Credit scoring has been regarded as a core appraisal tool of banks during the last few decades, and has been widely investigated in the area of finance, in general, and banking sectors, in particular. In this thesis, the main aims and objectives are: to identify the currently used techniques in the Egyptian banking credit evaluation process; and to build credit scoring models to evaluate personal bank loans. In addition, the subsidiary aims are to evaluate the impact of sample proportion selection on the Predictive capability of both advanced scoring techniques and conventional scoring techniques, for both public banks and a private banking case-study; and to determine the key characteristics that affect the personal loans' quality (default risk). The stages of the research comprised: firstly, an investigative phase, including an early pilot study, structured interviews and a questionnaire; and secondly, an evaluative phase, including an analysis of two different data-sets from the Egyptian private and public banks applying average correct classification rates and estimated misclassification costs as criteria. Both advanced scoring techniques, namely, neural nets (probabilistic neural nets and multi-layer feed-forward nets) and genetic programming, and conventional techniques, namely, a weight of evidence measure, multiple discriminant analysis, probit analysis and logistic regression were used to evaluate credit default risk in Egyptian banks. In addition, an analysis of the data-sets using Kohonen maps was undertaken to provide additional visual insights into cluster groupings. From the investigative stage, it was found that all public and the vast majority of private banks in Egypt are using judgemental approaches in their credit evaluation. From the evaluative stage, clear distinctions between the conventional techniques and the advanced techniques were found for the private banking case-study; and the advanced scoring techniques (such as powerful neural nets and genetic programming) were superior to the conventional techniques for the public sector banks. Concurrent loans from other banks and guarantees by the corporate employer of the loan applicant, which have not been used in other reported studies, are identified as key variables and recommended in the specific environment chosen, namely Egypt. Other variables, such as a feasibility study and the Central Bank of Egypt report also play a contributory role in affecting the loan quality.", notes = "Supervisor John Pointon uk.bl.ethos.494192", } @InProceedings{Abdulhamid:2011:ICARA, author = "Fahmi Abdulhamid and Kourosh Neshatian and Mengjie Zhang", title = "Genetic programming for evolving programs with loop structures for classification tasks", booktitle = "5th International Conference on Automation, Robotics and Applications (ICARA 2011)", year = "2011", month = "6-8 " # dec, pages = "202--207", address = "Wellington, New Zealand", size = "6 pages", abstract = "Object recognition and classification are important tasks in robotics. Genetic Programming (GP) is a powerful technique that has been successfully used to automatically generate (evolve) classifiers. The effectiveness of GP is limited by the expressiveness of the functions used to evolve programs. It is believed that loop structures can considerably improve the quality of GP programs in terms of both performance and interpretability. This paper proposes five new loop structures using which GP can evolve compact programs that can perform sophisticated processing. The use of loop structures in GP is evaluated against GP with no loops for both image and non-image classification tasks. Evolved programs using the proposed loop structures are analysed in several problems. The results show that loop structures can increase classification accuracy compared to GP with no loops.", keywords = "genetic algorithms, genetic programming, evolving program, image classification task, nonimage classification task, object classification task, object recognition task, program loop structure, robotics, image classification, learning (artificial intelligence), object recognition, robot vision", DOI = "doi:10.1109/ICARA.2011.6144882", notes = "Also known as \cite{6144882}", } @InProceedings{Abdulhamid:2012:CEC, title = "Evolving Genetic Programming Classifiers with Loop Structures", author = "Fahmi Abdulhamid and Andy Song and Kourosh Neshatian and Mengjie Zhang", pages = "2710--2717", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6252877", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Classification, clustering, data analysis and data mining", abstract = "Loop structure is a fundamental flow control in programming languages for repeating certain operations. It is not widely used in Genetic Programming as it introduces extra complexity in the search. However in some circumstances, including a loop structure may enable GP to find better solutions. This study investigates the benefits of loop structures in evolving GP classifiers. Three different loop representations are proposed and compared with other GP methods and a set of traditional classification methods. The results suggest that the proposed loop structures can outperform other methods. Additionally the evolved classifiers can be small and simple to interpret. Further analysis on a few classifiers shows that they indeed have captured genuine characteristics from the data for performing classification.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{abdulkarimova:2019:ajhpc, author = "Ulviya Abdulkarimova and Anna {Ouskova Leonteva} and Christian Rolando and Anne Jeannin-Girardon and Pierre Collet", title = "The {PARSEC} machine: a non-{Newtonian} supra-linear super-computer", journal = "Azerbaijan Journal of High Performance Computing", year = "2019", volume = "2", number = "2", pages = "122--140", month = dec, keywords = "genetic algorithms, genetic programming, beowulf cluster, HPC, parallel computing, relative space-time, supra-linear acceleration, qualitative acceleration, GPGPU, loosely coupled machines, artificial evolution, transfer learning, harmonic analysis, super-resolution, non-uniform sampling, Fourier transform", URL = "https://publis.icube.unistra.fr/docs/14472/easeaHPC.pdf", URL = "https://azjhpc.org/index.php/archives/15-paper/52-the-parsec-machine-a-non-newtonian-supra-linear-supercomputer", URL = "http://azjhpc.com//issue4/doi.org:10.32010:26166127.2019.2.2.122.140.pdf", DOI = "doi:10.32010/26166127.2019.2.2.122.140", size = "19 pages", abstract = "transfer-learning can turn a Beowulf cluster into a full super-computer with supra-linear qualitative acceleration. Harmonic Analysis is used as a real-world example to show the kind of result that can be achieved with the proposed super-computer architecture, that locally exploits absolute space-time parallelism on each machine (SIMD parallelism) and loosely-coupled relative space-time parallelisation between different machines (loosely coupled MIMD)", } @PhdThesis{abdulkarimova:tel-03700035, author = "Ulviya Abdulkarimova", title = "{SINUS-IT}: an evolutionary approach to harmonic analysis", title_fr = "SINUS-IT : une approche evolutionnaire de l'analyse harmonique", school = "Universite de Strasbourg", year = "2021", address = "France", month = "2 " # sep, keywords = "genetic algorithms, genetic programming, EASEA, NVIDA, CUDA, Artificial evolution, Evolution strategies, QAES, Fourier transform, FFT, Harmonic analysis, FT-ICR, Isotopic structure, GPU, GPGPU parallelisation, Island-based parallelization, Glutathione, binary radians, Brad2rad, Rad2brad, global random sampling, GRS", number = "2021STRAD018", hal_id = "tel-03700035", hal_version = "v1", URL = "https://theses.hal.science/tel-03700035/", URL = "https://theses.hal.science/tel-03700035/document", URL = "https://theses.hal.science/tel-03700035/file/ABDULKARIMOVA_Ulviya_2021_ED269.pdf", size = "185 pages", abstract = "This PhD project is about harmonic analysis of signals coming from Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometer. The analysis of these signals is usually done using Fourier Transform (FT) method. However, there are several limitations of this method, one of which is not being able to find the phase parameter. Mass spectrometers are used to determine the chemical composition of compounds. It is known that if the phase component is known, it would yield an improvement in mass accuracy and mass resolving power which would help to determine the composition of a given compound more accurately. In this PhD work we use evolutionary algorithm to overcome the limitations of the FT method. We explore different sampling, speed optimization and algorithm improvement methods. We show that our proposed method outperforms the FT method as it uses short transients to resolve the peaks and it automatically yields phase values.", notes = "Some mention of GP. In English. https://icube.unistra.fr/actualites-agenda/agenda/evenement/?tx_ttnews%5Btt_news%5D=23221&cHash=58d32af8fd94c8ff397921898600e7cd MSAP Page 149--159 Appendix A, Resume en francais de la these Thesis supervisors: Pierre Collet and Christian Rolando", } @Article{Abdulkarimova:2025:GPEM, author = "Ulviya Abdulkarimova and Rodrigo Abarca-del-Rio and Pierre Collet", title = "Harnessing evolutionary algorithms for enhanced characterization of {ENSO} events", journal = "Genetic Programming and Evolvable Machines", year = "2025", volume = "26", pages = "Article no 4", note = "Online first", keywords = "genetic algorithms, genetic programming, El Nino Southern Oscillation, ENSO, Evolutionary algorithm, Symbolic regression, Stochastic optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09497-z", notes = "Azerbaijan State Oil and Industry University (ASOIU)/French-Azerbaijani University (UFAZ), Baku, Azerbaijan", } @PhdThesis{Abdullah:thesis, author = "Norliza Binti Abdullah", title = "Android Malware Detection System using Genetic Programming", school = "Computer Science, University of York", year = "2019", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming, Supervised Learning, Multi-objective Genetic Algorithm, SPEA2, MOGP, Android Malware", URL = "https://etheses.whiterose.ac.uk/29027/", URL = "https://etheses.whiterose.ac.uk/29027/6/Abdullah_201051902.pdf", size = "165 pages", abstract = "Nowadays, smartphones and other mobile devices are playing a significant role in the way people engage in entertainment, communicate, network, work, and bank and shop online. As the number of mobile phones sold has increased dramatically worldwide, so have the security risks faced by the users, to a degree most do not realise. One of the risks is the threat from mobile malware. In this research, we investigate how supervised learning with evolutionary computation can be used to synthesise a system to detect Android mobile phone attacks. The attacks include malware, ransomware and mobile botnets. The datasets used in this research are publicly downloadable, available for use with appropriate acknowledgement. The primary source is Drebin. We also used ransomware and mobile botnet datasets from other Android mobile phone researchers. The research in this thesis uses Genetic Programming (GP) to evolve programs to distinguish malicious and non-malicious applications in Android mobile datasets. It also demonstrates the use of GP and Multi-Objective Evolutionary Algorithms (MOEAs) together to explore functional (detection rate) and non-functional (execution time and power consumption) trade-offs. Our results show that malicious and non-malicious applications can be distinguished effectively using only the permissions held by applications recorded in the application's Android Package (APK). Such a minimalist source of features can serve as the basis for highly efficient Android malware detection. Non-functional tradeoffs are also highlight.", notes = "Also known as \cite{wreo29027} uk.bl.ethos.832567", } @InProceedings{Abdul-Rahim:2006:ccis, author = "A. B. {Abdul rahim} and J. Teo and A. Saudi", title = "An Empirical Comparison of Code Size Limit in Auto-Constructive Artificial Life", booktitle = "2006 IEEE Conference on Cybernetics and Intelligent Systems", year = "2006", pages = "1--6", address = "Bangkok", month = jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Push, Breve, ALife, PushGP", ISBN = "1-4244-0023-6", DOI = "doi:10.1109/ICCIS.2006.252308", abstract = "This paper presents an evolving swarm system of flying agents simulated as a collective intelligence within the Breve auto-constructive artificial life environment. The behaviour of each agent is governed by genetically evolved program codes expressed in the Push programming language. There are two objectives in this paper, that is to investigate the effects of firstly code size limit and secondly two different versions of the Push genetic programming language on the auto-constructive evolution of artificial life. We investigated these genetic programming code elements on reproductive competence using a measure based on the self-sustainability of the population. Self-sustainability is the point in time when the current population's agents are able to reproduce enough offspring to maintain the minimum population size without any new agents being randomly injected from the system. From the results, we found that the Push2 implementation showed slightly better evolvability than Push3 in terms of achieving self-sufficiency. In terms of code size limit, the reproductive competence of the collective swarm was affected quite significantly at certain parameter settings", notes = "Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah", } @Article{Abdulrahman:2020:IJCA, author = "Hadeel Abdulrahman and Mohamed Khatib", title = "Classification of Retina Diseases from {OCT} using Genetic Programming", journal = "International Journal of Computer Applications", year = "2020", volume = "177", number = "45", pages = "41--46", month = mar, keywords = "genetic algorithms, genetic programming, feature extraction, Optical Coherence Tomography, OCT image classification, OCT feature extraction", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume177/number45/abdulrahman-2020-ijca-919973.pdf", URL = "http://www.ijcaonline.org/archives/volume177/number45/31212-2020919973", DOI = "doi:10.5120/ijca2020919973", size = "6 pages", abstract = "a fully automated method for feature extraction and classification of retina diseases is implemented. The main idea is to find a method that can extract the important features from the Optical Coherence Tomography (OCT) image, and acquire a higher classification accuracy. The using of genetic programming (GP) can achieve that aim. Genetic programming is a good way to choose the best combination of feature extraction methods from a set of feature extraction methods and determine the proper parameters for each one of the selected extraction methods. 800 OCT images are used in the proposed method, of the most three popular retinal diseases: Choroidal neovascularization (CNV), Diabetic Macular Edema (DME) and Drusen, beside the normal OCT images. While the set of the feature extraction methods that is used in this paper contains: Gabor filter, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), histogram of the image, and Speed Up Robust Filter (SURF). These methods are used for the both of global and local feature extraction. After that the classification process is achieved by the Support Vector Machine (SVM). The proposed method performed high accuracy as compared with the traditional methods.", notes = "Also known as \cite{10.5120/ijca2020919973,} www.ijcaonline.org Department of Artificial Intelligence, Faculty of Informatics Engineering, Aleppo University, Syria", } @InProceedings{Abednego:2011:ICEEI, author = "Luciana Abednego and Dwi Hendratmo", title = "Genetic programming hyper-heuristic for solving dynamic production scheduling problem", booktitle = "International Conference on Electrical Engineering and Informatics (ICEEI 2011)", year = "2011", month = "17-19 " # jul, pages = "K3--2", address = "Bandung, Indonesia", size = "4 pages", abstract = "This paper investigates the potential use of genetic programming hyper-heuristics for solution of the real single machine production problem. This approach operates on a search space of heuristics rather than directly on a search space of solutions. Genetic programming hyper-heuristics generate new heuristics from a set of potential heuristic components. Real data from production department of a metal industries are used in the experiments. Experimental results show genetic programming hyper-heuristics outperforms other heuristics including MRT, SPT, LPT, EDD, LDD, dan MON rules with respect to minimum tardiness and minimum flow time objectives. Further results on sensitivity to changes indicate that GPHH designs are robust. Based on experiments, GPHH outperforms six other benchmark heuristics with number of generations 50 and number of populations 50. Human designed heuristics are result of years of work by a number of experts, while GPHH automate the design of the heuristics. As the search process is automated, this would largely reduce the cost of having to create a new set of heuristics.", keywords = "genetic algorithms, genetic programming, cost reduction, dynamic production scheduling problem, genetic programming hyper heuristics, metal industries, minimum flow time, minimum tardiness, single machine production problem, cost reduction, dynamic scheduling, heuristic programming, lead time reduction, metallurgical industries, single machine scheduling", DOI = "doi:10.1109/ICEEI.2011.6021768", ISSN = "2155-6822", notes = "Also known as \cite{6021768}", } @InCollection{abernathy:2000:UGASBCRB, author = "Neil Abernathy", title = "Using a Genetic Algorithm to Select Beam Configurations for Radiosurgery of the Brain", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "1--7", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{Abhishek:2014:PMS, author = "Kumar Abhishek and Biranchi Narayan Panda and Saurav Datta and Siba Sankar Mahapatra", title = "Comparing Predictability of Genetic Programming and {ANFIS} on Drilling Performance Modeling for {GFRP} Composites", journal = "Procedia Materials Science", volume = "6", pages = "544--550", year = "2014", note = "3rd International Conference on Materials Processing and Characterisation (ICMPC 2014)", ISSN = "2211-8128", DOI = "doi:10.1016/j.mspro.2014.07.069", URL = "http://www.sciencedirect.com/science/article/pii/S2211812814004349", abstract = "Drilling of glass fibre reinforced polymer (GFRP) composite material is substantially complicated from the metallic materials due to its high structural stiffness (of the composite) and low thermal conductivity of plastics. During drilling of GFRP composites, problems generally arise like fibre pull out, delamination, stress concentration, swelling, burr, splintering and micro cracking etc. which reduces overall machining performance. Now-a-days hybrid approaches have been received remarkable attention in order to model machining process behaviour and to optimise machining performance towards subsequent improvement of both quality and productivity, simultaneously. In the present research, spindle speed, feed rate, plate thickness and drill bit diameter have been considered as input parameters; and the machining yield characteristics have been considered in terms of thrust and surface roughness (output responses) of the drilled composite product. The study illustrates the applicability of genetic programming with the help of GPTIPS as well as Adaptive Neuro Fuzzy Inference System (ANFIS) towards generating prediction models for better understanding of the process behavior and for improving process performances in drilling of GFRP composites.", keywords = "genetic algorithms, genetic programming, Glass fibre reinforced polymer (GFRP), Adaptive Neuro Fuzzy Inference System (ANFIS), GPTIPS.", notes = "PhD thesis (2015) http://ethesis.nitrkl.ac.in/6916/ Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization. PhD thesis. does not seem to be on GP", } @InCollection{Abid:2012:GPnew, author = "Fathi Abid and Wafa Abdelmalek and Sana {Ben Hamida}", title = "Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "7", pages = "141--172", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48148", size = "32 pages", notes = "Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{ABOELELA:2022:RE, author = "Abdelrahman E. Aboelela and Ahmed M. Ebid and Ayman L. Fayed", title = "Estimating the subgrade reaction at deep braced excavation bed in dry granular soil using genetic programming ({GP)}", journal = "Results in Engineering", volume = "13", pages = "100328", year = "2022", ISSN = "2590-1230", DOI = "doi:10.1016/j.rineng.2021.100328", URL = "https://www.sciencedirect.com/science/article/pii/S2590123021001298", keywords = "genetic algorithms, genetic programming, Deep braced excavation, Modulus of subgrade reaction", abstract = "Modulus of subgrade reaction (Ks) is a simplified and approximated approach to present the soil-structure interaction. It is widely used in designing combined and raft foundations due to its simplicity. (Ks) is not a soil propriety, its value depends on many factors including soil properties, shape, dimensions and stiffness of footing and even time (for saturated cohesive soils). Many earlier formulas were developed to estimate the (Ks) value. This research is concerned in studying the effect of de-stressing and shoring rigidity of deep excavation on the (Ks) value. A parametric study was carried out using 27 FEM models with different configurations to generate a database, then a well-known {"}Genetic Programming{"} technique was applied on the database to develop a formula to correlate the (Ks) value with the deep excavation configurations. The results indicated that (Ks) value increased with increasing the diaphragm wall stiffness and decreases with increasing the excavation depth", } @Article{Abooali:2014:JNGSE, author = "Danial Abooali and Ehsan Khamehchi", title = "Estimation of dynamic viscosity of natural gas based on genetic programming methodology", journal = "Journal of Natural Gas Science and Engineering", volume = "21", pages = "1025--1031", year = "2014", keywords = "genetic algorithms, genetic programming, Natural gas, Dynamic viscosity, Correlation", ISSN = "1875-5100", DOI = "doi:10.1016/j.jngse.2014.11.006", URL = "http://www.sciencedirect.com/science/article/pii/S1875510014003394", abstract = "Investigating the behaviour of natural gas can contribute to a detailed understanding of hydrocarbon reservoirs. Natural gas, alone or in association with oil in reservoirs, has a large impact on reservoir fluid properties. Thus, having knowledge about gas characteristics seems to be necessary for use in estimation and prediction purposes. In this project, dynamic viscosity of natural gas (mu_g), as an important quantity, was correlated with pseudo-reduced temperature (Tpr), pseudo-reduced pressure (Ppr), apparent molecular weight (Ma) and gas density (rhog) by operation of the genetic programming method on a large dataset including 1938 samples. The squared correlation coefficient (R2), average absolute relative deviation percent (AARDpercent) and average absolute error (AAE) are 0.999, 2.55percent and 0.00084 cp, respectively. The final results show that the obtained simple-to-use model can predict viscosity of natural gases with high accuracy and confidence.", notes = "GPTIPS", } @Article{ABOOALI:2019:JPSE, author = "Danial Abooali and Mohammad Amin Sobati and Shahrokh Shahhosseini and Mehdi Assareh", title = "A new empirical model for estimation of crude oil/brine interfacial tension using genetic programming approach", journal = "Journal of Petroleum Science and Engineering", volume = "173", pages = "187--196", year = "2019", keywords = "genetic algorithms, genetic programming, Interfacial tension, Correlation, Crude oil, Brine, Genetic programming (GP)", ISSN = "0920-4105", DOI = "doi:10.1016/j.petrol.2018.09.073", URL = "http://www.sciencedirect.com/science/article/pii/S0920410518308283", abstract = "Detailed understanding of the behavior of crude oils and their interactions with reservoir formations and other in-situ fluids can help the engineers to make better decisions about the future of oil reservoirs. As an important property, interfacial tension (IFT) between crude oil and brine has great impacts on the oil production efficiency in different recovery stages due to its effects on the capillary number and residual oil saturation. In the present work, a new mathematical model has been developed to estimate IFT between crude oil and brine on the basis of a number of physical properties of crude oil (i.e., specific gravity, and total acid number) and the brine (i.e., pH, NaCl equivalent salinity), temperature, and pressure. Genetic programming (GP) methodology has been implemented on a data set including 560 experimental data to develop the IFT correlation. The correlation coefficient (R2a =a 0.9745), root mean square deviation (RMSDa =a 1.8606a mN/m), and average absolute relative deviation (AARDa =a 3.3932percent) confirm the acceptable accuracy of the developed correlation for the prediction of IFT", } @Article{ABOOALI:2020:Fuel, author = "Danial Abooali and Reza Soleimani and Saeed Gholamreza-Ravi", title = "Characterization of physico-chemical properties of biodiesel components using smart data mining approaches", journal = "Fuel", volume = "266", pages = "117075", year = "2020", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2020.117075", URL = "http://www.sciencedirect.com/science/article/pii/S0016236120300703", keywords = "genetic algorithms, genetic programming, Fatty acid ester, Density, Speed of sound, Isentropic and isothermal compressibility, Stochastic gradient boosting", abstract = "Biodiesels are the most probable future alternatives for petroleum fuels due to their easy accessibility and extraction, comfortable transportation and storage and lower environmental pollutions. Biodiesels have wide range of molecular structures including various long chain fatty acid methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs) with different thermos-physical properties. Therefore, reliable methods estimating the ester properties seems necessary to choose the appropriate one for a special diesel engine. In the present study, the effort was developing a set of novel and robust methods for estimation of four important properties of common long chain fatty acid methyl and ethyl esters including density, speed of sound, isentropic and isothermal compressibility, directly from a number of basic effective variables (i.e. temperature, pressure, molecular weight and normal melting point). Stochastic gradient boosting (SGB) and genetic programming (GP) as innovative and powerful mathematical approaches in this area were applied and implemented on large datasets including 2117, 1048, 483 and 310 samples for density, speed of sound, isentropic and isothermal compressibility, respectively. Statistical assessments revealed high applicability and accuracy of the new developed models (R2 > 0.99 and AARD < 1.7percent) and the SGB models yield more accurate and confident predictions", } @Article{DBLP:journals/nca/AbooaliK19, author = "Danial Abooali and Ehsan Khamehchi", title = "New predictive method for estimation of natural gas hydrate formation temperature using genetic programming", journal = "Neural Comput. Appl.", volume = "31", number = "7", pages = "2485--2494", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s00521-017-3208-0", DOI = "doi:10.1007/s00521-017-3208-0", timestamp = "Thu, 10 Sep 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/nca/AbooaliK19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{abraham:2003:CEC, author = "Ajith Abraham and Vitorino Ramos", title = "Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1384--1391", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Web Usage Mining, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming, Adaptive control, Ant colony optimization, Artificial intelligence, Communication system traffic control, Decision support systems, Knowledge management, Marketing management, Programmable control, Traffic control, Internet, artificial life, data mining, decision support systems, electronic commerce, self-organising feature maps, statistical analysis, Web site management, Web usage mining, artificial ant colony clustering algorithm, decision support systems, distributed adaptive organisation, distributed control problems, e-commerce, intelligent marketing strategies, knowledge discovery, knowledge retrieval, network traffic flow analysis, self-organizing map", ISBN = "0-7803-7804-0", URL = "http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf", URL = "http://arxiv.org/abs/cs/0412071", DOI = "doi:10.1109/CEC.2003.1299832", size = "8 pages", abstract = "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @TechReport{abraham:2004:0405046, author = "Ajith Abraham and Ravi Jain", title = "Soft Computing Models for Network Intrusion Detection Systems", institution = "OSU", year = "2004", month = "13 " # may # " 2004", note = "Journal-ref: Soft Computing in Knowledge Discovery: Methods and Applications, Saman Halgamuge and Lipo Wang (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, Chapter 16, 20 pages, 2004", keywords = "genetic algorithms, genetic programming, Cryptography and Security", URL = "http://www.softcomputing.net/saman2.pdf", URL = "http://arxiv.org/abs/cs/0405046", abstract = "Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: external intruders, who are unauthorised users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This chapter presents a soft computing approach to detect intrusions in a network. Among the several soft computing paradigms, we investigated fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming and an ensemble method to model fast and efficient intrusion detection systems. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.", notes = "ACM-class: K.6.5 cs.CR/0405046", size = "20 pages", } @Article{Abraham:2003:JIKM, author = "Ajith Abraham", title = "Business Intelligence from Web Usage Mining", journal = "Journal of Information \& Knowledge Management", year = "2003", volume = "2", number = "4", pages = "375--390", keywords = "genetic algorithms, genetic programming, Web mining, knowledge discovery, business intelligence, hybrid soft computing, neuro-fuzzy-genetic system", URL = "http://www.softcomputing.net/jikm.pdf", DOI = "doi:10.1142/S0219649203000565", size = "16 pages", abstract = "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. This paper presents the important concepts of Web usage mining and its various practical applications. Further a novel approach called {"}intelligent-miner{"} (i-Miner) is presented. i-Miner could optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi?Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.", notes = "see also \cite{oai:arXiv.org:cs/0405030} http://www.worldscinet.com/jikm/jikm.shtml http://ajith.softcomputing.net Department of Computer Science, Oklahoma State University, 700 N Greenwood Avenue, Tulsa, Oklahoma 74106-0700, USA", } @Misc{oai:arXiv.org:cs/0405030, title = "Business Intelligence from Web Usage Mining", author = "Ajith Abraham", year = "2004", month = may # "~06", keywords = "genetic algorithms, genetic programming", abstract = "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.", identifier = "Journal of Information \& Knowledge Management (JIKM), World Scientific Publishing Co., Singapore, Vol. 2, No. 4, pp. 375-390, 2003", oai = "oai:arXiv.org:cs/0405030", URL = "http://arXiv.org/abs/cs/0405030", notes = "see also \cite{Abraham:2003:JIKM}", } @InCollection{abraham:2004:ECDM, author = "Ajith Abraham", title = "Evolutionary Computation in Intelligent Network Management", booktitle = "Evolutionary Computing in Data Mining", publisher = "Springer", year = "2004", editor = "Ashish Ghosh and Lakhmi C. Jain", volume = "163", series = "Studies in Fuzziness and Soft Computing", chapter = "9", pages = "189--210", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming, LGP, intrusion detection, ANN, www, fuzzy clustering, fuzzy inference, computer security, RIPPER, demes (ring topology), steady state 32-bit FPU machine code GP, SVM, decision trees, i-miner", ISBN = "3-540-22370-3", URL = "http://www.softcomputing.net/ec_web-chapter.pdf", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html", abstract = "Data mining is an iterative and interactive process concerned with discovering patterns, associations and periodicity in real world data. This chapter presents two real world applications where evolutionary computation has been used to solve network management problems. First, we investigate the suitability of linear genetic programming (LGP) technique to model fast and efficient intrusion detection systems, while comparing its performance with artificial neural networks and classification and regression trees. Second, we use evolutionary algorithms for a Web usage-mining problem. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Evolutionary algorithm is used to optimise the concurrent architecture of a fuzzy clustering algorithm (to discover data clusters) and a fuzzy inference system to analyse the trends. Empirical results clearly shows that evolutionary algorithm could play a major rule for the problems considered and hence an important data mining tool.", size = "22 pages", } @InCollection{intro:2006:GSP, author = "Ajith Abraham and Nadia Nedjah and Luiza {de Macedo Mourelle}", title = "Evolutionary Computation: from Genetic Algorithms to Genetic Programming", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "1--20", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISBN = "3-540-29849-5", URL = "http://www.softcomputing.net/gpsystems.pdf", DOI = "doi:10.1007/3-540-32498-4_1", abstract = "Evolutionary computation, offers practical advantages to the researcher facing difficult optimisation problems. These advantages are multi-fold, including the simplicity of the approach, its robust response to changing circumstance, its flexibility, and many other facets. The evolutionary approach can be applied to problems where heuristic solutions are not available or generally lead to unsatisfactory results. As a result, evolutionary computation have received increased interest, particularly with regards to the manner in which they may be applied for practical problem solving. we review the development of the field of evolutionary computations from standard genetic algorithms to genetic programming, passing by evolution strategies and evolutionary programming. For each of these orientations, we identify the main differences from the others. We also, describe the most popular variants of genetic programming. These include linear genetic programming (LGP), gene expression programming (GEP), multi-expression programming (MEP), Cartesian genetic programming (CGP), traceless genetic programming (TGP), gramatical evolution (GE) and genetic algorithm for deriving software (GADS).", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", size = "21 pages", } @InCollection{abraham:2006:GSP, author = "Ajith Abraham and Crina Grosan", title = "Evolving Intrusion Detection Systems", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "57--79", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", email = "ajith.abraham@ieee.org", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-29849-5", URL = "http://falklands.globat.com/~softcomputing.net/ids-chapter.pdf", DOI = "doi:10.1007/3-540-32498-4_3", abstract = "An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. We evaluate the performances of two Genetic Programming techniques for IDS namely Linear Genetic Programming (LGP) and Multi-Expression Programming (MEP). Results are then compared with some machine learning techniques like Support Vector Machines (SVM) and Decision Trees (DT). Empirical results clearly show that GP techniques could play an important role in designing real time intrusion detection systems.", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", } @InProceedings{abraham:2005:CEC, author = "Ajith Abraham and Crina Grosan", title = "Genetic Programming Approach for Fault Modeling of Electronic Hardware", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1563--1569", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, MEP, ANN, LGP", ISBN = "0-7803-9363-5", URL = "http://www.softcomputing.net/cec05.pdf", DOI = "doi:10.1109/CEC.2005.1554875", size = "7 pages", abstract = "presents two variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modelling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and standardisation are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @Article{journals/jikm/AbrahamG06, author = "Ajith Abraham and Crina Grosan", title = "Decision Support Systems Using Ensemble Genetic Programming", journal = "Journal of Information \& Knowledge Management (JIKM)", year = "2006", volume = "5", number = "4", pages = "303--313", month = dec, note = "Special topic: Knowledge Discovery Using Advanced Computational Intelligence Tools", keywords = "genetic algorithms, genetic programming, gene expression programming, Decision support systems, ensemble systems, evolutionary multi-objective optimisation", ISSN = "0219-6492", DOI = "doi:10.1142/S0219649206001566", abstract = "This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and that the method is efficient.", bibdate = "2008-06-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jikm/jikm5.html#AbrahamG06", } @Article{Abraham:2007:JNCS, author = "Ajith Abraham and Ravi Jain and Johnson Thomas and Sang Yong Han", title = "D-SCIDS: Distributed soft computing intrusion detection system", journal = "Journal of Network and Computer Applications", year = "2007", volume = "30", number = "1", pages = "81--98", month = jan, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.jnca.2005.06.001", abstract = "An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees, support vector machines and linear genetic programming. Further, we modelled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.", } @InProceedings{Abraham:2008:ieeeISI, author = "Ajith Abraham", title = "Real time intrusion prediction, detection and prevention programs", booktitle = "IEEE International Conference on Intelligence and Security Informatics, ISI 2008", year = "2008", month = jun, pages = "xli--xlii", note = "IEEE ISI 2008 Invited Talk (VI)", keywords = "genetic algorithms, genetic programming, distributed intrusion detection systems, hidden Markov model, intrusion detection program, online risk assessment, real time intrusion detection, real time intrusion prediction, real time intrusion prevention, hidden Markov models, risk management, security of data", DOI = "doi:10.1109/ISI.2008.4565018", size = "1.1 pages", abstract = "An intrusion detection program (IDP) analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. In this talk, we present some of the challenges in designing efficient intrusion detection systems (IDS) using nature inspired computation techniques, which could provide high accuracy, low false alarm rate and reduced number of features. Then we present some recent research results of developing distributed intrusion detection systems using genetic programming techniques. Further, we illustrate how intruder behavior could be captured using hidden Markov model and predict possible serious intrusions. Finally we illustrate the role of online risk assessment for intrusion prevention systems and some associated results.", notes = "Also known as \cite{4565018}", } @InProceedings{Abraham:2009:UKSIM, author = "Ajith Abraham and Crina Grosan and Vaclav Snasel", title = "Programming Risk Assessment Models for Online Security Evaluation Systems", booktitle = "11th International Conference on Computer Modelling and Simulation, UKSIM '09", year = "2009", month = "25-27 " # mar, pages = "41--46", keywords = "genetic algorithms, genetic programming, genetic programming methods, human reasoning, online security evaluation systems, perception process, programming risk assessment models, risk management, security of data", DOI = "doi:10.1109/UKSIM.2009.75", isbn13 = "978-0-7695-3593-7", abstract = "Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem.Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a genetic programming approach for risk assessment. Preliminary results indicate that genetic programming methods are robust and suitable for this problem when compared to other risk assessment models.", notes = "Also known as \cite{4809735}", } @InProceedings{Abraham:2009:IAS, author = "Ajith Abraham and Crina Grosan and Hongbo Liu and Yuehui Chen", title = "Hierarchical {Takagi-Sugeno} Models for Online Security Evaluation Systems", booktitle = "Fifth International Conference on Information Assurance and Security, IAS '09", year = "2009", month = aug, volume = "1", pages = "687--692", keywords = "genetic algorithms, genetic programming, hierarchical Takagi-Sugeno models, human perception, human reasoning, intrusion detection, neuro-fuzzy programming, online security evaluation systems, risk assessment, fuzzy reasoning, hierarchical systems, human factors, interactive programming, risk management, security of data", DOI = "doi:10.1109/IAS.2009.348", abstract = "Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a light weight risk assessment system based on an Hierarchical Takagi-Sugeno model designed using evolutionary algorithms. Performance comparison is done with neuro-fuzzy and genetic programming methods. Empirical results indicate that the techniques are robust and suitable for developing light weight risk assessment models, which could be integrated with intrusion detection and prevention systems.", notes = "Also known as \cite{5283215}", } @InCollection{abrams:2000:CSAMPR, author = "Zoe Abrams", title = "Complimentary Selection as an Alternative Method for Population Reproduction", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "8--15", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{abramson:1996:cccGP, author = "Myriam Abramson and Lawrence Hunter", title = "Classification using Cultural Co-Evolution and Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "249--254", address = "Stanford University, CA, USA", publisher = "MIT Press", broken = "ftp://lhc.nlm.nih.gov/pub/hunter/gp96.ps", size = "6 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap30.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @Article{Abu-Romoh:2018:ieeeCL, author = "M. Abu-Romoh and A. Aboutaleb and Z. Rezki", journal = "IEEE Communications Letters", title = "Automatic Modulation Classification Using Moments And Likelihood Maximization", year = "2018", abstract = "Motivated by the fact that moments of the received signal are easy to compute and can provide a simple way to automatically classify the modulation of the transmitted signal, we propose a hybrid method for automatic modulation classification that lies in the intersection between likelihood-based and feature-based classifiers. Specifically, the proposed method relies on statistical moments along with a maximum likelihood engine. We show that the proposed method offers a good tradeoff between classification accuracy and complexity relative to the Maximum Likelihood (ML) classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbour (GP-KNN) classifiers, the linear support vector machine classifier (LSVM) and the fold-based Kolmogorov-Smirnov (FB-KS) algorithm.", keywords = "genetic algorithms, genetic programming, Feature extraction, Machine learning algorithms, Modulation, Probability density function, Receivers, Signal to noise ratio, Support vector machines", DOI = "doi:10.1109/LCOMM.2018.2806489", ISSN = "1089-7798", notes = "Also known as \cite{8292836}", } @InProceedings{Abubakar:2016:ICCOINS, author = "Mustapha Yusuf Abubakar and Low Tang Jung and Mohamed Nordin Zakaria and Ahmed Younesy and Abdel-Haleem Abdel-Atyz", booktitle = "2016 3rd International Conference on Computer and Information Sciences (ICCOINS)", title = "New universal gate library for synthesizing reversible logic circuit using genetic programming", year = "2016", pages = "316--321", abstract = "We newly formed universal gate library, that includes NOT, CNOT (Feyman), Toffoli, Fredkin, Swap, Peres gates and a special gate called G gate. The gate G on its own is a universal gate, but using it alone in a library will result in large circuit realization. G gate combines the operations of Generalized Toffoli gates. For example a gate called G3 combines the operations of NOT, CNOT and T3 (3 - bit Toffoli) gates all in one place. The new library was used in synthesizing reversible circuits. The experiment was done using Genetic programming algorithm that is capable of allowing the choice of any type of gate library and optimising the circuit. The results were promising because the gate complexity in the circuits were drastically reduced compared to previously attempted synthesis.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCOINS.2016.7783234", month = aug, notes = "Also known as \cite{7783234}", } @Article{journals/qip/AbubakarJZYA17, author = "Mustapha Yusuf Abubakar and Low Tang Jung and Nordin Zakaria and Ahmed Younes and Abdel-Haleem Abdel-Aty", title = "Reversible circuit synthesis by genetic programming using dynamic gate libraries", journal = "Quantum Information Processing", year = "2017", number = "6", volume = "16", pages = "160", keywords = "genetic algorithms, genetic programming", bibdate = "2017-05-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/qip/qip16.html#AbubakarJZYA17", DOI = "doi:10.1007/s11128-017-1609-8", } @InProceedings{Abubakar:2018:ICCOINS, author = "Mustapha Yusuf Abubakar and Low {Tang Jung}", booktitle = "2018 4th International Conference on Computer and Information Sciences (ICCOINS)", title = "Synthesis of Reversible Logic Using Enhanced Genetic Programming Approach", year = "2018", abstract = "A new enhanced reversible logic circuit synthesis method was developed using reversible gates that include NOT, CNOT (Feynman), Toffoli, Fredkin, Swap, and Peres gates. The synthesis method was done using newly developed genetic programming. Usually previous synthesis methods that uses genetic algorithms or other similar evolutionary algorithms suffers a problem known as blotting which is a sudden uncontrolled growth of an individual (circuit), which may render the synthesis inefficient because of memory usage, making the algorithm difficult to continue running and eventually stack in a local minima, there for an optimized reversible circuit may not be generated. In this method the algorithm used was blot free, the blotting was carefully controlled by fixing a suitable length and size of the individuals in the population. Following this approach, the cost of generating circuits was greatly reduced giving the algorithm to reach the end of the last designated generation to give out optimal or near optimal results. The results of the circuits generated using this method were compared with some of the results already in the literature, and in many cases, our results appeared to be better in terms of gate count and quantum cost metrics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCOINS.2018.8510602", month = aug, notes = "Also known as \cite{8510602}", } @Article{Abud-Kappel:2016:Measurement, author = "Marco Andre {Abud Kappel} and Ricardo Fabbri and Roberto P. Domingos and Ivan N. Bastos", title = "Novel electrochemical impedance simulation design via stochastic algorithms for fitting equivalent circuits", journal = "Measurement", year = "2016", volume = "94", pages = "344--354", keywords = "genetic algorithms, genetic programming, Differential evolution, Electrochemical impedance, Impedance measurements, Corrosion, Optimization, Stochastic methods", ISSN = "0263-2241", URL = "https://www.sciencedirect.com/science/article/pii/S0263224116304699", DOI = "doi:10.1016/j.measurement.2016.08.008", abstract = "Electrochemical impedance spectroscopy (EIS) is of great value to corrosion studies because it is sensitive to transient changes that occur in the metal-electrolyte interface. A useful way to link the results of electrochemical impedance spectroscopy to corrosion phenomena is by simulating equivalent circuits. Equivalent circuit models are very attractive because of their relative simplicity, enabling the monitoring of electrochemical systems that have a complex physical mechanism. In this paper, the stochastic algorithm Differential Evolution is proposed to fit an equivalent circuit to the EIS results for a wide potential range. EIS is often limited to the corrosion potential despite being widely used. This greatly hinders the analysis regarding the effect of the applied potential, which strongly affects the interface, as shown, for example, in polarization curves. Moreover, the data from both the EIS and the DC values were used in the proposed scheme, allowing the best fit of the model parameters. The approach was compared to the standard Simplex square residual minimization of EIS data. In order to manage the large amount of generated data, the EIS-Mapper software package, which also plots the 2D/3D diagrams with potential, was used to fit the equivalent circuit of multiple diagrams. Furthermore, EIS-Mapper also computed all simulations. The results of 67 impedance diagrams of stainless steel in a 3.5percent NaCl medium at 25C obtained in steps of 10mV, and the respective values of the fitted parameters of the equivalent circuit are reported. The present approach conveys new insight to the use of electrochemical impedance and bridges the gap between polarization curves and equivalent electrical circuits.", } @PhdThesis{Tese_MarcoAndreAbudKappel, author = "Marco Andre Abud Kappel", title_pt = "Emprego de tecnicas computacionais estocasticas para simulacao de diagramasde espectroscopia de impedancia eletroquimica", title = "Stochastic computational techniques applied to the simulation of electrochemical impedance spectroscopy diagrams", school = "Centro de Tecnologia e Ciencias, Instituto Politecnico, Universidadedo Estado do Rio de Janeiro", year = "2016", address = "Nova Friburgo, Brazil", month = "8 " # apr, keywords = "genetic algorithms, genetic programming, Electrochemical impedance spectroscopy, Corrosion, Complex nonlinear optimization, Equivalent electrical circuit, Stochastic methods", URL = "http://www.bdtd.uerj.br/handle/1/13692", URL = "https://www.bdtd.uerj.br:8443/bitstream/1/13692/1/Tese_MarcoAndreAbudKappel.pdf", size = "169 pages", abstract = "Electrochemical impedance spectroscopy is a widely used technique in electrochemical systems characterization. With applications in several areas, the technique is very useful in the study of corrosion because it is sensitive to transient changes that occur in the metal interface. The results from the technique can be expressed and interpreted in different ways, allowing different modeling and analysis methods, such as the use of kinetic models or equivalent circuits. In corrosion, the technique is usually applied only in a few specific potentials, such as the corrosion potential, the most important. With the motivation of improving the impedance modeling and analysis process, taking into consideration that the electrochemical phenomena are strongly linked to the potential, this work introduces the possibility to express the impedance data in a wide potential range, and use them to equivalent circuits fitting. Thus, different phenomena can be modeled adequately by equivalent circuits corresponding to different potentials. For this purpose, the related inverse problem is solved for each potential through a complex nonlinear optimization process. In addition to the transient data obtained by the spectroscopy, stationary data are also used in the optimization as a regularisation factor, supporting a consistent solution to the physical phenomena involved, from the maximum experimental frequency to theoretical zero frequency. An analysis, modeling and simulation software was developed with the following features: 1) validation of experimental data, through the Kramers-Kronig relations; 2) simultaneous visualization of impedance results for a wide potential range; 3) fitting different equivalent circuits for different ranges using transient and stationary experimental data, in conjunction with deterministic or stochastic methods; 4) generation of confidence regions for the estimated parameters, making them statistically significant; 5) simulations using the fitted equivalent circuits in computer cluster; 6) parameter sensitivity analysis according to the applied potential, revealing important physical characteristics involved in the electrochemical processes. Finally, experimental fitting results and the corresponding simulations are shown and discussed. Results show that the use of a population-based stochastic optimization method not only increases the odds of finding the global optimum, but also enables the generation of confidence regions around the found values. Furthermore, only the circuit fitted with the new objective function has equivalence with both transient data and stationary data for the entire potential range involved.", resumo = "A espectroscopia de impedancia eletroquimica e uma tecnica amplamente utilizada na caracterizacao de sistemas eletroquimicos. Alem de possuir aplicacoes em diversas areas, a tecnica tem grande utilidade no estudo dacorrosao, pois e sensivel as variacoes transientes que ocorrem na interface metalica. Os resultados provenientes da tecnica podem ser expressos e interpretados de diversas formas, possibilitando diferentes metodologias de modelagem e analise, como o uso de modelos cineticos ou circuitos eletricos equivalentes. Em corrosao, a tecnica e aplicada, normalmente, em poucos potenciais especificos, como o de corrosao, o de maior importancia. Com a motivacao de aprimoraro procedimento de modelagem e analise de dados de impedancia, levando em consideracaoque os fenomenos eletroquimicos estao fortemente ligadosao potencial, este trabalho introduz a possibilidadede expressar os dados de impedancia em uma ampla faixa de potencial, e utiliza-los para ajuste de circuitos equivalentes. Assim, os diferentes fenomenos podem ser modelados, adequadamente, por circuitos eletricosequivalentes correspondentes a diferentes potenciais. Com esta finalidade, oproblema inverso associado eresolvidopara cada potencial, por meio de um processo de otimizacao complexa nao-linear. Alem dos dados transientes obtidos pela espectroscopia, dados estacionariossao utilizados na otimizacao de forma original, como uma regularizacao do problema, ajudando a garantir a obtencao de uma solucao coerente com os fenomenos fisicos envolvidos,desde a frequencia maxima do ensaio ate a frequencia nula. Um software de analise, modelagem e simulacao foi desenvolvido, com as seguintes funcionalidades: 1) validacaodos dados experimentais, por meio das relacoes de Kramers-Kronig; 2) visualizacao simultanea dos dados de impedanciapara ampla faixa de potencial; 3) ajuste dediferentes circuitos equivalentes para diferentes faixas,utilizando dados experimentais transientes e estacionarios, em conjunto com metodosdeterministicosou estocasticos; 4) geracao deregioes de confianca para os parametros ajustados, tornando-os estatisticamente significativos; 5) simulacoes utilizando os circuitos equivalentesajustadosem cluster de computador; 6) apresentacao de analise de sensibilidade dos parametros de acordo com o potencialaplicado, revelando caracteristicas fisicasimportantes envolvidas nos processos eletroquimicos. Por fim, resultados experimentaisdosajustes e das simulacoes correspondentes sao mostrados e discutidos.Os resultados obtidos mostram que a utilizacao de um metodo de otimizacao estocastico populacional nao apenas aumenta as probabilidades de se encontrar uma solucao melhor, como tambem possibilita a geracao das regioes de confianca em torno dos valores encontrados. Alem disso, apenas o circuito ajustado com a nova funcao objetivo possui equivalencia tanto com os dados transientes quanto com os dados estacionarios, para toda a faixa de potencial envolvida.", notes = "In Portuguese. Supervisors: Ivan Napoleao Bastos and Roberto Pinheiro Domingos", } @Article{Abud-Kappel:2017:ASC, author = "Marco Andre {Abud Kappel} and Fernando Cunha Peixoto and Gustavo Mendes Platt and Roberto Pinheiro Domingos and Ivan Napoleao Bastos", title = "A study of equivalent electrical circuit fitting to electrochemical impedance using a stochastic method", journal = "Applied Soft Computing", year = "2017", volume = "50", pages = "183--193", month = jan, keywords = "genetic algorithms, genetic programming, Differential evolution, Electrochemical impedance, Optimization, Stochastic method, Statistical analysis", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494616305993", DOI = "doi:10.1016/j.asoc.2016.11.030", size = "11 pages", abstract = "Modeling electrochemical impedance spectroscopy is usually done using equivalent electrical circuits. These circuits have parameters that need to be estimated properly in order to make possible the simulation of impedance data. Despite the fitting procedure is an optimization problem solved recurrently in the literature, rarely statistical significance of the estimated parameters is evaluated. In this work, the optimization process for the equivalent electrical circuit fitting to the impedance data is detailed. First, a mathematical development regarding the minimization of residual least squares is presented in order to obtain a statistically valid objective function of the complex nonlinear regression problem. Then, the optimization method used in this work is presented, the Differential Evolution, a global search stochastic method. Furthermore, it is shown how a population-based stochastic method like this can be used directly to obtain confidence regions to the estimated parameters. A sensitivity analysis was also conducted. Finally, the equivalent circuit fitting is done to model synthetic experimental data, in order to demonstrate the adopted procedure.", } @InProceedings{Abud-Kappel:2018:EngOpt, title = "Cartesian Genetic Programing Applied to Equivalent Electric Circuit Identification", author = "Marco Andre {Abud Kappel} and Roberto Pinheiro Domingos and Ivan Napoleao Bastos", booktitle = "Proceedings of the 6th International Conference on Engineering Optimization. EngOpt 2018", year = "2018", editor = "H. C. Rodrigues and J. Herskovits and C. M. {Mota Soares} and A. L. Araujo and J. M. Guedes and J. O. Folgado and F. Moleiro and J. F. A. Madeira", pages = "913--925", address = "Lisbon, Portugal", month = "17-19 " # sep, organisation = "Instituto Superior Tecnico", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Differential Evolution, Complex nonlinear optimization, Equivalent electric circuit identification", isbn13 = "978-3-319-97773-7", DOI = "doi:10.1007/978-3-319-97773-7_79", abstract = "Equivalent electric circuits are widely used in electrochemical impedance spectroscopy (EIS) data modeling. EIS modeling involves the identification of an electrical circuit physically equivalent to the system under analysis. This equivalence is based on the assumption that each phenomenon of the electrode interface and the electrolyte is represented by electrical components such as resistors, capacitors and inductors. This analogy allows impedance data to be used in simulations and predictions related to corrosion and electrochemistry. However, when no prior knowledge of the inner workings of the process under analysis is available, the identification of the circuit model is not a trivial task. The main objective of this work is to improve both the equivalent circuit topology identification and the parameter estimation by using a different approach than the usual Genetic Programming. In order to accomplish this goal, a methodology was developed to unify the application of Cartesian Genetic Programming to tackle system topology identification and Differential Evolution for optimization of the circuit parameters. The performance and effectiveness of this methodology were tested by performing the circuit identification on four different known systems, using numerically simulated impedance data. Results showed that the applied methodology was able to identify with satisfactory precision both the circuits and the values of the components. Results also indicated the necessity of using a stochastic method in the optimization process, since more than one electric circuit can fit the same dataset. The use of evolutionary adaptive metaheuristics such as the Cartesian Genetic Programming allows not only the estimation of the model parameters, but also the identification of its optimal topology. However, due to the possibility of multiple solutions, its application must be done with caution. Whenever possible, restrictions on the search space should be added, bearing in mind the correspondence of the model to the studied physical phenomena.", notes = "XVI Encontro de Modelagem Computacional ?", } @InProceedings{Abud-Kappel:2019:BRACIS, author = "Marco Andre {Abud Kappel}", booktitle = "2019 8th Brazilian Conference on Intelligent Systems (BRACIS)", title = "Action Scheduling Optimization using Cartesian Genetic Programming", year = "2019", pages = "293--298", month = oct, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", ISSN = "2643-6264", DOI = "doi:10.1109/BRACIS.2019.00059", abstract = "Action scheduling optimisation is a problem that involves chronologically organizing a set of actions, jobs or commands in order to accomplish a pre-established goal. This kind of problem can be found in a number of areas, such as production planning, delivery logistic organization, robot movement planning and behavior programming for intelligent agents in games. Despite being a recurrent problem, selecting the appropriate time and order to execute each task is not trivial, and typically involves highly complex techniques. The main objective of this work is to provide a simple alternative to tackle the action scheduling problem, by using Cartesian Genetic Programming as an approach. The proposed solution involves the application of two simple main steps: defining the set of available actions and specifying an objective function to be optimized. Then, by the means of the evolutionary algorithm, an automatically generated schedule will be revealed as the most fitting to the goal. The effectiveness of this methodology was tested by performing an action schedule optimization on two different problems involving virtual agents walking in a simulated environment. In both cases, results showed that, throughout the evolutionary process, the simulated agents naturally chose the most efficient sequential and parallel combination of actions to reach greater distances. The use of evolutionary adaptive metaheuristics such as Cartesian Genetic Programming allows the identification of the best possible schedule of actions to solve a problem.", notes = "Also known as \cite{8923702}", } @InProceedings{AbuDalhoum:2005:ESM, author = "Abdel Latif {Abu Dalhoum} and Moh'd {Al Zoubi} and Marina {de la Cruz} and Alfonso Ortega and Manuel Alfonseca", title = "A Genetic Algorithm for Solving the P-Median Problem", booktitle = "European Simulation and Modeling Conference ESM'2005", year = "2005", editor = "J. Manuel Feliz Teixeira and A. E.{Carvalho Brito}", pages = "141--145", address = "Porto, Portugal", month = oct # " 24-26", organisation = "Eurosim, The European Multidisciplinary Society for Modelling and Simulation Technology", publisher = "http://www.eurosis.org", keywords = "genetic algorithms, genetic programming, grammatical evolution, Christiansen grammar, location allocation, p-median model, grammar evolution", ISBN = "90-77381-22-8", URL = "http://arantxa.ii.uam.es/~alfonsec/docs/confint/pmedian.pdf", URL = "https://www.eurosis.org/cms/files/proceedings_full/ESM2005.deel2.pdf", size = "5 pages", abstract = "One of the most popular location-allocation models among researchers is the p-median. Most of the algorithmic research on these models has been devoted to developing heuristic solution procedures. The major drawback of heuristic methods is that the time required finding solutions can become unmanageable. In this paper, we propose a new algorithm, using different variants of grammar evolution, to solve the p-median problem.", notes = "Title may be listed as 'A Genetic Algorithm for solving the P-Medium Problem'. http://www.eurosis.org/cms/files/proceedings/ESM/ESM2005contents.pdf", } @Article{ABYANI:2022:oceaneng, author = "Mohsen Abyani and Mohammad Reza Bahaari and Mohamad Zarrin and Mohsen Nasseri", title = "Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques", year = "2022", journal = "Ocean Engineering", volume = "254", pages = "111382", month = "15 " # jun, keywords = "genetic algorithms, genetic programming, Offshore pipelines, Corrosion, Artificial neural network, ANN, Genetic programing, Support vector machine, SVM, Random forest, Gaussian process regression", ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2022.111382", URL = "https://www.sciencedirect.com/science/article/pii/S0029801822007697", abstract = "This paper aims to predict the failure pressure of corroded offshore pipelines, employing different machine learning techniques. To this end, an efficient finite element based algorithm is programmed to numerically estimate the failure pressure of offshore pipelines, subjected to internal corrosion. In this process, since the computational effort of such numerical assessment is very high, the application of reliable machine learning methods is used as an alternative solution. Thus, 1815 realizations of four variables are generated, and each one is keyed into the numerical model of a sample pipeline. Thereafter, the machine learning models are constructed based on the results of the numerical analyses, and their performance are compared with each other. The results indicate that Gaussian Process Regression (GPR) and MultiLayer Perceptron (MLP) have the best performance among all the chosen models. Considering the testing dataset, the squared correlation coefficient and Root Mean Squared Error (RMSE) values of GPR and MLP models are 0.535, 0.545 and 0.993 and 0.992, respectively. Moreover, the Maximum Von-Mises Stress (MVMS) of the pipeline increases as the water depth grows at low levels of Internal Pressure (IP). Inversely, increase in water depth leads to reduction in the MVMS values at high IP levels", notes = "Also known as \cite{ABYANI2022111382}", } @TechReport{AcarM05tr, author = "Aybar C. Acar and Amihai Motro", title = "Intensional Encapsulations of Database Subsets by Genetic Programming", institution = "Information and Software Engineering Department, The Volgenau School of Information Technology and Engineering, George Mason University", year = "2005", number = "ISE-TR-05-01", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://ise.gmu.edu/techrep/2005/05_01.pdf", abstract = "Finding intensional encapsulations of database subsets is the inverse of query evaluation. Whereas query evaluation transforms an intensional expression (the query) to its extension (a set of data values), intensional encapsulation assigns an intensional expression to a given set of data values. We describe a method for deriving intensional representations of subsets of records in large database tables. Our method is based on the paradigm of genetic programming. It is shown to achieve high accuracy and maintain compact expression size, while requiring cost that is acceptable to all applications, but those that require instantaneous results. Intensional encapsulation has a broad range of applications including cooperative answering, information integration, security and data mining.", notes = "See \cite{conf/dexa/AcarM05}", size = "17 pages", } @InProceedings{conf/dexa/AcarM05, title = "Intensional Encapsulations of Database Subsets via Genetic Programming", author = "Aybar C. Acar and Amihai Motro", year = "2005", pages = "365--374", editor = "Kim Viborg Andersen and John K. Debenham and Roland Wagner", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3588", booktitle = "Database and Expert Systems Applications, 16th International Conference, DEXA 2005, Proceedings", address = "Copenhagen, Denmark", month = aug # " 22-26", bibdate = "2005-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/dexa/dexa2005.html#AcarM05", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-28566-0", DOI = "doi:10.1007/11546924_36", size = "10 pages", abstract = "Finding intensional encapsulations of database subsets is the inverse of query evaluation. Whereas query evaluation transforms an intensional expression (the query) to its extension (a set of data values), intensional encapsulation assigns an intensional expression to a given set of data values. We describe a method for deriving intensional representations of subsets of records in large database tables. Our method is based on the paradigm of genetic programming. It is shown to achieve high accuracy and maintain compact expression size, while requiring cost that is acceptable to all applications, but those that require instantaneous results. Intensional encapsulation has a broad range of applications including cooperative answering, information integration, security and data mining.", notes = "See also \cite{AcarM05tr}", } @PhdThesis{Acar:thesis, author = "Aybar C. Acar", title = "Query Consolidation: Interpreting Queries Sent to Independent Heterogenous Databases", school = "The Volgenau School of Information Technology and Engineering, George Mason University", year = "2008", address = "Fairfax, VA, USA", month = "23 " # jul, keywords = "genetic algorithms, genetic programming, Databases, Information Integration, Query Processing, Machine Learning", URL = "http://hdl.handle.net/1920/3223", URL = "http://digilib.gmu.edu:8080/dspace/bitstream/1920/3223/1/Acar_Aybar.pdf", size = "182 pages", abstract = "This dissertation introduces the problem of query consolidation, which seeks to interpret a set of disparate queries submitted to independent databases with a single global query. The problem has multiple applications, from improving virtual database design, to aiding users in information retrieval, to protecting against inference of sensitive data from a seemingly innocuous set of apparently unrelated queries. The problem exhibits attractive duality with the much-researched problem of query decomposition, which has been addressed intensively in the context of multidatabase environments: How to decompose a query submitted to a virtual database into a set of local queries that are evaluated in individual databases. The new problem is set in the architecture of a canonical multidatabase system, using it in the reverse direction. The reversal is built on the assumption of conjunctive queries and source descriptions. A rational and efficient query decomposition strategy is also assumed, and this decomposition is reversed to arrive at the original query by analyzing the decomposed components. The process incorporates several steps where a number of solutions must be considered, due to the fact that query decomposition is not injective. Initially, the problem of finding the most likely join plan between component queries is investigated. This is accomplished by leveraging the referential constraints available in the underlying multidatabase, or by approximating these constraints from the data when not available. This approximation is done using the information theoretic concept of conditional entropy. Furthermore, the most likely join plans are enhanced by the expansion of their projections and adding precision to their selection constraints by estimating the selection constraints that would be applied to these consolidations offline. Additionally, the extraction of a set of queries related to the same retrieval task from an ongoing sequence of incoming queries is investigated. A conditional random field model is trained to segment and label incoming query sequences. Finally, the candidate consolidations are re-encapsulated with a genetic programming approach to find simpler intentional descriptions that are extensionally equivalent to discover the original intent of the query. The dissertation explains and discusses all of the above operations and validates the methods developed with experimentation on synthesised and real-world data. The results are highly encouraging and verify that the accuracy, time performance, and scalability of the methods would make it possible to exploit query consolidation in production environments.", notes = "GP chapters 7, 8", } @Article{ACEVEDO:2020:ESA, author = "Nicolas Acevedo and Carlos Rey and Carlos Contreras-Bolton and Victor Parada", title = "Automatic design of specialized algorithms for the binary knapsack problem", journal = "Expert Systems with Applications", year = "2020", volume = "141", pages = "112908", keywords = "genetic algorithms, genetic programming, Automatic generation of algorithms, Binary knapsack problem, Hyperheuristic, Generative design of algorithms", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419306268", DOI = "doi:10.1016/j.eswa.2019.112908", abstract = "Not all problem instances of a difficult combinatorial optimization problem have the same degree of difficulty for a given algorithm. Surprisingly, apparently similar problem instances may require notably different computational efforts to be solved. Few studies have explored the case that the algorithm that solves a combinatorial optimization problem is automatically designed. In consequence, the generation of the best algorithms may produce specialized algorithms according to the problem instances used during the constructive step. Following a constructive process based on genetic programming that combines heuristic components with an exact method, new algorithms for the binary knapsack problem are produced. We found that most of the automatically designed algorithms have better performance when solving instances of the same type used during construction, although the algorithms also perform well with other types of similar instances. The rest of the algorithms are partially specialized. We also found that the exact method that only solves a small knapsack problem has a key role in such results. When the algorithms are produced without considering such a method, the errors are higher. We observed this fact when the algorithms were constructed with a combination of instances from different types. These results suggest that the better the pre-classification of the instances of an optimization problem, the more specific and more efficient are the algorithms produced by the automatic generation of algorithms. Consequently, the method described in this article accelerates the search for efficient methods for NP-hard optimization problems", } @Article{ACHARYA:2020:PRL, author = "Divya Acharya and Shivani Goel and Rishi Asthana and Arpit Bhardwaj", title = "A novel fitness function in genetic programming to handle unbalanced emotion recognition data", journal = "Pattern Recognition Letters", volume = "133", pages = "272--279", year = "2020", ISSN = "0167-8655", DOI = "doi:10.1016/j.patrec.2020.03.005", URL = "http://www.sciencedirect.com/science/article/pii/S0167865520300830", keywords = "genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier transformation", abstract = "In the area of behavioral psychology, real-time emotion recognition by using physiological stimuli is an active topic of interest. This research considers the recognition of two class of emotions i.e., positive and negative emotions using EEG signals in response to happy, horror, sad, and neutral genres. In a noise-free framework for data acquisition of 50 participants, NeuroSky MindWave 2 is used. The dataset collected is unbalanced i.e., there are more instances of positive classes than negative ones. Therefore, accuracy is not a useful metric to assess the results of the unbalanced dataset because of biased results. So, the primary goal of this research is to address the issue of unbalanced emotion recognition dataset classification, for which we are proposing a novel fitness function known as Gap score (G score), which learns about both the classes by giving them equal importance and being unbiased. The genetic programming (GP) framework in which we implemented G score is named as G-score GP (GGP). The second goal is to assess how distinct genres affect human emotion recognition process and to identify an age group that is more active emotionally when their emotions are elicited. Experiments were conducted on EEG data acquired with a single-channel EEG device. We have compared the performance of GGP for the classification of emotions with state-of-the-art methods. The analysis shows that GGP provides 87.61percent classification accuracy by using EEG. In compliance with the self-reported feelings, brain signals of 26 to 35 years of age group provided the highest emotion recognition rate", } @Article{ACHARYA:2020:AA, author = "Divya Acharya and Anosh Billimoria and Neishka Srivastava and Shivani Goel and Arpit Bhardwaj", title = "Emotion recognition using fourier transform and genetic programming", journal = "Applied Acoustics", year = "2020", volume = "164", pages = "107260", month = jul, keywords = "genetic algorithms, genetic programming, Electroencephalogram, Fast Fourier Transform, Emotion recognition, Movie clips, Cinema Films", ISSN = "0003-682X", URL = "http://lrcdrs.bennett.edu.in:80/handle/123456789/1183", URL = "http://www.sciencedirect.com/science/article/pii/S0003682X19306954", DOI = "doi:10.1016/j.apacoust.2020.107260", abstract = "In cognitive science, the real-time recognition of humans emotional state is pertinent for machine emotional intelligence and human-machine interaction. Conventional emotion recognition systems use subjective feedback questionnaires, analysis of facial features from videos, and online sentiment analysis. This research proposes a system for real-time detection of emotions in response to emotional movie clips. These movie clips elicitate emotions in humans, and during that time, we have recorded their brain signals using Electroencephalogram (EEG) device and analyze their emotional state. This research work considered four class of emotions (happy, calm, fear, and sadness). This method leverages Fast Fourier Transform (FFT) for feature extraction and Genetic Programming (GP) for classification of EEG data. Experiments were conducted on EEG data acquired with a single dry electrode device NeuroSky Mind Wave 2. To collect data, a standardized database of 23 emotional Hindi film clips were used. All clips individually induce different emotions, and data collection was done based on these emotions elicited as the clips contain emotionally inductive scenes. Twenty participants took part in this study and volunteered for data collection. This system classifies four discrete emotions which are: happy, calm, fear, and sadness with an average of 89.14percent accuracy. These results demonstrated improvements in state-of-the-art methods and affirmed the potential use of our method for recognising these emotions", notes = "Also known as \cite{ACHARYA2020107260}", } @Article{ACHARYA:2021:ESA, author = "Divya Acharya and Nandana Varshney and Anindiya Vedant and Yashraj Saxena and Pradeep Tomar and Shivani Goel and Arpit Bhardwaj", title = "An enhanced fitness function to recognize unbalanced human emotions data", journal = "Expert Systems with Applications", volume = "166", pages = "114011", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2020.114011", URL = "https://www.sciencedirect.com/science/article/pii/S0957417420307843", keywords = "genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier Transformation, Unbalanced dataset", abstract = "In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers two-class (positive and negative) of emotions recognition using electroencephalogram (EEG) signals in response to an emotional clip from the genres happy, amusement, sad, and horror. This paper introduces an enhanced fitness function named as eD-score to recognize emotions using EEG signals. The primary goal of this research is to assess how genres affect human emotions. We also analyzed human behaviour based on age and gender responsiveness. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), D-score Genetic Programming (DGP), and enhanced D-score Genetic Programming (eDGP) for classification of emotions. The analysis shows that for two class of emotion eDGP provides classification accuracy as 83.33percent, 84.69percent, 85.88percent, and 87.61percent for 50-50, 60-40, 70-30, and 10-fold cross-validations. Generalizability and reliability of this approach is evaluated by applying the proposed approach to publicly available EEG datasets DEAP and SEED. When participants in this research are exposed to amusement genre, their reaction is positive emotion. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males. These results affirmed the potential use of our method for recognizing emotions", } @InProceedings{Ackling:2011:GECCO, author = "Thomas Ackling and Bradley Alexander and Ian Grunert", title = "Evolving patches for software repair", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1427--1434", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Debugging, fault-repair, Python", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", URL = "https://hdl.handle.net/2440/70777", DOI = "doi:10.1145/2001576.2001768", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "Defects are a major concern in software systems. Unsurprisingly, there are many tools and techniques to facilitate the removal of defects through their detection and localisation. However, there are few tools that attempt to repair defects. To date, evolutionary tools for software repair have evolved changes directly in the program code being repaired. In this work we describe an implementation: pyEDB, that encodes changes as a series of code modifications or patches. These modifications are evolved as individuals. We show pyEDB to be effective in repairing some small errors, including variable naming errors in Python programs. We also demonstrate that evolving patches rather than whole programs simplifies the removal of spurious errors.", notes = "Sofware bugs cost 2 10**10 dollar pa. Repairs 5 relational operators and wrong name. AST. tracing program execution. mod-tables like gramatical evolution. Tarantula. 32 bit GA. Examples: middleFunc and Facebook smallworld. Also known as \cite{2001768} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Article{journals/ijprai/Acosta-MendozaMEA14, title = "Learning to Assemble Classifiers via Genetic Programming", author = "Niusvel Acosta-Mendoza and Alicia Morales-Reyes and Hugo Jair Escalante and Andres Gago Alonso", journal = "IJPRAI", year = "2014", number = "7", volume = "28", keywords = "genetic algorithms, genetic programming", bibdate = "2014-10-20", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijprai/ijprai28.html#Acosta-MendozaMEA14", URL = "http://dx.doi.org/10.1142/S0218001414600052", } @InCollection{Adamatzky:2017:miller, author = "Andrew Adamatzky and Simon Harding and Victor Erokhin and Richard Mayne and Nina Gizzie and Frantisek Baluska and Stefano Mancuso and Georgios Ch. Sirakoulis", title = "Computers from Plants We Never Made: Speculations", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "17", pages = "357--387", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_17", abstract = "Plants are highly intelligent organisms. They continuously make distributed processing of sensory information, concurrent decision making and parallel actuation. The plants are efficient green computers per se. Outside in nature, the plants are programmed and hardwired to perform a narrow range of tasks aimed to maximize the plants ecological distribution, survival and reproduction. To persuade plants to solve tasks outside their usual range of activities, we must either choose problem domains which homomorphic to the plants natural domains or modify biophysical properties of plants to make them organic electronic devices. We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analogue electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, alpha-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @InProceedings{adamopoulos:1999:FMEPUGONN, author = "Adam V. Adamopoulos and Efstratios F. Georgopoulos and Spiridon D. Likothanassis and Photios A. Anninos", title = "Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1457--1462", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-767.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-767.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{adams:2002:CSDPSAGP, author = "Thomas P. Adams", title = "Creation of Simple, Deadline, and Priority Scheduling Algorithms using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Adams.pdf", notes = "part of \cite{koza:2002:gagp} memory, SETMO..SETM4 ECJ8, iteration (over job queue) branch and selection branch, communicate via memory. Run time used throughout (ie by functions and terminals) to identify jobs (ie scheduling tasks). 32 tasks shortest job first", } @InProceedings{Addis:2014:IACAP, author = "Mark Addis and Peter D. Sozou and Peter C. Lane and Fernand Gobet", title = "Computational Scientific Discovery and Cognitive Science Theories", booktitle = "Computing and Philosophy: Selected Papers from IACAP 2014", year = "2016", editor = "Vincent C. Mueller", pages = "83--97", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-23291-1", URL = "http://eprints.lse.ac.uk/66168/", URL = "https://doi.org/10.1007/978-3-319-23291-1_6", DOI = "doi:10.1007/978-3-319-23291-1_6", abstract = "This study is concerned with processes for discovering new theories in science. It considers a computational approach to scientific discovery, as applied to the discovery of theories in cognitive science. The approach combines two ideas. First, a process-based scientific theory can be represented as a computer program. Second, an evolutionary computational method, genetic programming, allows computer programs to be improved through a process of computational trial and error. Putting these two ideas together leads to a system that can automatically generate and improve scientific theories. The application of this method to the discovery of theories in cognitive science is examined. Theories are built up from primitive operators. These are contained in a theory language that defines the space of possible theories. An example of a theory generated by this method is described. These results support the idea that scientific discovery can be achieved through a heuristic search process, even for theories involving a sequence of steps. However, this computational approach to scientific discovery does not eliminate the need for human input. Human judgement is needed to make reasonable prior assumptions about the characteristics of operators used in the theory generation process, and to interpret and provide context for the computationally generated theories.", notes = "http://www.lse.ac.uk/philosophy/events/rethinking-theory-construction-in-social-science/", } @InProceedings{Adegboye:2017:ieeeSSCI), author = "Adesola Adegboye and Michael Kampouridis and Colin G. Johnson", booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Regression genetic programming for estimating trend end in foreign exchange market", year = "2017", address = "Honolulu, HI, USA", month = "27 " # nov # "-1 " # dec, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5386-2727-3", DOI = "doi:10.1109/SSCI.2017.8280833", abstract = "Most forecasting algorithms use a physical time scale for studying price movement in financial markets, making the flow of physical time discontinuous. The use of a physical time scale can make companies oblivious to significant activities in the market, which poses a risk. Directional changes is a different and newer approach, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change. Each of these trends are further dismembered into directional change (DC) event and overshoot (OS) event. We present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. This allows us to have an expectation when a trend will reverse, which can lead to increased profitability. This novel trend reversal estimation approach is then used as part of a DC-based trading strategy. We aim to appraise whether the new knowledge can lead to greater excess return. We assess the efficiency of the modified trading strategy on 250 different directional changes datasets from five different thresholds and five different currency pairs, consisting of intraday data from the foreign exchange (Forex) spot market. Results show that our algorithm is able to return profitable trading strategies and statistically outperform state-of-the-art financial trading strategies, such as technical analysis, buy and hold and other DC-based trading strategies.", notes = "Also known as \cite{8280833} https://www.ele.uri.edu/ieee-ssci2017/", } @Article{ADEGBOYE:2021:ESA, author = "Adesola Adegboye and Michael Kampouridis", title = "Machine learning classification and regression models for predicting directional changes trend reversal in {FX} markets", journal = "Expert Systems with Applications", year = "2021", volume = "173", month = "1 " # jul, pages = "114645", keywords = "genetic algorithms, genetic programming, Directional changes, Regression, Classification, Forex/FX, Machine learning", ISSN = "0957-4174", URL = "https://kar.kent.ac.uk/89886/1/Adegboye-INT2021_preprint.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S0957417421000865", DOI = "doi:10.1016/j.eswa.2021.114645", code_url = "https://github.com/adesolaadegboye/SymbolicRegression", size = "15 pages", abstract = "Most forecasting algorithms in financial markets use physical time for studying price movements, making the flow of time discontinuous. The use of physical time scale can make traders oblivious to significant activities in the market, which poses a risk. Directional changes (DC) is an alternative approach that uses event-based time to sample data. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to predict when a trend will reverse. This allows traders to be in a position to take an action before this happens and thus increase their profitability. We combine our approach with a novel DC-based trading strategy and perform an in-depth investigation, by applying it to 10-min data from 20 foreign exchange markets over a 10-month period. The total number of tested datasets is 1,000, which allows us to argue that our results can be generalised and are widely applicable. We compare our results to ten benchmarks (both DC and non-DC based, such as technical analysis and buy-and-hold). Our findings show that our proposed approach is able to return a significantly higher profit, as well as reduced risk, and statistically outperform the other trading strategies in a number of different performance metrics", notes = "School of Computing, University of Kent, Medway ME4 4AG, UK", } @PhdThesis{Adegboye:thesis, author = "Adesola Tolulope Noah Adegboye", title = "Estimating Directional Changes Trend Reversal in {Forex} Using Machine Learning", school = "University of Kent", year = "2022", address = "UK", month = mar, keywords = "genetic algorithms, genetic programming", URL = "https://kar.kent.ac.uk/94107/", URL = "https://kar.kent.ac.uk/94107/1/174thesis.pdf", DOI = "doi:10.22024/UniKent/01.02.94107", size = "209 pages", abstract = "Most forecasting algorithms use a physical time scale data to study price movement in financial markets by taking snapshots in fixed schedule, making the flow of time discontinuous. The use of a physical time scale can make traders oblivious to significant activities in the market, which poses risks. For example, currency risk, the risk that exchange rate will change. Directional changes is a different and newer approach of taking snapshot of the market, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change according to a change in price a trader considers to be significant, which is expressed as a threshold. The trends in the summary are split into directional change (DC) and overshoot (OS) events. In this work, we propose a novel DC-based framework, which uses machine learning algorithms to forecast when the next, alternate trend is expected to begin. First, we present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. Awareness of DC event and OS event lengths provide traders with an idea of when DC trends are expected to reverse and thus take appropriate action to increase profit or mitigate risk. Second, DC trends can be categorised into two distinct types: (1) trends with OS events; and (2) trends without OS events(i.e. OS event length is 0). Trends with OS events are those that continue beyond a period when they were first observed and trends without OS event are others that ends as soon as they were observed. To further improve trend reversal estimation accuracy, we identified these two categorises using classification techniques and estimated OS event length for trends that belong in the first category. We appraised whether this new knowledge could lead to an even greater excess return. Third, our novel trend reversal estimation approach was then used as part of a novel genetic algorithm (GA) based trading strategy. The strategy embedded an optimised trend reversal forecasting algorithm that was based on trend reversal point forecasted by multiple thresholds. We assessed the efficiency of our framework (i.e., a novel trend reversal approach and an optimised trading strategy) by performing an in-depth investigation. To assess our approach and evaluate the extent to which it could be generalised in Forex markets, we used five tailored thresholds to create 1000 DC datasets from 10, monthly 10 minute physical time data of 20 major Forex markets (i.e 5 thresholds * 10 months * 20 currency pairs). We compared our results to six benchmarks techniques, both DC and non-DC based, such as technical analysis and buy-and-hold. Our findings showed that our proposed approach can return a significantly higher profit at reduced risk, and statistically outperformed the other trading strategies compareds in a number of different performance metrics.", notes = "KAR id:94107 p156 'using symbolic regression GP approach ... to discover more complex relationship ... led to improvements in DC trend reversal forecasting accuracy' 'increased trading returns at reduced risk' Supervisor: Fernando Otero and Michael Kampouridis (Essex)", } @Article{ADEYI:2021:AIEPR, author = "Abiola John Adeyi and Oladayo Adeyi and Emmanuel Olusola Oke and Olusegun Abayomi Olalere and Seun Oyelami and Akinola David Ogunsola", title = "Effect of varied fiber alkali treatments on the tensile strength of Ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis", journal = "Advanced Industrial and Engineering Polymer Research", volume = "4", number = "1", pages = "29--40", year = "2021", ISSN = "2542-5048", DOI = "doi:10.1016/j.aiepr.2020.12.002", URL = "https://www.sciencedirect.com/science/article/pii/S2542504820300580", keywords = "genetic algorithms, genetic programming, Response surface methodology, Multigene genetic programming, Oracle crystal ball, Uncertainty and sensitivity analysis", abstract = "Studies on modeling and optimization of alkali treatment, investigation of experimental uncertainty and sensitivity analysis of alkali treatment factors of natural fibers are important to effective natural fiber reinforced polymer composite development. In this contribution, response surface methodology (RSM) was employed to investigate and optimize the effect of varied treatment factors (sodium hydroxide concentration (NaOH) and soaking time (ST)) of the alkali treatment of Ampelocissus cavicaulis natural fiber (ACNF) on the tensile strength (TS) of alkali treated ACNF reinforced polyester composite. RSM and multi gene genetic programming (MGGP) were comparatively employed to model the alkali treatment. The best model was applied in Oracle Crystal Ball (OCB) to investigate the uncertainty of the treatment results and sensitivity of the treatment factors. Results showed that increased NaOH and ST increased the TS of the alkali treated ACNF reinforced polyester composite up to 28.3500 MPa before TS decreased. The coefficient of determination (R2) and root mean square error (RMSE) of RSM model were 0.8920 and 0.6528 while that of MGGP were 0.9144 and 0.5812. The optimum alkali treatment established by RSM was 6.23percent of NaOH at 41.99 h of ST to give a TS of 28.1800 MPa with a desirability of 0.9700. The TS of the validated optimum alkali treatment condition was 28.2200 MPa. The certainty of the experimental results was 71.2580percent. TS was 13.8000percent sensitive to NaOH and 86.2000percent sensitive to ST. This work is useful for effective polymer composite materials production to reduce the enormous material and energy losses that usually accompany the process", } @Article{ADEYI:2022:AEJ, author = "Oladayo Adeyi and Abiola J. Adeyi and Emmanuel O. Oke and Bernard I. Okolo and Abayomi O. Olalere and John A. Otolorin and Samuel Okhale and Abiola E. Taiwo and Sunday O. Oladunni and Kelechi N. Akatobi", title = "Process integration for food colorant production from Hibiscus sabdariffa calyx: A case of multi-gene genetic programming ({MGGP)} model and techno-economics", journal = "Alexandria Engineering Journal", volume = "61", number = "7", pages = "5235--5252", year = "2022", ISSN = "1110-0168", DOI = "doi:10.1016/j.aej.2021.10.049", URL = "https://www.sciencedirect.com/science/article/pii/S1110016821006931", keywords = "genetic algorithms, genetic programming, Heat assisted technology based process, Multi-gene genetic programming, Annual production rate, Unit production cost, Techno-economics, calyx", abstract = "This work presents an integrated heat-assisted extraction process for the production of crude anthocyanins powder (CAnysP) from Hibiscus sabdariffa calyx using SuperPro Designer. The influence of process scale-up (0.04 -1000L) and variables (temperature, time and ethanol proportion in solvent) were investigated by adopting a circumscribed central composite design on techno-economic parameters such as annual production rate (APR) and unit production cost (UPC) CAnysP. The individual runs in the CCCD were taken as different process scenario and simulated independently. Multi-gene genetic programming (MGGP) was further used to develop robust predictive models. The robustness of the model and sensitivity analysis were ascertained using the Monte Carlo simulation. The process scenario at 30 min, 30 degreeC, 50percent and 1000 L possessed the highest CAnysP APR and lowest UPC. MGGP- models predicted R2 = 0.9984 for CAnysP APR and R2 = 0.9643 for UPC and certainty (99.98percent for CAnysP APR and 98.47percent for UPC)", } @InProceedings{adhikari:2019:SCDA, author = "Alok Adhikari and Nibedita Adhikari and K. C. Patra", title = "Shear Force Analysis and Modeling for Discharge Estimation Using Numerical and {GP} for Compound Channels", booktitle = "Soft Computing in Data Analytics", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-13-0514-6_32", DOI = "doi:10.1007/978-981-13-0514-6_32", } @Article{adhikari:JIEIa, author = "Alok Adhikari and N. Adhikari and K. C. Patra", title = "Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels", journal = "Journal of The Institution of Engineers (India): Series A", year = "2019", volume = "100", number = "3", pages = "395--405", month = sep, keywords = "genetic algorithms, genetic programming, FIS, ANFIS, GP", ISSN = "2250-2149", URL = "http://link.springer.com/article/10.1007/s40030-019-00367-x", DOI = "doi:10.1007/s40030-019-00367-x", size = "11 pages", abstract = "Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis.", } @Article{vu29881, author = "Sajal Kumar Adhikary and Nitin Muttil and Abdullah Yilmaz", title = "Genetic programming-based ordinary Kriging for spatial interpolation of rainfall", journal = "Journal of Hydrologic Engineering", year = "2016", volume = "21", number = "2", month = feb, keywords = "genetic algorithms, genetic programming, rainfall data, management of water resource systems, missing values, programming", publisher = "American Society of Civil Engineers", URL = "https://vuir.vu.edu.au/29881/", URL = "https://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0001300", DOI = "doi:10.1061/(ASCE)HE.1943-5584.0001300", abstract = "Rainfall data provide an essential input for most hydrologic analyses and designs for effective management of water resource systems. However, in practice, missing values often occur in rainfall data that can ultimately influence the results of hydrologic analysis and design. Conventionally, stochastic interpolation methods such as Kriging are the most frequently used approach to estimate the missing rainfall values where the variogram model that represents spatial correlations among data points plays a vital role and significantly impacts the performance of the methods. In the past, the standard variogram models in ordinary kriging were replaced with the universal function approximator-based variogram models, such as artificial neural networks (ANN). In the current study, applicability of genetic programming (GP) to derive the variogram model and use of this GP-derived variogram model within ordinary kriging for spatial interpolation was investigated. Developed genetic programming-based ordinary kriging (GPOK) was then applied for estimating the missing rainfall data at a rain gauge station using the historical rainfall data from 19 rain gauge stations in the Middle Yarra River catchment of Victoria, Australia. The results indicated that the proposed GPOK method outperformed the traditional ordinary kriging as well as the ANN-based ordinary kriging method for spatial interpolation of rainfall. Moreover, the GP-derived variogram model is shown to have advantages over the standard and ANN-derived variogram models. Therefore, the GP-derived variogram model seems to be a potential alternative to variogram models applied in the past and the proposed GPOK method is recommended as a viable option for spatial interpolation.", } @PhdThesis{vu35054, author = "Sajal Kumar Adhikary", title = "Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting", school = "College of Engineering and Science, Victoria University", year = "2017", address = "Melbourne, Australia", month = "20 " # mar, note = "This thesis includes 1 published article for which access is restricted due to copyright (Chapters 3, 4 (first paper). Details of access to these papers have been inserted in the thesis, replacing the articles themselves.", keywords = "genetic algorithms, genetic programming, rivers, water basins, streams, stream-flow simulation, modeling, water supply, spatial interpolation, genetic programming-based ordinary kriging, thesis by publication", URL = "https://vuir.vu.edu.au/35054/", URL = "https://vuir.vu.edu.au/35054/7/ADHIKARY%20Sajal-thesis_Redacted.pdf", size = "225 pages", abstract = "Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians. The study had three components. First, an evaluation of existing Kriging methods and universal function approximation techniques such as genetic programming (GP) and ANN were performed in terms of their potentials and suitability for the enhanced spatial estimation of rainfall. The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall and the ordinary cokriging using elevation can enhance the spatial estimation of rainfall. Second, the design of an optimal rain gauge network was undertaken for the case study catchment using the kriging-based geostatistical approach based on the variance reduction framework. It is likely that an existing rain gauge network may consist of redundant stations, which have no contribution to the network performance for providing quality rainfall estimates. Therefore, the optimal network was achieved through optimal placement of additional stations (network augmentation) as well as eliminating or optimally relocating of redundant stations (network rationalization). In order to take the rainfall variability caused by climatic factors like El Nino Southern Oscillation into account, the network was designed using rainfall records for both El Nino and La Nina periods. The rain gauge network that gives the improved estimates of areal average and point rainfalls for both the El Nino and La Nina periods was selected as the optimal network. It was found that the optimal network outperformed the existing one in estimating the spatiotemporal estimates of areal average and point rainfalls. Additionally, optimal positioning of redundant stations was found to be highly effective to achieve the optimal rain gauge network. Third, an ANN-based enhanced streamflow forecasting approach was demonstrated, which incorporated the optimal rain gauge network-based input instead of using input from an existing non-optimal network to achieve the enhanced streamflow forecasting. The approach was found to be highly effective in improving the accuracy of stream-flow forecasting, particularly when the current operational rain gauge network is not an optimal one. For example, it was found that use of the optimal rain gauge network-based input results in the improvement of streamflow forecasting accuracy by 7.1percent in terms of normalised root mean square error (NRMSE) compared to the current rain gauge network based-input. Further improvement in streamflow forecasting was achieved through augmentation of the optimal network by incorporating additional fictitious rain gauge stations. The fictitious stations were added in sub-catchments that were delineated based on the digital elevation model. It was evident from the results that 18.3percent improvement in streamflow forecasting accuracy was achieved in terms of NRMSE using the augmented optimal rain gauge network-based input compared to the current rain gauge network-based input. The ANN-based input selection technique that was employed in this study for streamflow forecasting offers a viable technique for significant input variables selection as this technique is capable of learning problems involving very non-linear and complex data.", notes = "pii 'The evaluation confirmed that the fusion of GP and ordinary kriging is highly effective for the improved estimation of rainfall' Supervisor: Nitin Muttil", } @Article{Adib:2021:WRM, author = "Arash Adib and Arash Zaerpour and Ozgur Kisi and Morteza Lotfirad", title = "A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from {SSMIS} Data and Evaluation of its Reliability by Uncertainty Parameters", journal = "Water Resources Management", year = "2021", volume = "35", pages = "2723--2740", month = jul, keywords = "genetic algorithms, genetic programming, gene expression programming, passive microwave, special sensor microwave imager sounder, snow depth retrieval, discrete wavelet transform, wavelet-packet transform", publisher = "springer", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02863-x", oai = "oai:RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02863-x", URL = "http://link.springer.com/10.1007/s11269-021-02863-x", DOI = "doi:10.1007/s11269-021-02863-x", abstract = "This study demonstrates the application of wavelet transform comprising discrete wavelet transform, maximum overlap discrete wavelet transform (MODWT), and multiresolution-based MODWT (MODWT-MRA), as well as wavelet packet transform (WP), coupled with artificial intelligence (AI)-based models including multi-layer perceptron, radial basis function, adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming to retrieve snow depth (SD) from special sensor microwave imager sounder obtained from the national snow and ice data center. Different mother wavelets were applied to the passive microwave (PM) frequencies; afterward, the dominant resultant decomposed subseries comprising low frequencies (approximations) and high frequencies (details) were detected and inserted into the AI-based models. The results indicated that the WP coupled with ANFIS (WP-ANFIS) outperformed the other studied models with the determination coefficient of 0.988, root mean square error of 3.458 cm, mean absolute error of 2.682 cm, and Nash--Sutcliffe efficiency of 0.987 during testing period. The final verification also confirmed that the WP is a promising pre-processing technique to improve the accuracy of the AI-based models in SD evaluation from PM data.", } @InProceedings{Adkins:2023:evomusart, author = "Sara Adkins and Pedro Sarmento and Mathieu Barthet", title = "{LooperGP}: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature", booktitle = "12th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMusArt 2023", year = "2023", month = apr # " 12-14", editor = "Colin Johnson and Nereida Rodriguez-Fernandez and Sergio M. Rebelo", series = "LNCS", volume = "13988", publisher = "Springer Verlag", address = "Brno, Czech Republic", pages = "3--19", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Controllable Music Generation, Sequence Models, Live Coding, Transformers, AI Music, Loops, Guitar Tabs", isbn13 = "978-3-031-29956-8", DOI = "doi:10.1007/978-3-031-29956-8_1", data_url = "https://github.com/dada-bots/dadaGP", size = "17 pages", abstract = "Despite their impressive offline results, deep learning models for symbolic music generation are not widely used in live performances due to a deficit of musically meaningful control parameters and a lack of structured musical form in their outputs. To address these issues we introduce LooperGP, a method for steering a Transformer-XL model towards generating loopable musical phrases of a specified number of bars and time signature, enabling a tool for live coding performances. We show that by training LooperGP on a dataset of 93681 musical loops extracted from the DadaGP dataset [Data GuitarPro], we are able to steer its generative output towards generating three times as many loopable phrases as our baseline. In a subjective listening test conducted by 31 participants, LooperGP loops achieved positive median ratings in originality, musical coherence and loop smoothness, demonstrating its potential as a performance tool.", notes = "http://www.evostar.org/2023/ EvoMusArt2023 held in conjunction with EuroGP'2023, EvoCOP2023 and EvoApplications2023", } @Misc{adler2021improving, author = "Felix Adler and Gordon Fraser and Eva Gruendinger and Nina Koerber and Simon Labrenz and Jonas Lerchenberger and Stephan Lukasczyk and Sebastian Schweikl", title = "Improving Readability of {Scratch} Programs with Search-based Refactoring", howpublished = "arXiv", year = "2021", month = "16 " # aug, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", eprint = "2108.07114", URL = "https://arxiv.org/abs/2108.07114", size = "13 pages", abstract = "Block-based programming languages like Scratch have become increasingly popular as introductory languages for novices. These languages are intended to be used with a tinkering approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, Scratch lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve Scratch code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of Scratch programs. By automating these transformations it is possible to explore the space of possible variations of Scratch programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given Scratch program and therefore form refactorings. Evaluation on a random sample of 1000 Scratch programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4percent of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.", notes = "p4 'we need the search to evolve sequences of refactorings that explain the changes' p10 'Solutions are transformed 10.1 times on average, and the most common transformations are Swap Statements, Merge Scripts and Split Scripts' p10 'our modification operators are designed to preserve the program semantics' Published as \cite{DBLP:conf/scam/AdlerFGKLLLS21} University of Passau, Germany", } @InProceedings{DBLP:conf/scam/AdlerFGKLLLS21, author = "Felix Adler and Gordon Fraser and Eva Gruendinger and Nina Koerber and Simon Labrenz and Jonas Lerchenberger and Stephan Lukasczyk and Sebastian Schweikl", title = "Improving Readability of {Scratch} Programs with Search-based Refactoring", booktitle = "21st IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2021", year = "2021", pages = "120--130", address = "Luxembourg", month = sep # " 27-28", note = "16000 GP entry", keywords = "genetic algorithms, genetic programming, genetic improvement, grammatical evolution, SBSE, NSGA-II, LitterBox, JSON, refactoring, Java", isbn13 = "978-1-6654-4898-7", timestamp = "Sat, 09 Apr 2022 12:45:31 +0200", biburl = "https://dblp.org/rec/conf/scam/AdlerFGKLLLS21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://arxiv.org/abs/2108.07114", URL = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9610643", DOI = "doi:10.1109/SCAM52516.2021.00023", code_url = "https://github.com/se2p/artifact-scam2021", video_url = "https://youtu.be/9ndMZvgEOVg", size = "11 pages", abstract = "Block-based programming languages like SCRATCH have become increasingly popular as introductory languages for novices. These languages are intended to be used with a tinkering approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, SCRATCH lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve SCRATCH code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of SCRATCH programs. By automating these transformations it is possible to explore the space of possible variations of SCRATCH programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given SCRATCH program and therefore form refactorings. Evaluation on a random sample of 1000 SCRATCH programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4% of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.", notes = "Also known as \cite{ImprovingReadabilityScratch}. See also \cite{adler2021improving} '26 atomic code transformations' [to the AST]. 'We evaluate an implementation of this approach on a random sample of 1000 learners program'. '704 are improved with respect to complexity and entropy, and 354...' size, Halstead complexity, and Shannon entropy (based on program syntax). p122 'Inverse transformation' of the AST. control dependence graph. data dependence graph. 'forward-may dataflow analysis to identify which statements are time-dependent on which other statements' p124 'integer representation inspired by grammatical evolution' p126 'verify that the code transformations preserve the semantics' 'Applicable code transformations were found for all but 28 [of 1000] projects in our dataset.' Many uses of swap. p129 'The number of code transformations that do not change the program semantics found by our search is substantial' 'neutral program space'. 'explainability central' SCAM 2021 Also known as \cite{9610643}", } @InProceedings{adorni:1998:cpapc, author = "Giovanni Adorni and Federico Bergenti and Stefano Cagnoni", title = "A cellular-programming approach to pattern classification", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "142--150", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055934", abstract = "In this paper we discuss the capability of the cellular programming approach to produce non-uniform cellular automata performing two-dimensional pattern classification. More precisely, after an introduction to the evolutionary cellular automata model, we describe a general approach suitable for designing cellular classifiers. The approach is based on a set of non-uniform cellular automata performing specific classification tasks, which have been designed by means of a cellular evolutionary algorithm. The proposed approach is discussed together with some preliminary results obtained on a benchmark data set consisting of car-plate digits.", notes = "EuroGP'98", affiliation = "University of Parma Department of Computer Engineering viale delle Scienze 43100 Parma Italy viale delle Scienze 43100 Parma Italy", } @InProceedings{adorni:1999:GPgkcsrcmsc, author = "Giovanni Adorni and Stefano Cagnoni and Monica Mordonini", title = "Genetic Programming of a Goal-Keeper Control Strategy for the RoboCup Middle Size Competition", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "109--119", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_9", abstract = "In this paper we describe a genetic programming approach to the design of a motion-control strategy for a goalkeeper robot created to compete in the RoboCup99, the robot soccer world championships which have been held yearly since 1997, as part of the Italian middle size robot team (ART, Azzurra Robot Team). The evolved program sends a motion command to the robot, based on the analysis of information received from a human-coded vision sub-system. The preliminary results obtained on a simulator are encouraging. They suggest that even using very simple fitness functions and training sets including only a small sub-set of the situations that the goalkeeper is required to tackle, it is possible to evolve a complex behaviour that permits the goalkeeper to perform well also in more challenging real-world conditions.", notes = "EuroGP'99, part of \cite{poli:1999:GP} Robot goalkeeper (.4m) controlled by twin cameras using GP. Able to intercept football sometimes.", } @InProceedings{oai:CiteSeerPSU:539182, author = "Giovanni Adorni and Stefano Cagnoni and Monica Mordonini", title = "Efficient low-level vision program design using Sub-machine-code Genetic Programming", booktitle = "AIIA 2002, Workshop sulla Percezione e Visione nelle Macchine", year = "2002", editor = "Marco Gori", address = "Siena, Italy", month = "10-13 " # sep, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:127480; oai:CiteSeerPSU:236266; oai:CiteSeerPSU:154480; oai:CiteSeerPSU:124779; oai:CiteSeerPSU:98673; oai:CiteSeerPSU:519310; oai:CiteSeerPSU:493558; oai:CiteSeerPSU:151056; oai:CiteSeerPSU:128026; oai:CiteSeerPSU:195245; oai:CiteSeerPSU:154216; oai:CiteSeerPSU:198970; oai:CiteSeerPSU:98326; oai:CiteSeerPSU:197064; oai:CiteSeerPSU:88615; oai:CiteSeerPSU:200149; oai:CiteSeerPSU:299112", citeseer-references = "oai:CiteSeerPSU:70349; oai:CiteSeerPSU:329358", annote = "The Pennsylvania State University CiteSeer Archives", description = "Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs.", language = "en", oai = "oai:CiteSeerPSU:539182", rights = "unrestricted", URL = "http://www-dii.ing.unisi.it/aiia2002/paper/PERCEVISIO/adorni-aiia02.pdf", URL = "http://citeseer.ist.psu.edu/539182.html", size = "8 pages", abstract = "Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs. The paper describes an approach to low-level vision algorithm design for real-time applications by means of Sub-machine-code Genetic Programming(SmcGP), a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs. The SmcGPbased design of two processing modules of a license-plate recognition system is taken into consideration as a case study to show the potential of the approach. The paper reports results obtained in recognizing the very low-resolution binary patterns that have to be classified in such an application along with preliminary results obtained using SmcGP to design a license-plate extraction algorithm.", notes = "http://www-dii.ing.unisi.it/aiia2002/paper.htm", } @InProceedings{adorni:2001:wsc6, author = "Giovanni Adorni and Stefano Cagnoni", title = "Design of Explicitly or Implicitly Parallel Low-resolution Character Recognition Algorithms by Means of Genetic Programming", booktitle = "Soft Computing and Industry Recent Applications", year = "2001", editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann", pages = "387--398", month = "10--24 " # sep, publisher = "Springer-Verlag", note = "Published 2002", keywords = "genetic algorithms, genetic programming", ISBN = "1-85233-539-4", URL = "https://link.springer.com/book/10.1007/978-1-4471-0123-9", URL = "http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394", notes = "WSC6 March 2020 hardcover out of print, available as softcover. ", } @Misc{oai:arXiv.org:1410.0532, title = "Automated conjecturing of {Frobenius} numbers via grammatical evolution", note = "Comment: 8 pages, 2 tables; added a clear introduction, otherwise reduced text significantly", author = "Nikola Adzaga", year = "2014", month = feb # "~17", keywords = "genetic algorithms, genetic programming, grammatical evolution, mathematics, number theory, mathematics, combinatorics", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1410.0532", URL = "http://arxiv.org/abs/1410.0532", abstract = "Conjecturing formulae and other symbolic relations occurs frequently in number theory and combinatorics. If we could automate conjecturing, we could benefit not only from speeding up, but also from finding conjectures previously out of our grasp. Grammatical evolution, a genetic programming technique, can be used for automated conjecturing in mathematics. Concretely, this work describes how one can interpret the Frobenius problem as a symbolic regression problem, and then apply grammatical evolution to it. In this manner, a few formulas for Frobenius numbers of specific quadruples were found automatically. The sketch of the proof for one conjectured formula, using lattice point enumeration method, is provided as well. Same method can easily be used on other problems to speed up and enhance the research process.", notes = "See also \cite{Adzaga:2017:EM} Faculty of Civil Engineering, University of Zagreb", } @Article{Adzaga:2017:EM, author = "Nikola Adzaga", title = "Automated Conjecturing of {Frobenius} Numbers via Grammatical Evolution", journal = "Experimental Mathematics", year = "2017", volume = "26", number = "2", pages = "247--252", keywords = "genetic algorithms, genetic programming, grammatical evolution, automated conjecturing, Frobenius problem", ISSN = "1058-6458", publisher = "Taylor \& Francis", DOI = "doi:10.1080/10586458.2016.1175393", size = "6 pages", abstract = "Conjecturing formulas and other symbolic relations occurs frequently in number theory and combinatorics. If we could automate conjecturing, we could benefit not only from faster conjecturing but also from finding conjectures previously out of our grasp. Grammatical evolution (GE), a genetic programming technique, can be used for automated conjecturing in mathematics. Concretely, this work describes how one can interpret the Frobenius problem as a symbolic regression problem, and then apply GE to it. In this manner, a few formulas for Frobenius numbers of specific quadruples were found automatically. The sketch of the proof of one conjectured formula, using lattice point enumeration method, is provided as well. The same method can easily be used on other problems to speed up and enhance the research process.", notes = "See also \cite{oai:arXiv.org:1410.0532} Department of Mathematics, Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia", } @InProceedings{Aenugu:2019:GECCO, author = "Sneha Aenugu and Lee Spector", title = "Lexicase selection in learning classifier systems", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "356--364", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321828", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, LCS, Learning Classifier Systems, Parent Selection, Lexicase Selection", size = "9 pages", abstract = "The lexicase parent selection method selects parents by considering performance on individual data points in random order instead of using a fitness function based on an aggregated data accuracy. While the method has demonstrated promise in genetic programming and more recently in genetic algorithms, its applications in other forms of evolutionary machine learning have not been explored. In this paper, we investigate the use of lexicase parent selection in Learning Classifier Systems (LCS) and study its effect on classification problems in a supervised setting. We further introduce a new variant of lexicase selection, called batch-lexicase selection, which allows for the tuning of selection pressure. We compare the two lexicase selection methods with tournament and fitness proportionate selection methods on binary classification problems. We show that batch-lexicase selection results in the creation of more generic rules which is favourable for generalization on future data. We further show that batch-lexicase selection results in better generalization in situations of partial or missing data.", notes = "Does not appear to be GP? Also known as \cite{3321828} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Affenzeller:2005:ICANNGA, author = "M. Affenzeller and S. Wagner", title = "Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms", booktitle = "Proceedings of the seventh International Conference Adaptive and Natural Computing Algorithms", year = "2005", editor = "Bernardete Ribeiro and Rudolf F. Albrecht and Andrej Dobnikar and David W. Pearson and Nigel C. Steele", pages = "218--221", address = "Coimbra, Portugal", month = "21-23 " # mar, publisher = "Springer", keywords = "genetic algorithms, genetic programming, OS-GP", isbn13 = "978-3-211-24934-5", URL = "https://link.springer.com/chapter/10.1007/3-211-27389-1_52", URL = "https://doi.org/10.1007/b138998", DOI = "doi:10.1007/3-211-27389-1_52", abstract = "In terms of goal orientedness, selection is the driving force of Genetic Algorithms (GAs). In contrast to crossover and mutation, selection is completely generic, i.e. independent of the actually employed problem and its representation. GA-selection is usually implemented as selection for reproduction (parent selection). In this paper we propose a second selection step after reproduction which is also absolutely problem independent. This self-adaptive selection mechanism, which will be referred to as offspring selection, is closely related to the general selection model of population genetics. As the problem- and representation-specific implementation of reproduction in GAs (crossover) is often critical in terms of preservation of essential genetic information, offspring selection has proven to be very suited for improving the global solution quality and robustness concerning parameter settings and operators of GAs in various fields of applications. The experimental part of the paper discusses the potential of the new selection model exemplarily on the basis of standardized real-valued test functions in high dimensions", notes = "\cite{DBLP:conf/eurocast/BurlacuAKKW17} gives this as reference for OS-GP http://icannga05.dei.uc.pt/", } @Book{Affenzeller:GAGP, author = "Michael Affenzeller and Stephan Winkler and Stefan Wagner and Andreas Beham", title = "Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications", publisher = "CRC Press", year = "2009", series = "Numerical Insights", address = "Singapore", keywords = "genetic algorithms, genetic programming", ISBN = "1-58488-629-3", URL = "http://gagp2009.heuristiclab.com/", URL = "http://www.crcpress.com/product/isbn/9781584886297", abstract = "Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimisation problems and describes structure identification using HeuristicLab as a platform for algorithm development. The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimisation problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems. Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.", notes = "Reviewed in \cite{Pappa:2009:GPEM}. My copy missing pages i to vi. ", size = "379 pages", } @Article{Affenzeller:2010:IJSPM, title = "Effective allele preservation by offspring selection: an empirical study for the {TSP}", author = "Michael Affenzeller and Stefan Wagner and Stephan M. Winkler", journal = "International Journal of Simulation and Process Modelling", year = "2010", month = apr # "~11", volume = "6", number = "1", pages = "29--39", keywords = "genetic algorithms, genetic programming, soft computing, evolutionary computation, GAs selection, self adaptation, population genetics, evolution strategies, modelling, allele preservation, offspring selection, travelling salesman problem", ISSN = "1740-2131", URL = "https://pure.fh-ooe.at/en/publications/effective-allele-preservation-by-offspring-selection-an-empirical-2", URL = "http://www.inderscience.com/link.php?id=32655", DOI = "doi:10.1504/IJSPM.2010.032655", language = "eng", publisher = "Inderscience Publishers", abstract = "The basic selection ideas of the different representatives of evolutionary algorithms are sometimes quite diverse. The selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realised by the selection of above-average parents for reproduction, whereas Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest due to birth surplus). This contribution considers aspects of population genetics and ES in order to propose an enhanced and generic selection model for GAs which is able to preserve the alleles which are part of a high quality solution. Some selected aspects of these enhanced techniques are discussed exemplary on the basis of the Travelling Salesman Benchmark (TSP) problem instances.", bibsource = "OAI-PMH server at www.inderscience.com", notes = "GA rather than GP? Also http://www.liophant.org/i3m/i3m2008/", } @InProceedings{2453, author = "M. Affenzeller and C. Fischer and G. K. Kronberger and S. M. Winkler and S. Wagner", title = "New Genetic Programming Hypothesis Search Strategies for Improving the Interpretability in Medical Data Mining Applications", booktitle = "Proccedings of 23rd IEEE European Modeling \& Simulation Symposium EMSS 2011", year = "2011", address = "Roma, Italy", month = sep, URL = "http://research.fh-ooe.at/files/publications/2453_EMSS_2011_Affenzeller.pdf", keywords = "genetic algorithms, genetic programming", } @InProceedings{Affenzeller:2012:EMSS, author = "Michael Affenzeller and Stephan M. Winkler and Stefan Forstenlechner and Gabriel Kronberger and Michael Kommenda and Stefan Wagner and Herbert Stekel", title = "Enhanced Confidence Interpretations of GP Based Ensemble Modeling Results", booktitle = "The 24th European Modeling and Simulation Symposium, EMSS 2012", year = "2012", editor = "Emilio Jimenez and Boris Sokolov", pages = "340--345", address = "Vienna, Austria", month = sep # ", 19-21", keywords = "genetic algorithms, genetic programming, data mining, ensemble modelling, medical data analysis", URL = "http://research.fh-ooe.at/en/publication/2935", URL = "http://research.fh-ooe.at/files/publications/2935_EMSS_2012_Affenzeller.pdf", size = "6 pages", abstract = "In this paper we describe the integration of ensemble modelling into genetic programming based classification and discuss concepts how to use genetic programming specific features for achieving new confidence indicators that estimate the trustworthiness of predictions. These new concepts are tested on a real world dataset from the field of medical diagnosis for cancer prediction where the trustworthiness of modeling results is of highest importance", notes = "EMSS_76 http://www.msc-les.org/conf/emss2012/index.htm http://www.m-s-net.org/conf/i3m2012_program.pdf University of Applied Sciences Upper Austria; General Hospital Linz Central Laboratory - Austria", } @InProceedings{Affenzeller:2013:EUROCAST, author = "Michael Affenzeller and Stephan M. Winkler and Herbert Stekel and Stefan Forstenlechner and Stefan Wagner", title = "Improving the Accuracy of Cancer Prediction by Ensemble Confidence Evaluation", booktitle = "Computer Aided Systems Theory - EUROCAST 2013", year = "2013", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "8111", series = "Lecture Notes in Computer Science", pages = "316--323", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 10-15", publisher = "Springer", note = "Revised Selected Papers, Part I", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-53855-1", language = "English", URL = "http://dx.doi.org/10.1007/978-3-642-53856-8_40", DOI = "doi:10.1007/978-3-642-53856-8_40", size = "8 pages", abstract = "This paper discusses a novel approach for the prediction of breast cancer, melanoma and cancer in the respiratory system using ensemble modelling techniques. For each type of cancer, a set of unequally complex predictors are learnt by symbolic classification based on genetic programming. In addition to standard ensemble modeling, where the prediction is based on a majority voting of the prediction models, two confidence parameters are used which aim to quantify the trustworthiness of each single prediction based on the clearness of the majority voting. Based on the calculated confidence of each ensemble prediction, predictions might be considered uncertain. The experimental part of this paper discusses the increase of accuracy that can be obtained for those samples which are considered trustable depending on the ratio of predictions that are considered trustable.", } @InCollection{Affenzeller:2013:GPTP, author = "Michael Affenzeller and Stephan M. Winkler and Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and Stefan Wagner", title = "Gaining Deeper Insights in Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XI", year = "2013", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek", publisher = "Springer", chapter = "10", pages = "175--190", address = "Ann Arbor, USA", month = "9-11 " # may, keywords = "genetic algorithms, genetic programming, Symbolic regression, Algorithm analysis, Population diversity Building block analysis, Genealogy, Variable networks", isbn13 = "978-1-4939-0374-0", DOI = "doi:10.1007/978-1-4939-0375-7_10", abstract = "A distinguishing feature of symbolic regression using genetic programming is its ability to identify complex nonlinear white-box models. This is especially relevant in practice where models are extensively scrutinised in order to gain knowledge about underlying processes. This potential is often diluted by the ambiguity and complexity of the models produced by genetic programming. In this contribution we discuss several analysis methods with the common goal to enable better insights in the symbolic regression process and to produce models that are more understandable and show better generalisation. In order to gain more information about the process we monitor and analyse the progresses of population diversity, building block information, and even more general genealogy information. Regarding the analysis of results, several aspects such as model simplification, relevance of variables, node impacts, and variable network analysis are presented and discussed.", notes = " Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013", } @InProceedings{6339, author = "Michael Affenzeller and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Gabriel K. Kronberger and Stefan Wagner", title = "Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals", booktitle = "16th International Conference on Computer Aided Systems Theory, EUROCAST 2017", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "424--431", address = "Las Palmas de Gran Canaria, Spain", month = feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-74718-7", DOI = "doi:10.1007/978-3-319-74718-7_51", URL = "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_51", abstract = "This paper proposes some algorithmic extensions to the general concept of offspring selection which itself is an algorithmic extension of genetic algorithms and genetic programming. Offspring selection is characterized by the fact that many offspring solution candidates will not participate in the ongoing evolutionary process if they do not achieve the success criterion. The algorithmic enhancements proposed in this contribution aim to early estimate if a solution candidate will not be accepted based on partial solution evaluation. The qualitative characteristics of offspring selection are not affected by this means. The discussed variant of offspring selection is analysed for several symbolic regression problems with offspring selection genetic programming. The achievable gains in terms of efficiency are remarkable especially for large data-sets.", } @InProceedings{Affenzeller:2017:GECCO, author = "Michael Affenzeller and Stephan M. Winkler and Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Stefan Wagner", title = "Dynamic Observation of Genotypic and Phenotypic Diversity for Different Symbolic Regression {GP} Variants", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "1553--1558", size = "6 pages", URL = "http://doi.acm.org/10.1145/3067695.3082530", DOI = "doi:10.1145/3067695.3082530", acmid = "3082530", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic and phenotypic diversity, offspring selection, population dynamics, symbolic regression", month = "15-19 " # jul, abstract = "Understanding the relationship between selection, genotype-phenotype map and loss of population diversity represents an important step towards more effective genetic programming (GP) algorithms. This paper describes an approach to capture dynamic changes in this relationship. We analyse the frequency distribution of points in the diversity plane defined by structural and semantic similarity measures. We test our methodology using standard GP (SGP) on a number of test problems, as well as Offspring Selection GP (OS-GP), an algorithmic flavour where selection is explicitly focused towards adaptive change. We end with a discussion about the implications of diversity maintenance for each of the tested algorithms. We conclude that diversity needs to be considered in the context of fitness improvement, and that more diversity is not necessarily beneficial in terms of solution quality.", notes = "Also known as \cite{Affenzeller:2017:DOG:3067695.3082530} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{DBLP:conf/eurocast/AffenzellerBDDH19, author = "Michael Affenzeller and Bogdan Burlacu and Viktoria Dorfer and Sebastian Dorl and Gerhard Halmerbauer and Tilman Koenigswieser and Michael Kommenda and Julia Vetter and Stephan M. Winkler", title = "White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems", booktitle = "17th International Conference, Computer Aided Systems Theory - {EUROCAST} 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "288--295", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 17-22", publisher = "Springer", note = "Revised Selected Papers, Part {I}", keywords = "genetic algorithms, genetic programming", timestamp = "Mon, 05 Feb 2024 20:28:43 +0100", biburl = "https://dblp.org/rec/conf/eurocast/AffenzellerBDDH19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1007/978-3-030-45093-9_35", DOI = "doi:10.1007/978-3-030-45093-9_35", } @InProceedings{Affenzeller:2022:GPTP, author = "Bogdan Burlacu and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler and Michael Affenzeller", title = "Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "1--30", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", URL = "https://arxiv.org/abs/2206.06422", DOI = "doi:10.1007/978-981-19-8460-0_1", abstract = "Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and the understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow calculating the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however, their applicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful “white-box” approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{Affenzeller:2023:GPTP, author = "Michael Affenzeller", title = "{GP} in Prescriptive Analytics", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @Article{Afshar:2017:RSE, author = "M. H. Afshar and M. T. Yilmaz", title = "The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products", journal = "Remote Sensing of Environment", volume = "196", pages = "224--237", year = "2017", ISSN = "0034-4257", DOI = "doi:10.1016/j.rse.2017.05.017", URL = "http://www.sciencedirect.com/science/article/pii/S003442571730216X", abstract = "In this study, the added utility of nonlinear rescaling methods relative to linear methods in the framework of creating a homogenous soil moisture time series has been explored. The performances of 31 linear and nonlinear rescaling methods are evaluated by rescaling the Land Parameter Retrieval Model (LPRM) soil moisture datasets to station-based watershed average datasets obtained over four United States Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds. The linear methods include first-order linear regression, multiple linear regression, and multivariate adaptive regression splines (MARS), whereas the nonlinear methods include cumulative distribution function matching (CDF), artificial neural networks (ANN), support vector machines (SVM), Genetic Programming (GEN), and copula methods. MARS, GEN, SVM, ANN, and the copula methods are also implemented to use lagged observations to rescale the datasets. The results of a total of 31 different methods show that the nonlinear methods improve the correlation and error statistics of the rescaled product compared to the linear methods. In general, the method that yielded the best results using training data improved the validation correlations, on average, by 0.063, whereas ELMAN ANN and GEN, using lagged observations methods, yielded correlation improvements of 0.052 and 0.048, respectively. The lagged observations improved the correlations when they were incorporated into rescaling equations in linear and nonlinear fashions, with the nonlinear methods (particularly SVM and GEN but not ANN and copula) benefitting from these lagged observations more than the linear methods. The overall results show that a large majority of the similarities between the LPRM and watershed average datasets are due to linear relations; however, nonlinear relations clearly exist, and the use of nonlinear rescaling methods clearly improves the accuracy of the rescaled product.", keywords = "genetic algorithms, genetic programming, Soil moisture, Rescaling, Linear, Nonlinear, Remote sensing", } @InProceedings{Timperley:2018:GI, author = "Afsoon Afzal and Jeremy Lacomis and Claire {Le Goues} and Christopher Steven Timperley", title = "A {Turing} Test for Genetic Improvement", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "17--18", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "978-1-4503-5753-1", URL = "http://dx.doi.org/10.1145/3194810.3194817", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Timperley_2018_GI.pdf", URL = "https://afsafzal.github.io/materials/AfzalTuringTest2018.pdf", DOI = "doi:10.1145/3194810.3194817", size = "2 pages", abstract = "Genetic improvement is a research field that aims to develop searchbased techniques for improving existing code. GI has been used to automatically repair bugs, reduce energy consumption, and to improve run-time performance. In this paper, we reflect on the often-overlooked relationship between GI and developers within the context of continually evolving software systems. We introduce a distinction between transparent and opaque patches based on intended lifespan and developer interaction. Finally, we outline a Turing test for assessing the ability of a GI system to produce opaque patches that are acceptable to humans. This motivates research into the role GI systems will play in transparent development contexts.", notes = "Note author order change. GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @PhdThesis{Afsoon_Afzal:thesis, author = "Afsoon Afzal", title = "Automated Testing of Robotic and Cyberphysical Systems", school = "Institute for Software Research, School of Computer Science, Carnegie Mellon University", year = "2021", address = "Pittsburgh, PA 15213, USA", month = may # " 2021", keywords = "SBSE, testing cyber-physical systems, robotics testing, automated quality assurance, simulation-based testing, challenges of testing, automated oracle inference, automated test generation", URL = "https://afsafzal.github.io/materials/thesis.pdf", size = "142 pages", abstract = "Robotics and cyberphysical systems are increasingly being deployed to settings where they are in frequent interaction with the public. Therefore, failures in these systems can be catastrophic by putting human lives in danger and causing extreme financial loss. Large-scale assessment of the quality of these systems before deployment can prevent these costly damages. Because of the complexity and other special features of these systems, testing,and more specifically automated testing, faces challenges. In this dissertation, I study the unique challenges of testing robotics and cyberphysical systems, and propose an end-to-end automated testing pipeline to provide tools and methods that can help roboticists in large-scale, automated testing of their systems. My key insight is that we can use (low-fidelity) simulation to automatically test robotic and cyber-physical systems, and identify many potentially catastrophic failures in advance at low cost. My core thesis is: Robotic and cyberphysical systems have unique features such as interacting with the physical world and integrating hardware and software components, which creates challenges for automated, large-scale testing approaches. An automated testing framework using software-in-the-loop (low-fidelity) simulation can facilitate automated testing for these systems. This framework can be offered using a clustering approach as an automated oracle, and an evolutionary-based automated test input generation with scenario coverage fitness functions. To support this thesis, I conduct a number of qualitative, quantitative, and mixed method studies that 1) identify main challenges of testing robotic and cyberphysical systems, 2) show that low-fidelity simulation can be an effective approach in detecting bugs and errors with low cost, and 3) identify challenges of using simulators in automated testing. Additionally, I propose automated techniques for creating oracles and generating test inputs to facilitate automated testing of robotic and cyberphysical systems. I present an approach to automatically generate oracles for cyberphysical systems using clustering, which can observe and identify common patterns of system behavior.These patterns can be used to distinguish erroneous behavior of the system and act as an oracle. I evaluate the quality of test inputs for robotic systems with respect to their reliability, and effectiveness in revealing faults in the system. I observe a high rate of non-determinism among test executions that complicates test input generation and evaluation, and show that coverage-based metrics are generally poor indicators of test input quality. Finally, I present an evolutionary-based automated test generation approach with a fitness function that is based on scenario coverage. The automated oracle and automated test input generation approaches contribute to a fully automated testing framework that can perform large-scale, automated testing on robotic and cyberphysical systems in simulation.", notes = "is this GP? CMU-ISR-21-105 Supervisor: Claire Le Goues", } @Article{Afzal:2021:TSE, author = "Afsoon Afzal and Manish Motwani and Kathryn T. Stolee and Yuriy Brun and Claire {Le Goues}", title = "{SOSRepair}: Expressive Semantic Search for Real-World Program Repair", journal = "IEEE Transactions on Software Engineering", year = "2021", volume = "47", number = "10", pages = "2162--2181", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", ISSN = "0098-5589", URL = "https://doi.org/10.1109/TSE.2019.2944914", DOI = "doi:10.1109/TSE.2019.2944914", abstract = "Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35percent) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35percent) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair's success. Manually improving fault localisation allows SOSRepair to patch 24 (37percent) defects, of which 16 (67percent) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches.", notes = "Also known as \cite{8854217}", } @InProceedings{AfzalTF08, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "A Systematic Mapping Study on Non-Functional Search-based Software Testing", booktitle = "Proceedings of the 20th International Conference on Software Engineering and Knowledge Engineering (SEKE '08)", year = "2008", pages = "488--493", address = "San Francisco, CA, USA", month = jul # " 1-3", publisher = "Knowledge Systems Institute Graduate School", keywords = "genetic algorithms, genetic programming", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", ISBN = "1-891706-22-5", URL = "http://www.torkar.se/resources/A-systematic-mapping-study-on-non-functional-search-based-software-testing.pdf", size = "6 pages", abstract = "Automated software test generation has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional), grey-box (combination of structural and functional) and non-functional testing. In this paper, we undertake a systematic mapping study to present a broad review of primary studies on the application of search-based optimization techniques to non-functional testing. The motivation is to identify the evidence available on the topic and to identify gaps in the application of search-based optimization techniques to different types of non-functional testing. The study is based on a comprehensive set of 35 papers obtained after using a multi-stage selection criteria and are published in workshops, conferences and journals in the time span 1996--2007. We conclude that the search-based software testing community needs to do more and broader studies on non-functional search-based software testing (NFSBST) and the results from our systematic map can help direct such efforts.", notes = "http://www.ksi.edu/seke/seke08.html http://www.ksi.edu/seke/sk08pgm.html http://www.ksi.edu/seke/tocs/seke2008toc.pdf ", } @InProceedings{Afzal08e, author = "Wasif Afzal and Richard Torkar", title = "Suitability of Genetic Programming for Software Reliability Growth Modeling", booktitle = "The 2008 International Symposium on Computer Science and its Applications (CSA'08)", year = "2008", pages = "114--117", address = "Hobart, ACT", month = "13-15 " # oct, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, software reliability data points, software reliability growth modeling, SBSE", DOI = "doi:10.1109/CSA.2008.13", abstract = "Genetic programming (GP) has been found to be effective in finding a model that fits the given data points without making any assumptions about the model structure. This makes GP a reasonable choice for software reliability growth modeling. This paper discusses the suitability of using GP for software reliability growth modeling and highlights the mechanisms that enable GP to progressively search for fitter solutions.", notes = "Also known as \cite{4654071} ", } @InProceedings{Afzal08d, author = "Wasif Afzal and Richard Torkar", title = "A comparative evaluation of using genetic programming for predicting fault count data", booktitle = "Proceedings of the Third International Conference on Software Engineering Advances (ICSEA'08)", year = "2008", pages = "407--414", address = "Sliema, Malta", month = "26-31", keywords = "genetic algorithms, genetic programming, prediction, software reliability growth modeling, SBSE", isbn13 = "978-1-4244-3218-9", DOI = "doi:10.1109/ICSEA.2008.9", abstract = "There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count data.", notes = "Also known as \cite{4668139} ", } @InProceedings{Afzal08b, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "Prediction of fault count data using genetic programming", booktitle = "Proceedings of the 12th IEEE International Multitopic Conference (INMIC'08)", year = "2008", pages = "349--356", address = "Karachi, Pakistan", month = "23-24 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, fault count data, prediction", isbn13 = "978-1-4244-2823-6", URL = "http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf", DOI = "doi:10.1109/INMIC.2008.4777762", abstract = "Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past. Due to the existence of many models, the models' inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The goodness of fit (adaptability) and predictive accuracy of the evolved model is measured using five different measures in an attempt to present a fair evaluation. The results show that the GP evolved model has statistically significant goodness of fit and predictive accuracy.", notes = "Also known as \cite{4777762} ", } @InProceedings{Afzal:2009:SSBSE, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "Search-Based Prediction of Fault Count Data", booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009", year = "2009", editor = "Massimiliano {Di Penta} and Simon Poulding", pages = "35--38", address = "Windsor, UK", month = "13-15 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, search-based prediction, software fault count data, software reliability growth model, symbolic regression, regression analysis, software fault tolerance", isbn13 = "978-0-7695-3675-0", DOI = "doi:10.1109/SSBSE.2009.17", abstract = "Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-defined function, symbolic regression finds a general function, with coefficients, fitting the given set of data points. The concepts of symbolic regression using genetic programming can be used to evolve a model for fault count predictions. Such a model has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictions using genetic programming and comparing the results with traditional approaches to compare efficiency gains.", notes = "order number P3675 http://www.ssbse.info/ Also known as \cite{5033177}", } @Article{Afzal2009, author = "Wasif Afzal and Richard Torkar and Robert Feldt", title = "A systematic review of search-based testing for non-functional system properties", journal = "Information and Software Technology", year = "2009", volume = "51", number = "6", pages = "957--976", month = jun, keywords = "genetic algorithms, genetic programming, Systematic review, Non-functional system properties, Search-based software testing", ISSN = "0950-5849", URL = "http://drfeldt.googlepages.com/afzal_submitted0805ist_sysrev_nfr_sb.pdf", URL = "http://www.sciencedirect.com/science/article/B6V0B-4VHXDTD-1/2/9da989f9d874eb88d1f82d9a0878114b", DOI = "doi:10.1016/j.infsof.2008.12.005", size = "19 pages", abstract = "Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.", } @MastersThesis{Afzal:Licentiate, author = "Wasif Afzal", title = "Search-Based Approaches to Software Fault Prediction and Software Testing", school = "School of Engineering, Dept. of Systems and Software Engineering, Blekinge Institute of Technology", year = "2009", type = "Licentiate Dissertation", address = "Sweden", keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Computer Science, Artificial Intelligence", URL = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3/$file/Afzal_lic.pdf", broken = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3?OpenDocument", size = "212 pages", isbn13 = "978-91-7295-163-1", language = "eng", oai = "oai:bth.se:forskinfoF0738B5FC4CA0BBAC12575980043DEF3", abstract = "Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing schedule and testing resource allocation needs to be as accurate as possible. This thesis investigates the application of search-based techniques within two activities of software verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing non-functional system properties. Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.", } @InCollection{Afzal:2010:ECoaSE, author = "Wasif Afzal and Richard Torkar and Robert Feldt and Tony Gorschek", title = "Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects", booktitle = "Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques", publisher = "IGI Global", year = "2010", editor = "Monica Chis", chapter = "6", pages = "94--126", month = jun, keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "9781615208098", DOI = "doi:10.4018/978-1-61520-809-8.ch006", abstract = "Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modelling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.", } @InProceedings{Afzal:2010:SSBSE, author = "Wasif Afzal and Richard Torkar and Robert Feldt and Greger Wikstrand", title = "Search-based Prediction of Fault-slip-through in Large Software Projects", booktitle = "Second International Symposium on Search Based Software Engineering (SSBSE 2010)", year = "2010", month = "7-9 " # sep, pages = "79--88", address = "Benevento, Italy", keywords = "genetic algorithms, genetic programming, gene expression programming, sbse, AIRS, GEP, GP, MR, PSO-ANN, artificial immune recognition system, artificial neural network, fault-slip-through, multiple regression, particle swarm optimisation, search-based prediction, software project, software testing process, artificial immune systems, fault tolerant computing, neural nets, particle swarm optimisation, program testing, regression analysis", DOI = "doi:10.1109/SSBSE.2010.19", isbn13 = "978-0-7695-4195-2", abstract = "A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.", notes = "IEEE Computer Society Order Number P4195 BMS Part Number: CFP1099G-PRT Library of Congress Number 2010933544 http://ssbse.info/2010/program.php Also known as \cite{5635180}", } @InProceedings{Afzal:2010:APSEC, author = "Wasif Afzal", title = "Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness", booktitle = "17th Asia Pacific Software Engineering Conference (APSEC 2010)", year = "2010", month = nov # " 30-" # dec # " 3", pages = "414--422", abstract = "Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the specific test levels of integration and system test. Method: We applied eight classification techniques, to the task of identifying fault prone modules, representing a variety of approaches, including a standard statistical technique for classification (logistic regression), tree-structured classifiers (C4.5 and random forests), a Bayesian technique (Naive Bayes), machine-learning techniques (support vector machines and back-propagation artificial neural networks) and search-based techniques (genetic programming and artificial immune recognition systems) on FST data collected from two large industrial projects from the telecommunication domain. Results: Using area under the receiver operating characteristic (ROC) curve and the location of (PF, PD) pairs in the ROC space, the faults slip-through metric showed impressive results with the majority of the techniques for predicting fault-prone modules at both integration and system test levels. There were, however, no statistically significant differences between the performance of different techniques based on AUC, even though certain techniques were more consistent in the classification performance at the two test levels. Conclusions: We can conclude that the faults-slip-through metric is a potentially strong predictor of fault-proneness at integration and system test levels. The faults-slip-through measurements interact in ways that is conveniently accounted for by majority of the data mining techniques.", keywords = "genetic algorithms, genetic programming, sbse, Bayesian technique, artificial immune recognition systems, back-propagation artificial neural networks, data mining, fault-proneness predictor, faults-slip-through metric, logistic regression, machine-learning techniques, receiver operating characteristic curve, search-based techniques, software faults, software quality, standard statistical technique, support vector machines, system test levels, tree-structured classifiers, backpropagation, data mining, neural nets, program testing, software quality, statistical analysis, support vector machines", DOI = "doi:10.1109/APSEC.2010.54", ISSN = "1530-1362", notes = "Blekinge Inst. of Technol., Ronneby, Sweden. Also known as \cite{5693218}", } @Article{Afzal201111984, author = "Wasif Afzal and Richard Torkar", title = "On the application of genetic programming for software engineering predictive modeling: A systematic review", journal = "Expert Systems with Applications", volume = "38", number = "9", pages = "11984--11997", year = "2011", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2011.03.041", URL = "http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c", keywords = "genetic algorithms, genetic programming, Systematic review, Symbolic regression, Modelling", abstract = "The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modelling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modelling; the results are inconclusive for software cost/effort/size estimation.", } @PhdThesis{Afzal:thesis, author = "Wasif Afzal", title = "Search-Based Prediction of Software Quality: Evaluations And Comparisons", school = "School of Computing, Blekinge Institute of Technology", year = "2011", address = "Sweden", month = "5 " # may, keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://www.bth.se/fou/forskinfo.nsf/0/dd0dcce8cc126a52c125784500410306/$file/Dis%20Wasif%20Afzal%20thesis.pdf", isbn13 = "978-91-7295-203-4", size = "313 pages", abstract = "Software verification and validation (V&V) activities are critical for achieving software quality; however, these activities also constitute a large part of the costs when developing software. Therefore efficient and effective software V&V activities are both a priority and a necessity considering the pressure to decrease time-to-market and the intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions that affects software quality, e.g., how to allocate testing resources, develop testing schedules and to decide when to stop testing, needs to be as stable and accurate as possible. The objective of this thesis is to investigate how search-based techniques can support decision-making and help control variation in software V&V activities, thereby indirectly improving software quality. Several themes in providing this support are investigated: predicting reliability of future software versions based on fault history; fault prediction to improve test phase efficiency; assignment of resources to fixing faults; and distinguishing fault-prone software modules from non-faulty ones. A common element in these investigations is the use of search-based techniques, often also called metaheuristic techniques, for supporting the V&V decision-making processes. Search-based techniques are promising since, as many problems in real world, software V&V can be formulated as optimisation problems where near optimal solutions are often good enough. Moreover, these techniques are general optimization solutions that can potentially be applied across a larger variety of decision-making situations than other existing alternatives. Apart from presenting the current state of the art, in the form of a systematic literature review, and doing comparative evaluations of a variety of metaheuristic techniques on large-scale projects (both industrial and open-source), this thesis also presents methodological investigations using search-based techniques that are relevant to the task of software quality measurement and prediction. The results of applying search-based techniques in large-scale projects, while investigating a variety of research themes, show that they consistently give competitive results in comparison with existing techniques. Based on the research findings, we conclude that search-based techniques are viable techniques to use in supporting the decision-making processes within software V&V activities. The accuracy and consistency of these techniques make them important tools when developing future decision support for effective management of software V&V activities.", notes = "Advisors, Dr. Richard Torkar and Dr. Robert Feldt. http://www.bth.se/eng/calendar.nsf/allaDok/9984ce8a1e18e8ecc125782a004d4167!OpenDocument Doctoral Dissertation Series No. 2011:06", } @Article{Afzal:2013:SQJ, author = "Wasif Afzal and Richard Torkar and Robert Feldt and Tony Gorschek", title = "Prediction of faults-slip-through in large software projects: an empirical evaluation", journal = "Software Quality Journal", year = "2014", volume = "22", number = "1", pages = "51--86", month = mar, publisher = "Springer US", keywords = "genetic algorithms, genetic programming, SBSE, Prediction, Empirical, Faults-slip-through, Search-based", ISSN = "0963-9314", DOI = "doi:10.1007/s11219-013-9205-3", language = "English", oai = "oai:bth.se:forskinfo3D40224F7CBF862DC1257B7800251E66", URL = "http://www.bth.se/fou/forskinfo.nsf/all/3d40224f7cbf862dc1257b7800251e66?OpenDocument", size = "36 pages", abstract = "A large percentage of the cost of rework can be avoided by finding more faults earlier in a software test process. Therefore, determination of which software test phases to focus improvement work on has considerable industrial interest. We evaluate a number of prediction techniques for predicting the number of faults slipping through to unit, function, integration, and system test phases of a large industrial project. The objective is to quantify improvement potential in different test phases by striving toward finding the faults in the right phase. The results show that a range of techniques are found to be useful in predicting the number of faults slipping through to the four test phases; however, the group of search-based techniques (genetic programming, gene expression programming, artificial immune recognition system, and particle swarm optimisation (PSO) based artificial neural network) consistently give better predictions, having a representation at all of the test phases. Human predictions are consistently better at two of the four test phases. We conclude that the human predictions regarding the number of faults slipping through to various test phases can be well supported by the use of search-based techniques. A combination of human and an automated search mechanism (such as any of the search-based techniques) has the potential to provide improved prediction results.", } @InCollection{Afzal2016, author = "Wasif Afzal and Richard Torkar", title = "Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction", booktitle = "Computational Intelligence and Quantitative Software Engineering", publisher = "Springer", year = "2016", editor = "Witold Pedrycz and Giancarlo Succi and Alberto Sillitti", volume = "617", series = "Studies in Computational Intelligence", chapter = "3", pages = "33--58", keywords = "genetic algorithms, genetic programming, SBSE, Feature subset selection, Fault prediction, Empirical", isbn13 = "978-3-319-25964-2", DOI = "doi:10.1007/978-3-319-25964-2_3", abstract = "Despite the general acceptance that software engineering datasets often contain noisy, irrelevant or redundant variables, very few benchmark studies of feature subset selection (FSS) methods on real-life data from software projects have been conducted. This paper provides an empirical comparison of state-of-the-art FSS methods: information gain attribute ranking (IG); Relief (RLF); principal component analysis (PCA); correlation-based feature selection (CFS); consistency-based subset evaluation (CNS); wrapper subset evaluation (WRP); and an evolutionary computation method, genetic programming (GP), on five fault prediction datasets from the PROMISE data repository. For all the datasets, the area under the receiver operating characteristic curve, the AUC value averaged over 10-fold cross-validation runs, was calculated for each FSS method-dataset combination before and after FSS. Two diverse learning algorithms, C4.5 and naive Bayes (NB) are used to test the attribute sets given by each FSS method. The results show that although there are no statistically significant differences between the AUC values for the different FSS methods for both C4.5 and NB, a smaller set of FSS methods (IG, RLF, GP) consistently select fewer attributes without degrading classification accuracy. We conclude that in general, FSS is beneficial as it helps improve classification accuracy of NB and C4.5. There is no single best FSS method for all datasets but IG, RLF and GP consistently select fewer attributes without degrading classification accuracy within statistically significant boundaries.", } @InProceedings{afzali:2018:AJCAI, author = "Shima Afzali and Harith Al-Sahaf and Bing Xue and Christopher Hollitt and Mengjie Zhang", title = "A Genetic Programming Approach for Constructing Foreground and Background Saliency Features for Salient Object Detection", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_21", DOI = "doi:10.1007/978-3-030-03991-2_21", } @InProceedings{Afzali:2019:evoapplications, author = "Shima Afzali and Harith Al-Sahaf and Bing Xue and Christopher Hollitt and Mengjie Zhang", title = "Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "308--324", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Salient Object Detection, Feature combination, Feature selection", isbn13 = "978-3-030-16691-5", DOI = "doi:10.1007/978-3-030-16692-2_21", abstract = "Salient Object Detection (SOD) aims to model human visual attention system to cope with the complex natural scene which contains various objects at different scales. Over the past two decades, a wide range of saliency features have been introduced in the SOD field, however feature selection has not been widely investigated for selecting informative, non-redundant, and complementary features from the existing features. In SOD, multi-level feature extraction and feature combination are two fundamental stages to compute the final saliency map. However, designing a good feature combination framework is a challenging task and requires domain-expert intervention. In this paper, we propose a genetic programming (GP) based method that is able to automatically select the complementary saliency features and generate mathematical function to combine those features. The performance of the proposed method is evaluated using four benchmark datasets and compared to nine state-of-the-art methods. The qualitative and quantitative results show that the proposed method significantly outperformed, or achieved comparable performance to, the competitor methods.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @PhdThesis{Afzali:thesis, author = "Shima {Afzali Vahed Moghaddam}", title = "Evolutionary Computation for Feature Manipulation in Salient Object Detection", school = "Computer Science, Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/8897", URL = "http://researcharchive.vuw.ac.nz/xmlui/handle/10063/8897?show=full", URL = "http://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/8897/thesis_access.pdf", size = "267 pages", abstract = "The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection (SOD) aims to simulate the capability of the human visual system in prioritizing objects for high-level processing. SOD is a process of identifying and localizing the most attention grabbing object(s) of a scene and separating the whole extent of the object(s) from the scene. In SOD, significant research has been dedicated to design and introduce new features to the domain. The existing saliency feature space suffers from some difficulties such as having high dimensionality, features are not equally important, some features are irrelevant, and the original features are not informative enough. These difficulties can lead to various performance limitations. Feature manipulation is the process which improves the input feature space to enhance the learning quality and performance. Evolutionary computation (EC) techniques have been employed in a wide range of tasks due to their powerful search abilities. Genetic programming (GP) and particle swarm optimization (PSO) are well-known EC techniques which have been used for feature manipulation. The overall goal of this thesis is to develop feature manipulation methods including feature weighting, feature selection, and feature construction using EC techniques to improve the input feature set for SOD. This thesis proposes a feature weighting method using PSO to explore the relative contribution of each saliency feature in the feature combination process. Saliency features are referred to the features which are extracted from different levels (e.g., pixel, segmentation) of an image to compute the saliency values over the entire image. The experimental results show that different datasets favour different weights for the employed features. The results also reveal that by considering the importance of each feature in the combination process, the proposed method has achieved better performance than that of the competitive methods. This thesis proposes a new bottom-up SOD method to detect salient objects by constructing two new informative saliency features and designing a new feature combination framework. The proposed method aims at developing features which target to identify different regions of the image. The proposed method makes a good balance between computational time and performance. This thesis proposes a GP-based method to automatically construct foreground and background saliency features. The automatically constructed features do not require domain-knowledge and they are more informative compared to the manually constructed features. The results show that GP is robust towards the changes in the input feature set (e.g., adding more features to the input feature set) and improves the performance by introducing more informative features to the SOD domain. This thesis proposes a GP-based SOD method which automatically produces saliency maps (a 2-D map containing saliency values) for different types of images. This GP-based SOD method applies feature selection and feature combination during the learning process for SOD. GP with built-in feature selection process which selects informative features from the original set and combines the selected features to produce the final saliency map. The results show that GP can potentially explore a large search space and find a good way to combine different input features. This thesis introduces GP for the first time to construct high-level saliency features from the low-level features for SOD, which aims to improve the performance of SOD, particularly on challenging and complex SOD tasks. The proposed method constructs fewer features that achieve better saliency performance than the original full feature set.", notes = "Supervisors: Bing Xue, Mengjie Zhang, Christopher Hollitt, Harith Al-Sahaf", } @Article{Afzali:2021:ESA, author = "Shima {Afzali Vahed Moghaddam} and Harith Al-Sahaf and Bing Xue and Christopher Hollitt and Mengjie Zhang", title = "An automatic feature construction method for salient object detection: A genetic programming approach", journal = "Expert Systems with Applications", volume = "186", pages = "115726", year = "2021", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2021.115726", URL = "https://www.sciencedirect.com/science/article/pii/S0957417421011076", keywords = "genetic algorithms, genetic programming, Salient object detection, Feature construction", abstract = "Over the last two decades, salient object detection (SOD) has received increasingly more attention due to its ability to handle complex natural scenes and its various real-world applications. The performance of an SOD method mainly relies on saliency features that are extracted with different levels of information. Low-level saliency features are often effective in simple scenarios, but they are not always robust in challenging scenarios. With the recent prevalence of high-level saliency features such as deep convolutional neural networks (CNNs) features, a remarkable progress has been achieved in the SOD field. However, CNN-based constructed high-level features unavoidably drop the location information and low-level fine details (e.g., edges and corners) of salient object(s), leading to unclear/blurry boundary predictions. In addition, deep CNN methods have difficulties to generalize and accurately detect salient objects when they are trained with limited number of images (e.g. small datasets). This paper proposes a new automatic feature construction method using Genetic Programming (GP) to construct informative high-level saliency features for SOD. The proposed method takes low-level and hand-crafted saliency features as input to construct high-level features. The constructed GP-based high-level features not only detect the general objects, but they are also good at capturing details and edges/boundaries. The GP-based constructed features have better interpretability compared to CNN-based features. The proposed GP-based method can potentially cope with a small number of samples for training to obtain a good generalization as long as the given training data has enough information to represent the distribution of the data. The experiments on six datasets reveal that the new method achieves consistently high performance compared to twelve state-of-the-art SOD methods", } @InProceedings{agapie:1999:RSCC, author = "Alexandru Agapie", title = "Random Systems with Complete Connections", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "770", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-862.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{eurogp06:AgapitosLucas, author = "Alexandros Agapitos and Simon M. Lucas", title = "Learning Recursive Functions with Object Oriented Genetic Programming", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10 - 12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "166--177", DOI = "doi:10.1007/11729976_15", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This paper describes the evolution of recursive functions within an Object-Oriented Genetic Programming (OOGP) system. We evolved general solutions to factorial, Fibonacci, exponentiation, even-n-Parity, and nth-3. We report the computational effort required to evolve these methods and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the evolutionary algorithms performed much better than undirected random search, and that mutation outperformed crossover on most problems.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Java reflection.", } @InProceedings{Agapitos:2006:CEC, author = "Alexandros Agapitos and Simon M. Lucas", title = "Evolving Efficient Recursive Sorting Algorithms", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "9227--9234", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computational complexity, evolutionary computation, object-oriented languages, object-oriented programming, OOGP, evolutionary process, fitness function, language primitives, object oriented genetic programming, recursive sorting algorithms, time complexity", ISBN = "0-7803-9487-9", URL = "http://privatewww.essex.ac.uk/~aagapi/papers/AgapitosLucasEvolvingSort.pdf", DOI = "doi:10.1109/CEC.2006.1688643", size = "8 pages", abstract = "Object Oriented Genetic Programming (OOGP) is applied to the task of evolving general recursive sorting algorithms. We studied the effects of language primitives and fitness functions on the success of the evolutionary process. For language primitives, these were the methods of a simple list processing package. Five different fitness functions based on sequence disorder were evaluated. The time complexity of the successfully evolved algorithms was measured experimentally in terms of the number of method invocations made, and for the best evolved individuals this was best approximated as O(n log(n)). This is the first time that sorting algorithms of this complexity have been evolved.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. Best in session. IEEE Xplore gives pages as 2677--2684", } @InProceedings{eurogp07:agapitos1, author = "Alexandros Agapitos and Simon M. Lucas", title = "Evolving a Statistics Class Using Object Oriented Evolutionary Programming", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "291--300", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71602-0", ISBN = "3-540-71602-5", DOI = "doi:10.1007/978-3-540-71605-1_27", abstract = "Object Oriented Evolutionary Programming is used to evolve programs that calculate some statistical measures on a set of numbers. We compared this technique with a more standard functional representation. We also studied the effects of scalar and Pareto-based multi-objective fitness functions to the induction of multi-task programs. We found that the induction of a program residing in an OO representation space is more efficient, yielding less fitness evaluations, and that scalar fitness performed better than Pareto-based fitness in this problem domain.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{eurogp07:agapitos2, author = "Alexandros Agapitos and Simon M. Lucas", title = "Evolving Modular Recursive Sorting Algorithms", editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar", booktitle = "Proceedings of the 10th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "4445", year = "2007", address = "Valencia, Spain", month = "11-13 " # apr, pages = "301--310", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71602-0", ISBN = "3-540-71602-5", DOI = "doi:10.1007/978-3-540-71605-1_28", abstract = "A fundamental issue in evolutionary learning is the definition of the solution representation language. We present the application of Object Oriented Genetic Programming to the task of coevolving general recursive sorting algorithms along with their primitive representation alphabet. We report the computational effort required to evolve target solutions and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the induction of evolved method signatures (typed parameters and return type) can be realized through an evolutionary fitness-driven process. We also found that the evolutionary algorithm outperformed undirected random search, and that mutation performed better than crossover in this problem domain. The main result is that modular sorting algorithms can be evolved.", notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007", } @InProceedings{1277271, author = "Alexandros Agapitos and Julian Togelius and Simon Mark Lucas", title = "Evolving controllers for simulated car racing using object oriented genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1543--1550", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1543.pdf", DOI = "doi:10.1145/1276958.1277271", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, evolutionary computer games, evolutionary robotics, homologous uniform crossover, neural networks, object oriented, subtree macro-mutation", abstract = "The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively Co-evolves a population of adaptive mappings and associated genotypes is used to learn recursive solutions given a function set consisting of general (not implicitly recursive) machine-language instructions. PAM DGP using redundant encodings to model the evolution of the biological genetic code is found to more efficiently learn 2nd and 3rd order recursive Fibonacci functions than related developmental systems and traditional linear GP. PAM DGP using redundant encoding is also demonstrated to produce the semantically highest quality solutions for all three recursive functions considered (Factorial, 2nd and 3rd order Fibonacci). PAM DGP is then shown to have produced such solutions by evolving redundant mappings to select and emphasise appropriate subsets of the function set useful for producing the naturally recursive solutions.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Agapitos:2007:cec, title = "Multiobjective Techniques for the Use of State in Genetic Programming Applied to Simulated Car Racing", author = "Alexandros Agapitos and Julian Togelius and Simon M. Lucas", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1562--1569", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1977.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424659", abstract = "Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different metrics for measuring the use of state within a program are introduced. Comparisons are performed among multi- and single-objective fitness functions with respect to learning speed and final fitness of evolved individuals, and attempts are made at understanding whether there is a trade-off between good performance and stateful controllers in this problem domain.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{Agapitos:2008:gecco, author = "Alexandros Agapitos and Matthew Dyson and Simon M. Lucas and Francisco Sepulveda", title = "Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1155--1162", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1155.pdf", DOI = "doi:10.1145/1389095.1389326", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Brain computer interface, classification on Raw signal, stateful representation, statistical signal primitives", size = "8 pages", abstract = "Two families (stateful and stateless) of genetically programmed classifiers were tested on a five class brain-computer interface (BCI) data set of raw EEG signals. The ability of evolved classifiers to discriminate mental tasks from each other were analysed in terms of accuracy, precision and recall. A model describing the dynamics of state usage in stateful programs is introduced. An investigation of relationships between the model attributes and associated classification results was made. The results show that both stateful and stateless programs can be successfully evolved for this task, though stateful programs start from lower fitness and take longer to evolve", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389326}", } @InProceedings{Agapitos2:2008:gecco, author = "Alexandros Agapitos and Matthew Dyson and Jenya Kovalchuk and Simon Mark Lucas", title = "On the genetic programming of time-series predictors for supply chain management", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1163--1170", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1163.pdf", URL = "http://privatewww.essex.ac.uk/~yvkova/Papers/GP_GECCO08.pdf", DOI = "doi:10.1145/1389095.1389327", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Iterated single-step prediction, prediction/forecasting, single-step prediction, statistical time-series Features", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389327}", } @InProceedings{Agapitos:2008:CIG, author = "Alexandros Agapitos and Julian Togelius and Simon M. Lucas and Jurgen Schmidhuber and Andreas Konstantinidis", title = "Generating Diverse Opponents with Multiobjective Evolution", booktitle = "Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games", year = "2008", pages = "135--142", address = "Perth, Australia", month = dec # " 15-18", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, EMOA, Car Racing, MOGA, AI game agent, computational intelligence, diverse opponent generation, game play learning, multiobjective evolutionary algorithm, nonplayer character, computer games, evolutionary computation, learning (artificial intelligence), multi-agent systems", URL = "http://julian.togelius.com/Agapitos2008Generating.pdf", DOI = "doi:10.1109/CIG.2008.5035632", abstract = "For computational intelligence to be useful in creating game agent AI, we need to focus on creating interesting and believable agents rather than just learn to play the games well. To this end, we propose a way to use multiobjective evolutionary algorithms to automatically create populations of NPCs, such as opponents and collaborators, that are interestingly diverse in behaviour space. Experiments are presented where a number of partially conflicting objectives are defined for racing game competitors, and multiobjective evolution of GP-based controllers yield Pareto fronts of interesting controllers.", notes = "Also known as \cite{5035632}", } @InProceedings{agapitos_etal:ppsn2010, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Evolutionary Learning of Technical Trading Rules without Data-mining Bias", booktitle = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", pages = "294--303", year = "2010", volume = "6238", editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph", publisher = "Springer", series = "Lecture Notes in Computer Science", isbn13 = "978-3-642-15843-8", address = "Krakow, Poland", month = "11-15 " # sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-15844-5_30", abstract = "In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule's statistical significance using Hansen's Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness function, as compared to a single-criterion fitness measure that considers solely the average return.", } @InProceedings{Agapitos:2010:AIAI, author = "Alexandros Agapitos and Andreas Konstantinidis and Haris Haralambous and Harris Papadopoulos", title = "Evolutionary Prediction of Total Electron Content over Cyprus", booktitle = "6th IFIP Advances in Information and Communication Technology AIAI 2010", year = "2010", editor = "Harris Papadopoulos and Andreas Andreou and Max Bramer", volume = "339", series = "IFIP Advances in Information and Communication Technology", pages = "387--394", address = "Larnaca, Cyprus", month = oct # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Global Positioning System, Total Electron Content", DOI = "doi:10.1007/978-3-642-16239-8_50", abstract = "Total Electron Content (TEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on trans-ionospheric links and subsequently overwhelm its negative impact in accurate position determination. In this paper, an Evolutionary Algorithm (EA), and particularly a Genetic Programming (GP) based model is designed. The proposed model is based on the main factors that influence the variability of the predicted parameter on a diurnal, seasonal and long-term time-scale. Experimental results show that the GP-model, which is based on TEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. The GP-based approach performs better than the existing Neural Network-based approach in several cases.", affiliation = "School of Computer Science and Informatics, University College Dublin, Dublin, Ireland", notes = "http://www.cs.ucy.ac.cy/aiai2010/", } @InProceedings{agapitosetal:2010:cfe, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Promoting the generalisation of genetically induced trading rules", booktitle = "Proceedings of the 4th International Conference on Computational and Financial Econometrics CFE'10", year = "2010", editor = "G. Kapetanios and O. Linton and M. McAleer and E. Ruiz", pages = "E678", address = "Senate House, University of London, UK", month = "10-12 " # dec, organisation = "CSDA, LSE, Queen Mary and Westerfield College", publisher = "ERCIM", keywords = "genetic algorithms, genetic programming", URL = "http://www.cfe-csda.org/cfe10/LondonBoA.pdf", size = "Abstracts only", abstract = "The goal of Machine Learning is not to induce an exact representation of the training patterns themselves, but rather to build a model of the underlying pattern-generation process. One of the most important aspects of this computational process is how to obtain general models that are representative of the true concept, and as a result, perform efficiently when presented with novel patterns from that concept. A particular form of evolutionary machine learning, Genetic Programming, tackles learning problems by means of an evolutionary process of program discovery. In this paper we investigate the profitability of evolved technical trading rules when accounting for the problem of over-fitting. Out-of-sample rule performance deterioration is a well-known problem, and has been mainly attributed to the tendency of the evolved models to find meaningless regularities in the training dataset due to the high dimensionality of features and the rich hypothesis space. We present a review of the major established methods for promoting generalisation in conventional machine learning paradigms. Then, we report empirical results of adapting such techniques to the Genetic Programming methodology, and applying it to discover trading rules for various financial datasets.", notes = "http://www.cfe-csda.org/cfe10/", } @InProceedings{agapitos:2011:EuroGP, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon and Theodoros Theodoridis", title = "Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "61--72", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_6", abstract = "Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space, which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other learning algorithms.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Agapitos:2011:GECCOcomp, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Stateful program representations for evolving technical trading rules", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "199--200", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2001969", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A family of stateful program representations in grammar-based Genetic Programming are being compared against their stateless counterpart in the problem of binary classification of sequences of daily prices of a financial asset. Empirical results suggest that stateful classifiers learn as fast as stateless ones but generalise better to unseen data, rendering this form of program representation strongly appealing to the automatic programming of technical trading rules.", notes = "Also known as \cite{2001969} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Agapitos:2011:CIG, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon and Theodoros Theodoridis", title = "Learning Environment Models in Car Racing Using Stateful Genetic Programming", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", address = "Seoul, South Korea", pages = "219--226", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, Car Racing, AI game agent, computational intelligence, diverse opponent generation, game play learning, nonplayer character, computer games, evolutionary computation, learning (artificial intelligence), multi-agent systems, 2D data structures, artificial agents, car racing games, learning environment models, model building behaviour, modular programs, non player characters, cognition, computer games, data structures, learning (artificial intelligence), multi-agent systems", isbn13 = "978-1-4577-0010-1", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf", DOI = "doi:10.1109/CIG.2011.6032010", size = "8 pages", abstract = "For computational intelligence to be useful in creating game agent AI we need to focus on methods that allow the creation and maintenance of models for the environment, which the artificial agents inhabit. Maintaining a model allows an agent to plan its actions more effectively by combining immediate sensory information along with a memories that have been acquired while operating in that environment. To this end, we propose a way to build environment models for non-player characters in car racing games using stateful Genetic Programming. A method is presented, where general-purpose 2-dimensional data-structures are used to build a model of the racing track. Results demonstrate that model-building behaviour can be cooperatively coevolved with car-controlling behaviour in modular programs that make use of these models in order to navigate successfully around a racing track.", notes = "Indexed memory. Also known as \cite{6032010}", } @InCollection{Agapitos:NCFE:2011, author = "Alexandros Agapitos and Abhinav Goyal and Cal Muckley", title = "An Evolutionary Algorithmic Investigation of {US} Corporate Payout Policy", booktitle = "Natural Computing in Computational Finance (Volume 4)", publisher = "Springer", year = "2012", editor = "Anthony Brabazon and Michael O'Neill and Dietmar Maringer", volume = "380", series = "Studies in Computational Intelligence", chapter = "7", pages = "123--139", keywords = "genetic algorithms, genetic programming, US Corporate Payout Policy, Symbolic Regression", isbn13 = "978-3-642-23335-7", URL = "http://hdl.handle.net/10197/3552", URL = "https://researchrepository.ucd.ie/bitstream/10197/3552/1/gp_bookchapter.pdf", URL = "http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23335-7", DOI = "doi:10.1007/978-3-642-23336-4_7", abstract = "This Chapter examines cash dividends and share repurchases in the United States during the period 1990 to 2008. In the extant literature a variety of classical statistical methodologies have been adopted, foremost among these is the method of panel regression modelling. Instead, in this Chapter, we have informed our model specifications and our coefficient estimates using a genetic program. Our model captures effects from a wide range of pertinent proxy variables related to the agency cost-based life cycle theory, the signalling theory and the catering theory of corporate payout policy determination. In line with the extant literature, our findings indicate the predominant importance of the agency-cost based life cycle theory. The adopted evolutionary algorithm approach also provides important new insights concerning the influence of firm size, the concentration of firm ownership and cash flow uncertainty with respect to corporate payout policy determination in the United States.", } @InProceedings{agapitos:evoapps12, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Derivatives", booktitle = "Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC", year = "2011", month = "11-13 " # apr, editor = "Cecilia {Di Chio} and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and Gianni A. {Di Caro} and Rolf Drechsler and Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis", series = "LNCS", volume = "7248", publisher = "Springer Verlag", address = "Malaga, Spain", publisher_address = "Berlin", pages = "135--144", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-29177-7", DOI = "doi:10.1007/978-3-642-29178-4_14", size = "10 pages", abstract = "In this study we evolve seasonal forecasting temperature models, using Genetic Programming (GP), in order to provide an accurate, localised, long-term forecast of a temperature profile as part of the broader process of determining appropriate pricing model for weather-derivatives, financial instruments that allow organisations to protect themselves against the commercial risks posed by weather fluctuations. Two different approaches for time-series modelling are adopted. The first is based on a simple system identification approach whereby the temporal index of the time-series is used as the sole regressor of the evolved model. The second is based on iterated single-step prediction that resembles autoregressive and moving average models in statistical time-series modelling. Empirical results suggest that GP is able to successfully induce seasonal forecasting models, and that autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three datasets investigated.", notes = "EvoFIN Part of \cite{DiChio:2012:EvoApps} EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012", affiliation = "Financial Mathematics and Computation Research Cluster Natural Computing Research and Applications Group Complex and Adaptive Systems Laboratory, University College Dublin, Ireland", } @InCollection{Agapitos:FDMCI:2012, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather Derivatives", booktitle = "Financial Decision Making Using Computational Intelligence", publisher = "Springer", year = "2012", editor = "Doumpos Michael and Zopounidis Constantin and Pardalos Panos", volume = "70", series = "Springer Optimization and Its Applications", chapter = "6", pages = "153--182", note = "Due: July 31, 2012", keywords = "genetic algorithms, genetic programming, Weather derivatives pricing, Seasonal temperature forecasting, Autoregressive models, Supervised ensemble learning, Generalisation", isbn13 = "978-1-4614-3772-7", URL = "http://www.springer.com/mathematics/applications/book/978-1-4614-3772-7", } @InProceedings{conf/ppsn/Agapitos12, author = "Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition", booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)", year = "2012", editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone", volume = "7491", series = "Lecture Notes in Computer Science", pages = "438--447", address = "Taormina, Italy", month = sep # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-32936-4", DOI = "doi:10.1007/978-3-642-32937-1_44", size = "10 pages", abstract = "We consider the fundamental property of generalisation of data-driven models evolved by means of Genetic Programming (GP). The statistical treatment of decomposing the regression error into bias and variance terms provides insight into the generalisation capability of this modelling method. The error decomposition is used as a source of inspiration to design a fitness function that relaxes the sensitivity of an evolved model to a particular training dataset. Results on eight symbolic regression problems show that new method is capable on inducing better-generalising models than standard GP for most of the problems.", affiliation = "Natural Computing Research and Applications Group, University College Dublin, Ireland", } @InProceedings{agapitos:2013:EuroGP, author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", title = "Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming", booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013", year = "2013", month = "3-5 " # apr, editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu", series = "LNCS", volume = "7831", publisher = "Springer Verlag", address = "Vienna, Austria", pages = "1--12", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37206-3", DOI = "doi:10.1007/978-3-642-37207-0_1", abstract = "Nearest Neighbour (NN) classification is a widely-used, effective method for both binary and multi-class problems. It relies on the assumption that class conditional probabilities are locally constant. However, this assumption becomes invalid in high dimensions, and severe bias can be introduced, which degrades the performance of the method. The employment of a locally adaptive distance metric becomes crucial in order to keep class conditional probabilities approximately uniform, whereby better classification performance can be attained. This paper presents a locally adaptive distance metric for NN classification based on a supervised learning algorithm (Genetic Programming) that learns a vector of feature weights for the features composing an instance query. Using a weighted Euclidean distance metric, this has the effect of adaptive neighbourhood shapes to query locations, stretching the neighbourhood along the directions for which the class conditional probabilities don't change much. Initial empirical results on a set of real-world classification datasets showed that the proposed method enhances the generalisation performance of standard NN algorithm, and that it is a competent method for pattern classification as compared to other learning algorithms.", notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013", } @InProceedings{agapitos:2014:EuroGP, author = "Alexandros Agapitos and James McDermott and Michael O'Neill and Ahmed Kattan and Anthony Brabazon", title = "Higher Order Functions for Kernel Regression", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "1--12", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_1", abstract = "Kernel regression is a well-established nonparametric method, in which the target value of a query point is estimated using a weighted average of the surrounding training examples. The weights are typically obtained by applying a distance-based kernel function, which presupposes the existence of a distance measure. This paper investigates the use of Genetic Programming for the evolution of task-specific distance measures as an alternative to Euclidean distance. Results on seven real-world datasets show that the generalisation performance of the proposed system is superior to that of Euclidean-based kernel regression and standard GP.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Agapitos:2014:CEC, title = "Ensemble {Bayesian} Model Averaging in Genetic Programming", author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon", pages = "2451--2458", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, Genetic programming, Data mining, Classification, clustering and data analysis", DOI = "doi:10.1109/CEC.2014.6900567", abstract = "This paper considers the general problem of function estimation via Genetic Programming (GP). Data analysts typically select a model from a population of models, and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and lack of generalisation. We adopt a coherent method for accounting for this uncertainty through a weighted averaging of all models competing in a population of GP. It is a principled statistical method for post-processing a population of programs into an ensemble, which is based on Bayesian Model Averaging (BMA). Under two different formulations of BMA, the predictive probability density function (PDF) of a response variable is a weighted average of PDFs centred around the individual predictions of component models that take the form of either standalone programs or ensembles of programs. The weights are equal to the posterior probabilities of the models generating the predictions, and reflect the models' skill on the training dataset. The method was applied to a number of synthetic symbolic regression problems, and results demonstrate that it generalises better than standard methods for model selection, as well as methods for ensemble construction in GP.", notes = "WCCI2014", } @InProceedings{agapitos:cec2015, author = "Alexandros Agapitos and Michael O'Neill and Miguel Nicolau and David Fagan and Ahmed Kattan and Kathleen Curran", title = "Deep Evolution of Feature Representations for Handwritten Digit Recognition", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", editor = "Yadahiko Murata", pages = "2452--2459", year = "2015", address = "Sendai, Japan", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2015.7257189", abstract = "A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.", notes = "CEC2015", } @InProceedings{EvoBafin16Agapitosetal, author = "Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Genetic Programming with Memory For Financial Trading", booktitle = "19th European Conference on the Applications of Evolutionary Computation", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", series = "Lecture Notes in Computer Science", volume = "9597", pages = "19--34", address = "Porto, Portugal", month = mar # " 30 - " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-31204-0_2", DOI = "doi:10.1007/978-3-319-31204-0_2", abstract = "A memory-enabled program representation in strongly-typed Genetic Programming (GP) is compared against the standard representation in a number of financial time-series modelling tasks. The paper first presents a survey of GP systems that use memory. Thereafter, a number of simulations show that memory-enabled programs generalise better than their standard counterparts in most datasets of this problem domain.", notes = "EvoApplications2016 held in conjunction with EuroGP'2016, EvoCOP2016 and EvoMusArt2016", } @Article{Agapitos:2016:GPEM, author = "Alexandros Agapitos and Michael O'Neill and Ahmed Kattan and Simon M. Lucas", title = "Recursion in tree-based genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "2", pages = "149--183", month = jun, keywords = "genetic algorithms, genetic programming, Evolutionary program synthesis Recursive programs, Variation operators, Fitness landscape analysis", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9277-5", size = "35 pages", abstract = "Recursion is a powerful concept that enables a solution to a problem to be expressed as a relatively simple decomposition of the original problem into sub-problems of the same type. We survey previous research about the evolution of recursive programs in tree-based Genetic Programming. We then present an analysis of the fitness landscape of recursive programs, and report results on evolving solutions to a range of problems. We conclude with guidelines concerning the choice of fitness function and variation operators, as well as the handling of the halting problem. The main findings are as follows. The distribution of fitness changes initially as we look at programs of increasing size but once some threshold has been exceeded, it shows very little variation with size. Furthermore, the proportion of halting programs decreases as size increases. Recursive programs exhibit the property of weak causality; small changes in program structure may cause big changes in semantics. Nevertheless, the evolution of recursive programs is not a needle-in-a-haystack problem; the neighbourhoods of optimal programs are populated by halting individuals of intermediate fitness. Finally, mutation-based variation operators performed the best in finding recursive solutions. Evolution was also shown to outperform random search.", notes = "Factorial, Fibonacci, Exponentiation, Even-n-parity, Nth ftp://ftp.cs.ucl.ac.uk/genetic/gp-code/rand_tree.cc Random walks and error-distance correlation. Canberra distance. (hard) limit of 10000 recursive calls. '..the distribution of error is roughly independent of size' BUT '..Even-n-parity and Nth in Fig. 4d,e do not show a convergence..' 'Overall, our findings are in accordance with simulation results published in \cite{langdon:2006:eurogp}'. 'Fig. 4 Proportion of halting programs (out of 2,000,000 programs) as a function of program size' '..once programs containing recursive nodes wither away from the population, it is impossible to be introduced again.'", } @Article{Agapitos:2018:CMS, author = "Alexandros Agapitos and Anthony Brabazon and Michael O'Neill", title = "Regularised Gradient Boosting for Financial Time-series Modelling", journal = "Computational Management Science", year = "2017", volume = "14", number = "3", pages = "367--391", month = jul, keywords = "genetic algorithms, genetic programming, Boosting algorithms, Gradient boosting, Stagewise additive modelling, Regularisation, Financial time-series modelling, Financial forecasting, Feedforward neural networks, ANN, Noisy data, Ensemble learning", URL = "https://ideas.repec.org/a/spr/comgts/v14y2017i3d10.1007_s10287-017-0280-y.html", DOI = "doi:10.1007/s10287-017-0280-y", abstract = "Gradient Boosting (GB) learns an additive expansion of simple basis-models. This is accomplished by iteratively fitting an elementary model to the negative gradient of a loss function with respect to the expansion's values at each training data-point evaluated at each iteration. For the case of squared-error loss function, the negative gradient takes the form of an ordinary residual for a given training data-point. Studies have demonstrated that running GB for hundreds of iterations can lead to overfitting, while a number of authors showed that by adding noise to the training data, generalisation is impaired even with relatively few basis-models. Regularisation is realised through the shrinkage of every newly-added basis-model to the expansion. This paper demonstrates that GB with shrinkage-based regularisation is still prone to overfitting in noisy datasets. We use a transformation based on a sigmoidal function for reducing the influence of extreme values in the residuals of a GB iteration without removing them from the training set. This extension is built on top of shrinkage-based regularisation. Simulations using synthetic, noisy data show that the proposed method slows-down overfitting and reduces the generalisation error of regularised GB. The proposed method is then applied to the inherently noisy domain of financial time-series modelling. Results suggest that for the majority of datasets the method generalises better when compared against standard regularised GB, as well as against a range of other time-series modelling methods.", notes = "School of Computer Science, University College Dublin, Dublin, Ireland", } @Article{Agapitos:ieeeTEC, author = "Alexandros Agapitos and Roisin Loughran and Miguel Nicolau and Simon Lucas and Michael O'Neill and Anthony Brabazon", title = "A Survey of Statistical Machine Learning Elements in Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2019", volume = "23", number = "6", pages = "1029--1048", month = dec, keywords = "genetic algorithms, genetic programming, Statistical Machine Learning, SML, Generalisation, Overfitting, Classification, Symbolic Regression, Model selection, Regularisation, Model Averaging, Bias-Variance trade-off", ISSN = "1089-778X", URL = "http://ncra.ucd.ie/papers/08648159.pdf", DOI = "doi:10.1109/TEVC.2019.2900916", size = "20 pages", abstract = "Modern Genetic Programming operates within the Statistical Machine Learning framework. In this framework evolution needs to balance between approximation of an unknown target function on the training data and generalisation, which is the ability to predict well on new data. The article provides a survey and critical discussion of Statistical Machine Learning methods that enable Genetic Programming to generalise.", notes = "also known as \cite{8648159} School of Computer Science, University College Dublin, Ireland", } @InProceedings{agapow:1996:cbecv, author = "Paul-Michael Agapow", title = "Computational Brittleness and the Evolution of Computer Viruses", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "2--11", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "genetic algorithms, genetic programming", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_964", size = "10 pages", abstract = "In recent years computer viruses have grown to be of great concern. They have also been proposed as prototypical artificial life, but the possibility of their evolution has been dismissed due to modern computer programs being computationally brittle (i.e. a random change to a functional program will almost certainly render it non-functional) and the series of steps required for the evolution of a new virus being improbable. These allegations are examined by studying homology between functional program sequences. It is concluded that programs are far less brittle than expected. While the evolution of viruses de novo is still unlikely, evolution of pre-existing viruses and programs is feasible. This has significant implications for computer security and evolutionary computation.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 (UNIX Sun RISC) {"}programs are far less brittle than expected{"}.", affiliation = "La Trobe University Computer Science V. 3083 Melbourne Australia V. 3083 Melbourne Australia", } @InCollection{agarwal:2000:GPWPPAPE, author = "Ashish Agarwal", title = "Genetic Programming for Wafer Property Prediction After Plasma Enhanced", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "16--24", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{agarwal:2023:SDECGE, author = "Ekansh Agarwal and Ajeet Kumar Verma and Anindya Pain and Shantanu Sarkar", title = "Probabilistic Analysis of a Geosynthetic Reinforced Soil Retaining Wall Under Seismic Conditions Using Genetic Programming", booktitle = "Soil Dynamics, Earthquake and Computational Geotechnical Engineering", year = "2023", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-19-6998-0_20", DOI = "doi:10.1007/978-981-19-6998-0_20", } @Article{Aggarwal:2011:ijcse, author = "Khushboo Aggarwal and Sunil Kumar Singh and Sakar Khattar", title = "A high Performance Algorithm for Solving large scale Travelling Salesman Problem using Distributed Memory Architectures", journal = "Indian Journal of Computer Science and Engineering", year = "2011", volume = "2", number = "4", pages = "516--521", month = aug # "-" # sep, keywords = "genetic algorithms, genetic programming, TSP, traveling salesman problem, fitness functions", ISSN = "2231-3850", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.300.6369", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.6369", URL = "http://www.ijcse.com/docs/INDJCSE11-02-04-175.pdf", size = "6 pages", abstract = "In this paper, we present an intelligent solution system for travelling salesman problem. The solution has three stages. The first stage uses Clustering Analysis in Data Mining to classify all customers by a number of attributes, such as distance, demand level, the density of customer, and city layout. The second stage introduces how to generate feasible routing schemes for each vehicle type. Specifically, a depth-first search algorithm with control rules is presented to generate feasible routing schemes. In the last stage, a genetic programming model is applied to find the best possible solution. Finally, we present a paradigm for using this algorithm for distributed memory architectures to gain the benefits of parallel processing.", } @Misc{Aggarwal:intern, author = "Varun Aggarwal", title = "Prediction of Protein Secondary Structure using Genetic Programming", howpublished = "Summer Internship Project Report During June-July 2003", year = "2003", keywords = "genetic algorithms, genetic programming", URL = "http://web.mit.edu/varun_ag/www/psspreport.pdf", size = "23 pages", abstract = "Project 1:Using SOM and Genetic Programming to predict Protein Secondary structure Project 2: Improving PSIPRED Predictions using Genetic Programming", notes = "Under: Dr. Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden", } @InProceedings{maccallum:2004:eurogp, author = "Varun Aggarwal and Robert MacCallum", title = "Evolved Matrix Operations for Post-Processing Protein Secondary Structure Predictions", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "220--229", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", URL = "http://web.mit.edu/varun_ag/www/aggarwal-eurogp2004.pdf", DOI = "doi:10.1007/978-3-540-24650-3_20", abstract = "Predicting the three-dimensional structure of proteins is a hard problem, so many have opted instead to predict the secondary structural state (usually helix, strand or coil) of each amino acid residue. This should be an easier task, but it now seems that a ceiling of around 76 percent per-residue three-state accuracy has been reached. Further improvements will require the correct processing of so-called {"}long-range information{"}. We present a novel application of genetic programming to evolve high level matrix operations to post-process secondary structure prediction probabilities produced by the popular, state-of-the-art neural network based PSIPRED by David Jones. We show that global and long-range information may be used to increase three-state accuracy by at least 0.26 percentage points - a small but statistically significant difference. This is on top of the 0.14 percentage point increase already made by PSIPRED's built-in filters.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InCollection{Aggarwal:2006:GPTP, author = "Varun Aggarwal and Una-May O'Reilly", title = "Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "219--236", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, circuit sizing, symbolic regression, posynomial models, geometric programming", ISBN = "0-387-33375-4", URL = "http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf", DOI = "doi:10.1007/978-0-387-49650-4_14", size = "19 pages", abstract = "Starting from a broad description of analogue circuit design in terms of topology design and sizing, we discuss the difficulties of sizing and describe approaches that are manual or automatic. These approaches make use of blackbox optimisation techniques such as evolutionary algorithms or convex optimization techniques such as geometric programming. Geometric programming requires posynomial expressions for a circuit's performance measurements. We show how a genetic algorithm can be exploited to evolve a polynomial expression (i.e. model) of transistor (i.e. mosfet) behaviour more accurately than statistical techniques in the literature.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop", } @Article{Aghbashlo:2016:Energy, author = "Mortaza Aghbashlo and Shahaboddin Shamshirband and Meisam Tabatabaei and Por Lip Yee and Yaser Nabavi Larimi", title = "The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a {DI} diesel engine running on diesel/biodiesel blends containing polymer waste", journal = "Energy", volume = "94", pages = "443--456", year = "2016", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.11.008", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215015327", abstract = "In this study, a novel method based on Extreme Learning Machine with wavelet transform algorithm (ELM-WT) was designed and adapted to estimate the exergetic performance of a DI diesel engine. The exergetic information was obtained by calculating mass, energy, and exergy balance equations for the experimental trials conducted at various engine speeds and loads as well as different biodiesel and expanded polystyrene contents. Furthermore, estimation capability of the ELM-WT model was compared with that of the ELM, GP (genetic programming) and ANN (artificial neural network) models. The experimental results showed that an improvement in the exergetic performance modelling of the DI diesel engine could be achieved by the ELM-WT approach in comparison with the ELM, GP, and ANN methods. Furthermore, the results showed that the applied algorithm could learn thousands of times faster than the conventional popular learning algorithms. Obviously, the developed ELM-WT model could be used with a high degree of confidence for further work on formulating novel model predictive strategy for investigating exergetic performance of DI diesel engines running on various renewable and non-renewable fuels.", keywords = "genetic algorithms, genetic programming, Biodiesel, DI diesel engine, Exergetic performance parameters, Expanded polystyrene, Cost sensitivity analysis, Extreme learning machine-wavelet (ELM-WT)", } @Article{agnelli:2002:PRL, author = "Davide Agnelli and Alessandro Bollini and Luca Lombardi", title = "Image classification: an evolutionary approach", journal = "Pattern Recognition Letters", year = "2002", number = "1-3", volume = "23", pages = "303--309", month = jan, keywords = "genetic algorithms, genetic programming, Image classification, Supervised learning", ISSN = "0167-8655", broken = "http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656", DOI = "doi:10.1016/S0167-8655(01)00128-3", size = "7 pages", abstract = "Evolutionary algorithms are proving viable in solving complex optimization problems such as those typical of supervised learning approaches to image understanding. This paper presents an evolutionary approach to image classification and discusses some experimental results, suggesting that genetic programming could provide a convenient alternative to standard supervised learning methods.", } @InProceedings{Agrawal:2023:ICSE, author = "Arpan Agrawal and Emily First and Zhanna Kaufman and Tom Reichel and Shizhuo Zhang and Timothy Zhou and Alex Sanchez-Stern and Talia Ringer and Yuriy Brun", title = "Proofster: Automated Formal Verification", booktitle = "Proceedings of the Demonstrations Track at the 45th International Conference on Software Engineering (ICSE)", year = "2023", pages = "26--30", address = "Melbourne", month = "14-20 " # may, keywords = "genetic algorithms, genetic programming", isbn13 = "979-8-3503-2264-4", DOI = "doi:10.1109/ICSE-Companion58688.2023.00018", video_url = "https://youtu.be/xQAi66lRfwI/", code_url = "https://proofster.cs.umass.edu/", size = "5 pages", abstract = "Formal verification is an effective but extremely work-intensive method of improving software quality. Verifying the correctness of software systems often requires significantly more effort than implementing them in the first place, despite the existence of proof assistants, such as Coq, aiding the process. Recent work has aimed to fully automate the synthesis of formal verification proofs, but little tool support exists for practitioners. This paper presents Proofster, a web-based tool aimed at assisting developers with the formal verification process via proof synthesis. Proofster inputs a Coq theorem specifying a property of a software system and attempts to automatically synthesize a formal proof of the correctness of that property. When it is unable to produce a proof, Proofster outputs the proof-space search tree its synthesis explored, which can guide the developer to provide a hint to enable Proofster to synthesize the proof. Proofster runs online at https://proofster.cs.umass.edu/ and a video demonstrating Proofster is available at https://youtu.be/xQAi66lRfwI/.", notes = "Demo Track", } @InProceedings{aguilar:1998:rcmmcfssdft, author = "Jose L. Aguilar and Mariela Cerrada", title = "Reliability-Centered Maintenance Methodology-Based Fuzzy Classifier System Design for Fault Tolerance", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "621", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, classifiers", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{aguilar:1999:ABGAMOP, author = "Jose Aguilar and Pablo Miranda", title = "Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "3--10", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-873.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-873.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{aguilar:1999:TGADRSLS, author = "Jesus Aguilar and Jose Riquelme and Miguel Toro", title = "Three Geometric Approaches for representing Decision Rules in a Supervised Learning System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "771", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-391.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-391.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). See also \cite{aguilar:1999:T}", } @InProceedings{aguilar:1999:T, author = "Jesus Aguilar and Jose Riquelme and Miguel Toro", title = "Three geometric approaches for representing decision rules in a supervised learning system", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "8--15", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms, data mining, supervised learning, hyper rectangles, rotated hyper rectangles, hyper ellipse", abstract = "hyperrectangles, rotated hyperrectangles and hyperellipses", notes = "GECCO-99LB", } @InProceedings{aguilar3:2001:gecco, title = "Fuzzy Classifier System and Genetic Programming on System Identification Problems", author = "Jose Aguilar and Mariela Cerrada", pages = "1245--1251", year = "2001", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)", editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke", address = "San Francisco, California, USA", publisher_address = "San Francisco, CA 94104, USA", month = "7-11 " # jul, keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-774-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d24.pdf", notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}", } @InProceedings{WSEAS_640_Aguilar, author = "Jose Aguilar and Mariela Cerrada", title = "Genetic Programming-Based Approach for System Identification Applying Genetic Programming to obtain Separation", address = "Puerto De La Cruz, Tenerife, Spain", year = "2001", booktitle = "WSES International Conferences WSEAS NNA-FSFS-EC 2001", editor = "Nikos E. Mastorakis", pages = "6401--6406", month = feb # " 11-15", organisation = "The World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Genetic Programming, Evolutionary Computation, Identification Systems", URL = "http://www.wseas.us/e-library/conferences/tenerife2001/papers/640.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.611.1267", size = "6 pages", abstract = "In this work, an approach based on Genetic Programming is proposed for the input-output systems identification problem. Laguerre's functions and the ARX method have been commonly used to solve the systems identification problem. Recently, approaches based on Artificial Neural Networks have been used to solve this problem. Genetic Programming is an Evolutionary Computation technique based on the evolution of mathematical symbols (constants, functions, variables, operators, etc.). To achieve the identification, a set of analysis trees is used to describe the different models (individuals) that our approach proposes during its execution. At the end of the evolutionary process, an input-output model of the system is proposed by our approach (it is the best individual).", notes = "paper ID number 640 http://www.wseas.us/e-library/conferences/tenerife2001/index.htm ", } @Misc{Aguilar:2004:sci, author = "J. Aguilar and J. Altamiranda", title = "A Data Mining Algorithm Based on the Genetic Programming", year = "2004", keywords = "genetic algorithms, genetic programming, Data Mining, Clustering", size = "10 pages", abstract = "Data Mining is composed by a set of methods to extract knowledgement from large database. One of these methods is Genetic Programming. In this work we use this method to build a Data Mining System that define a set of patterns in order to classify the data. We define a grammar, which is used by the Genetic Programming in order to define the rules that represent the patterns. In this way, we can group the data in class and simplify the information in the database according to the set of patterns.", notes = " J. Aguilar Universidad de los Andes, Facultad de Ingenieria, Departamento de Computacion, CEMISID, Merida, Venezuela, 5101 J. Altamiranda Universidad de los Andes, Facultad de Ingenieria, Postgrado de Computacion, CEMISID, Merida, Venezuela, 5101 ", } @InProceedings{Aguilar:DEU:cec2006, author = "Jose Aguilar and Gilberto Gonzalez", title = "Data Extrapolation Using Genetic Programming to Matrices Singular Values Estimation", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson", pages = "3227--3230", address = "Vancouver, BC, Canada", month = "16-21 " # jul, publisher = "IEEE Press", ISBN = "0-7803-9487-9", URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11108", DOI = "doi:10.1109/CEC.2006.1688718", keywords = "genetic algorithms, genetic programming", abstract = "In mathematical models where the dimensions of the matrices are very large, the use of classical methods to compute the singular values is very time consuming and requires a lot of computational resources. In this way, it is necessary to find new faster methods to compute the singular values of a very large matrix. We present a method to estimate the singular values of a matrix based on Genetic Programming (GP). GP is an approach based on the evolutionary principles of the species. GP is used to make extrapolations of data out of sample data. The extrapolations of data are achieved by irregularity functions which approximate very well the trend of the sample data. GP produces from just simple's functions, operators and a fitness function, complex mathematical expressions that adjust smoothly to a group of points of the form (xi, yi). We obtain amazing mathematical formulas that follow the behaviour of the sample data. We compare our algorithm with two techniques: the linear regression and non linear regression approaches. Our results suggest that we can predict with some percentage of error the largest singular values of a matrix without computing the singular values of the whole matrix and using only some random selected columns of the matrix.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D", } @Article{AguilarRivera:2015:ESA, author = "Ruben Aguilar-Rivera and Manuel Valenzuela-Rendon and J. J. Rodriguez-Ortiz", title = "Genetic algorithms and {Darwinian} approaches in financial applications: A survey", journal = "Expert Systems with Applications", year = "2015", volume = "42", number = "21", pages = "7684--7697", month = "30 " # nov, keywords = "genetic algorithms, genetic programming, Evolutionary computation, Finance, Portfolio optimization, Survey", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2015.06.001", URL = "http://www.sciencedirect.com/science/article/pii/S0957417415003954", abstract = "This article presents a review of the application of evolutionary computation methods to solving financial problems. Genetic algorithms, genetic programming, multi-objective evolutionary algorithms, learning classifier systems, co-evolutionary approaches, and estimation of distribution algorithms are the techniques considered. The novelty of our approach comes in three different manners: it covers time lapses not included in other review articles, it covers problems not considered by others, and the scope covered by past and new references is compared and analysed. The results concluded the interest about methods and problems has changed through time. Although, genetic algorithms have remained the most popular approach in the literature. There are combinations of problems and solutions methods which are yet to be investigated.", } @InProceedings{aguirre:1999:EH, author = "Arturo Hernandez Aguirre and Carlos A. Coello Coello and Bill P. Buckles", title = "A Genetic Programming Approach to Logic Function Synthesis by Means of Multiplexers", booktitle = "Proceedings of the The First NASA/DOD Workshop on Evolvable Hardware", year = "1999", editor = "Adrian Stoica and Didier Keymeulen and Jason Lohn", pages = "46--53", address = "Pasadena, California", month = "19-21 " # jul, organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, evolvable hardware, 1-control line multiplexer, Boolean functions, fitness function, genetic programming approach, logic function synthesis, minimisation, multiplexers, Boolean functions, logic design, minimisation, multiplexing equipment", ISBN = "0-7695-0256-3", DOI = "doi:10.1109/EH.1999.785434", abstract = "This paper presents an approach based on the use of genetic programming to synthesize logic functions. The proposed approach uses the 1-control line multiplexer as the only design unit, defining any logic function (defined by a truth table) through the replication of this single unit. Our fitness function first explores the search space trying to find a feasible design and then concentrates in the minimization of such (fully feasible) circuit. The proposed approach is illustrated using several sample Boolean functions.", notes = "EH-1999", } @InProceedings{aguirre:1999:CCMOGA, author = "Hernan E. Aguirre and Kiyoshi Tanaka and Tatsuo Sugimura", title = "Cooperative Crossover and Mutation Operators in Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "772", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Aguirre:2003:AIR, author = "Arturo Hernandez Aguirre and Carlos A. {Coello Coello}", title = "Evolutionary Synthesis of Logic Circuits Using Information Theory", journal = "Artificial Intelligence Review", year = "2003", volume = "20", number = "3-4", pages = "445--471", keywords = "genetic algorithms, genetic programming, circuit synthesis, computer-aided design, evolutionary algorithms, evolvable hardware, information theory", language = "English", publisher = "Kluwer Academic Publishers", ISSN = "0269-2821", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.378.9801", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.9801", URL = "http://hera.ugr.es/doi/14977278.pdf", URL = "http://dx.doi.org/10.1023/B%3AAIRE.0000006603.98023.97", DOI = "doi:10.1023/B:AIRE.0000006603.98023.97", abstract = "In this paper, we propose the use of Information Theory as the basis for designing a fitness function for Boolean circuit design using Genetic Programming. Boolean functions are implemented by replicating binary multiplexers. Entropy-based measures, such as Mutual Information and Normalised Mutual Information are investigated as tools for similarity measures between the target and evolving circuit. Three fitness functions are built over a primitive one. We show that the landscape of Normalized Mutual Information is more amenable for being used as a fitness function than simple Mutual Information. The evolutionary synthesised circuits are compared to the known optimum size. A discussion of the potential of the Information-Theoretical approach is given.", } @InProceedings{Hernandez-Aguirre:2004:MIFFfECS, title = "Mutual Information-based Fitness Functions for Evolutionary Circuit Synthesis", author = "Arturo Hernandez-Aguirre and Carlos Coello-Coello", pages = "1309--1316", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", volume = "2", keywords = "genetic algorithms, genetic programming, EHW, Evolutionary Design Automation, Evolutionary design \& evolvable hardware", ISBN = "0-7803-8515-2", URL = "http://delta.cs.cinvestav.mx/~ccoello/conferences/cec04-muxmutual.pdf.gz", DOI = "doi:10.1109/CEC.2004.1331048", size = "8 pages", abstract = "Mutual Information and Normalised Mutual Information measures are investigated. The goal is the analysis of some fitness functions based in mutual information and what problems prevent them from common use. We identify and find a clear explanation to them, thereafter, we propose new fitness functions and ran several experiments to investigate their effect on the search space, convergence time, and quality of solutions.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @Article{AGWU:2018:PT, author = "Okorie E. Agwu and Julius U. Akpabio and Sunday B. Alabi and Adewale Dosunmu", title = "Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies", journal = "Powder Technology", volume = "339", pages = "728--746", year = "2018", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Drill cuttings, Numerical simulations, Settling velocity", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2018.08.064", URL = "http://www.sciencedirect.com/science/article/pii/S0032591018307022", abstract = "In this paper, a comprehensive review of experimental, numerical and artificial intelligence studies on the subject of cuttings settling velocity in drilling muds made by researchers over the last seven decades is brought to the fore. In this respect, 91 experimental, 13 numerical simulations and 7 artificial intelligence researches were isolated, reviewed, tabulated and discussed. A comparison of the three methods and the challenges facing each of these methods were also reviewed. The major outcomes of this review include: (1) the unanimity among experimental researchers that mud rheology, particle size and shape and wall effect are major parameters affecting the settling velocity of cuttings in wellbores; (2) the prevalence of cuttings settling velocity experiments done with the mud in static conditions and the wellbore in the vertical configuration; (3) the extensive use of rigid particles of spherical shape to represent drill cuttings due to their usefulness in experimental visualization, particle tracking, and numerical implementation; (4) the existence of an artificial intelligence technique - multi-gene genetic programming (MGGP) which can provide an explicit equation that can help in predicting settling velocity; (5) the limited number of experimental studies factoring in the effect of pipe rotation and well inclination effects on the settling velocity of cuttings and (6) the most applied numerical method for determining settling velocity is the finite element method. Despite these facts, there is need to perform more experiments with real drill cuttings and factor in the effects of conditions such as drillstring rotation and well inclination and use data emanating therefrom to develop explicit models that would include the effects of these. It should be noted however, that the aim of this paper is not to create an encyclopaedia of particle settling velocity research, but to provide to the researcher with a basic, theoretical, experimental and numerical overview of what has so far been achieved in the area of cuttings settling velocity in drilling muds", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Drill cuttings, Numerical simulations, Settling velocity", } @Article{AGWU:2021:UOGT, author = "Okorie Ekwe Agwu and Julius Udoh Akpabio and Adewale Dosunmu", title = "Modeling the downhole density of drilling muds using multigene genetic programming", journal = "Upstream Oil and Gas Technology", volume = "6", pages = "100030", year = "2021", ISSN = "2666-2604", DOI = "doi:10.1016/j.upstre.2020.100030", URL = "https://www.sciencedirect.com/science/article/pii/S266626042030030X", keywords = "genetic algorithms, genetic programming, Multigene genetic programming, Downhole mud density, Drilling mud, HTHP", abstract = "The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a mean absolute error of 0.0246 and the square of correlation coefficient (R2) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R2 of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95percent", } @Misc{nlin/0607029, author = "Dilip P. Ahalpara and Jitendra C. Parikh", title = "Modeling Time Series of Real Systems using Genetic Programming", howpublished = "ArXiv Nonlinear Sciences e-prints", year = "2006", month = "14 " # jul, note = "Submitted to Physical Review E", adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2006nlin......7029A&db_key=PRE", adsnote = "Provided by the Smithsonian/NASA Astrophysics Data System", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/PS_cache/nlin/pdf/0607/0607029v1.pdf", size = "10 pages", abstract = "Analytic models of two computer generated time series (Logistic map and Rossler system) and two real time series (ion saturation current in Aditya Tokamak plasma and NASDAQ composite index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very good description of rest of the data. Predictions made using the map iteratively range from being very good to fair.", notes = "nlin/0607029 See also \cite{Ahalpara:2008:IJMPC}", } @Article{Ahalpara:2008:IJMPC, author = "Dilip P. Ahalpara and Jitendra C. Parikh", title = "Genetic Programming based approach for Modeling Time Series data of real systems", journal = "International Journal of Modern Physics C, Computational Physics and Physical Computation", year = "2008", volume = "19", number = "1", pages = "63--91", keywords = "genetic algorithms, genetic programming, Time series analysis, artificial neural networks", DOI = "doi:10.1142/S0129183108011942", abstract = "Analytic models of a computer generated time series (logistic map) and three real time series (ion saturation current in Aditya Tokamak plasma, NASDAQ composite index and Nifty index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very good description of the rest of the data. Predictions made using the map iteratively are very good for computer generated time series but not for the data of real systems. For such cases, an extended GP model is proposed and illustrated. A comparison of these results with those obtained using Artificial Neural Network (ANN) is also carried out.", notes = "IJMPC PACS numbers: 05.45.Tp, 02.30.NW Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar-382428, India Physical Research Laboratory, Navrangpura, Ahmedabad-380009, India", } @Article{2008Prama..71..459A, author = "Dilip P. Ahalpara and Amit Verma and Jitendra C. Parikh and Prasanta K. Panigrahi", title = "Characterizing and modelling cyclic behaviour in non-stationary time series through multi-resolution analysis", journal = "Pramana", year = "2008", month = nov, volume = "71", pages = "459--485", publisher = "Springer India, in co-publication with Indian Academy of Sciences", keywords = "genetic algorithms, genetic programming, finance, Non-stationary time series, wavelet transform, Characterizing and modelling cyclic behaviour in non-stationary time series through multi-resolution analysis", ISSN = "0304-4289", DOI = "doi:10.1007/s12043-008-0125-x", adsurl = "http://adsabs.harvard.edu/abs/2008Prama..71..459A", adsnote = "Provided by the SAO/NASA Astrophysics Data System", abstract = "A method based on wavelet transform is developed to characterise variations at multiple scales in non-stationary time series. We consider two different financial time series, S&P CNX Nifty closing index of the National Stock Exchange (India) and Dow Jones industrial average closing values. These time series are chosen since they are known to comprise of stochastic fluctuations as well as cyclic variations at different scales. The wavelet transform isolates cyclic variations at higher scales when random fluctuations are averaged out; this corroborates correlated behaviour observed earlier in financial time series through random matrix studies. Analysis is carried out through Haar, Daubechies-4 and continuous Morlet wavelets for studying the character of fluctuations at different scales and show that cyclic variations emerge at intermediate time scales. It is found that Daubechies family of wavelets can be effectively used to capture cyclic variations since these are local in nature. To get an insight into the occurrence of cyclic variations, we then proceed to model these wavelet coefficients using genetic programming (GP) approach and using the standard embedding technique in the reconstructed phase space. It is found that the standard methods (GP as well as artificial neural networks) fail to model these variations because of poor convergence. A novel interpolation approach is developed that overcomes this difficulty. The dynamical model equations have, primarily, linear terms with additive Pade-type terms. It is seen that the emergence of cyclic variations is due to an interplay of a few important terms in the model. Very interestingly GP model captures smooth variations as well as bursty behaviour quite nicely.", notes = "(1) Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar, 382 428, India (2) Physical Research Laboratory, Navrangpura, Ahmedabad, 380 009, India (3) Indian Institute of Science Education and Research, Salt Lake City, Kolkata, 700 106, India", } @InProceedings{Ahalpara:2009:eurogp, author = "Dilip Ahalpara and Siddharth Arora and M Santhanam", title = "Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEG", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "13--24", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_2", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @InProceedings{Ahalpara:2010:gecco, author = "Dilip P. Ahalpara", title = "Improved forecasting of time series data of real system using genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "977--978", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830658", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A study is made to improve short term forecasting of time series data of real system using Genetic Programming (GP) under the framework of time delayed embedding technique. GP based approach is used to make analytical model of time series data of real system using embedded vectors that help reconstruct the phase space. The map equations, involving non-linear symbolic expressions in the form of binary trees comprising of time delayed components in the immediate past, are first obtained by carrying out single-step GP fit for the training data set and usually they are found to give good fitness as well as single-step predictions. However while forecasting the time series based on multi-step predictions in the out-of-sample region in an iterative manner, these solutions often show rapid deterioration as we dynamically forward the solution in future time. With a view to improve on this limitation, it is shown that if the multi-step aspect is incorporated while making the GP fit itself, the corresponding GP solutions give multi-step predictions that are improved to a fairly good extent for around those many multi-steps as incorporated during the multi-step GP fit. Two different methods for multi-step fit are introduced, and the corresponding prediction results are presented. The modified method is shown to make better forecast for out-of-sample multi-step predictions for the time series of a real system, namely Electroencephelograph (EEG) signals.", notes = "Also known as \cite{1830658} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{ahalpara:2011:EuroGP, author = "Dilip Ahalpara and Abhijit Sen", title = "A Sniffer Technique for an Efficient Deduction of Model Dynamical Equations using Genetic Programming", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "1--12", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, local search, hill climbing", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_1", abstract = "A novel heuristic technique that enhances the search facility of the standard genetic programming (GP) algorithm is presented. The method provides a dynamic sniffing facility to optimise the local search in the vicinity of the current best chromosomes that emerge during GP iterations. Such a hybrid approach, that combines the GP method with the sniffer technique, is found to be very effective in the solution of inverse problems where one is trying to construct model dynamical equations from either finite time series data or knowledge of an analytic solution function. As illustrative examples, some special function ordinary differential equations (ODEs) and integrable nonlinear partial differential equations (PDEs) are shown to be efficiently and exactly recovered from known solution data. The method can also be used effectively for solution of model equations (the direct problem) and as a tool for generating multiple dynamical systems that share the same solution space.", notes = "Mathematica. Order of partial or ordinary differential equation search in sequence starting with first order and increasing until satisfactory match found. Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{ahalpara:2007:EMBN, author = "Dilip P. Ahalpara and Prasanta K. Panigrahi and Jitendra C. Parikh", title = "Variations in Financial Time Series: Modelling Through Wavelets and Genetic Programming", booktitle = "Econophysics of Markets and Business Networks", year = "2007", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-88-470-0665-2_3", DOI = "doi:10.1007/978-88-470-0665-2_3", } @Article{Ahangar-Asr:2011:EC, author = "Alireza Ahangar-Asr and Asaad Faramarzi and Akbar A. Javadi and Orazio Giustolisi", title = "Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression", journal = "Engineering Computation", year = "2011", volume = "28", number = "4", pages = "492--507", keywords = "genetic algorithms, genetic programming, Mechanical \& Materials Engineering, Concretes, Mechanical behaviour of materials, Rubbers", ISSN = "0264-4401", DOI = "doi:10.1108/02644401111131902", publisher = "Emerald Group Publishing Limited", abstract = "Using discarded tyre rubber as concrete aggregate is an effective solution to the environmental problems associated with disposal of this waste material. However, adding rubber as aggregate in concrete mixture changes, the mechanical properties of concrete, depending mainly on the type and amount of rubber used. An appropriate model is required to describe the behaviour of rubber concrete in engineering applications. The purpose of this paper is to show how a new evolutionary data mining technique, evolutionary polynomial regression (EPR), is used to predict the mechanical properties of rubber concrete. Design/methodology/approach EPR is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures. Findings Data from 70 cases of experiments on rubber concrete are used for development and validation of the EPR models. Three models are developed relating compressive strength, splitting tensile strength, and elastic modulus to a number of physical parameters that are known to contribute to the mechanical behaviour of rubber concrete. The most outstanding characteristic of the proposed technique is that it provides a transparent, structured, and accurate representation of the behaviour of the material in the form of a polynomial function, giving insight to the user about the contributions of different parameters involved. The proposed model shows excellent agreement with experimental results, and provides an efficient method for estimation of mechanical properties of rubber concrete. Originality/value In this paper, a new evolutionary data mining approach is presented for the analysis of mechanical behaviour of rubber concrete. The new approach overcomes the shortcomings of the traditional and artificial neural network-based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.", notes = "Research paper. Computational Geomechanics Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK Civil and Environmental Engineering Department, Faculty of Engineering, Technical University of Bari, Taranto, Italy", } @PhdThesis{Ahangar-Asr:thesis, author = "Alireza AhangarAsr", title = "Application of an Evolutionary Data Mining Technique for Constitutive Modelling of Geomaterials", school = "University of Exeter", year = "2012", address = "UK", month = "31 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10871/9925", URL = "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/9925/AhangarasrA.pdf", size = "214 pages", abstract = "Modelling behaviour of materials involves approximating the actual behaviour with that of an idealised material that deforms in accordance with some constitutive relationships. Several constitutive models have been developed for various materials many of which involve determination of material parameters with no physical meaning. ANN is a computer-based modelling technique for computation and knowledge representation inspired by the neural architecture and operation of the human brain. It has been shown by various researchers that ANNs offer outstanding advantages in constitutive modelling of material; however, these networks have some shortcoming. In this thesis, the Evolutionary Polynomial Regression (EPR) was introduced as an alternative approach to constitutive modelling of the complex behaviour of saturated and unsaturated soils and also modelling of a number of other civil and geotechnical engineering materials and systems. EPR overcomes the shortcomings of ANN by providing a structured and transparent model representing the behaviour of the system. In this research EPR is applied to modelling of stress-strain and volume change behaviour of unsaturated soils, modelling of SWCC in unsaturated soils, hydro-thermo-mechanical modelling of unsaturated soils, identification of coupling parameters between shear strength behaviour and chemical's effects in compacted soils, modelling of permeability and compaction characteristics of soils, prediction of the stability status of soil and rock slopes and modelling the mechanical behaviour of rubber concrete. Comparisons between EPR-based material model predictions, the experimental data and the predictions from other data mining and regression modelling techniques and also the results of the parametric studies revealed the exceptional capabilities of the proposed methodology in modelling the very complicated behaviour of geotechnical and civil engineering materials.", notes = "Ahangar-Asr Supervisor: Akbar Javadi 2nd supervisor: Slobodan Djordjevic", } @Article{DBLP:journals/ewc/AhangarAsrJJ23, author = "Alireza Ahangar-Asr and A. Johari and Akbar A. Javadi", title = "An evolutionary-based polynomial regression modeling approach to predicting discharge flow rate under sheet piles", journal = "Engineering with Computers", year = "2023", volume = "39", number = "6", pages = "4093--4101", keywords = "genetic algorithms, genetic programming, EPR, Sheet piles/cut-off walls, Seepage flow rate, Evolutionary computation, Data mining", timestamp = "Sat, 08 Jun 2024 13:15:18 +0200", biburl = "https://dblp.org/rec/journals/ewc/AhangarAsrJJ23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://rdcu.be/dPatP", DOI = "doi:10.1007/S00366-023-01872-1", size = "9 pages", notes = "Contrast EPR and GP?", } @InProceedings{Aher:2012:ICSP, author = "R. P. Aher and K. C. Jodhanle", booktitle = "Signal Processing (ICSP), 2012 IEEE 11th International Conference on", title = "Removal of Mixed Impulse noise and Gaussian noise using genetic programming", year = "2012", volume = "1", pages = "613--618", abstract = "In this paper, we have put forward a nonlinear filtering method for removing mixed Impulse and Gaussian noise, based on the two step switching scheme. The switching scheme uses two cascaded detectors for detecting the noise and two corresponding estimators which effectively and efficiently filters the noise from the image. A supervised learning algorithm, Genetic programming, is employed for building the two detectors with complementary characteristics. Most of the noisy pixels are identified by the first detector. The remaining noises are searched by the second detector, which is usually hidden in image details or with amplitudes close to its local neighbourhood. Both the detectors designed are based on the robust estimators of location and scale i.e. Median and Median Absolute Deviation (MAD). Unlike many filters which are specialised only for a particular noise model, the proposed filters in this paper are capable of effectively suppressing all kinds of Impulse and Gaussian noise. The proposed two-step Genetic Programming filters removes impulse and Gaussian noise very efficiently, and also preserves the image details.", keywords = "genetic algorithms, genetic programming, Gaussian noise, image denoising, impulse noise, learning (artificial intelligence), nonlinear filters, Gaussian noise, Median Absolute Deviation, cascaded detectors, complementary characteristics, image details, impulse noise, local neighbourhood, noisy pixels, nonlinear filtering method, second detector, supervised learning algorithm, two step switching scheme, alpha trimmed mean estimator, CWM, Gaussian Noise, Impulse noise, Median, Median Absolute Deviation (MAD), Non-Linear filters, Supervised Learning, Switching scheme", DOI = "doi:10.1109/ICoSP.2012.6491563", ISSN = "2164-5221", notes = "Also known as \cite{6491563}", } @InProceedings{Ahlgren:2020:GI, author = "John Ahlgren and Maria Eugenia Berezin and Kinga Bojarczuk and Elena Dulskyte and Inna Dvortsova and Johann George and Natalija Gucevska and Mark Harman and Ralf Laemmel and Erik Meijer and Silvia Sapora and Justin Spahr-Summers", title = "{WES}: Agent-based User Interaction Simulation on Real Infrastructure", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "276--284", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, social testing, APR, Connectivity, Data Science, Facebook AI Research, Human Computer Interaction, UX Human, Machine Learning", isbn13 = "978-1-4503-7963-2", URL = "https://research.fb.com/wp-content/uploads/2020/04/WES-Agent-based-User-Interaction-Simulation-on-Real-Infrastructure.pdf", URL = "https://research.fb.com/publications/wes-agent-based-user-interaction-simulation-on-real-infrastructure/", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392089", size = "9 pages", abstract = "We introduce the Web-Enabled Simulation (WES) research agenda, and describe FACEBOOK WW system. We describe the application of WW to reliability, integrity and privacy at FACEBOOK, where it is used to simulate social media interactions on an infrastructure consisting of hundreds of millions of lines of code. The WES agenda draws on research from many areas of study, including Search Based Software Engineering, Machine Learning, Programming Languages, Multi Agent Systems, Graph Theory, Game AI, and AI Assisted Game Play. We conclude with a set of open problems and research challenges to motivate wider investigation.", notes = "London probable . The WES agenda draws on research from many areas of study, including (but not limited to) Search Based Software Engineering, Machine Learning, Programming Languages, Multi Agent Systems, Graph Theory, Game AI, AI Assisted Game Play. Intelligent learning/trainable Bots interact with The social graph. A/B testing. Automated Mechanism Design. Social bugs/social testing. scammer bot. Super-human bots. Big changes in machine learning classification performance, data pipeline line breakages. WES Test Oracle uses machine learning. Co-evolutionary Mechanism Learning. virtual speed humps to hinter anti-social behaviour. Video: https://youtu.be/GsNKCifm44A (13--55 minutes from start, followed by discussion, end 1:13:00) http://geneticimprovementofsoftware.com/gi2020icse.html FACEBOOK Inc", } @InProceedings{Ahlgren:2021:ICSE, author = "John Ahlgren and Maria Eugenia Berezin and Kinga Bojarczuk and Elena Dulskyte and Inna Dvortsova and Johann George and Natalija Gucevska and Mark Harman and Maria Lomeli and Erik Meijer and Silvia Sapora and Justin Spahr-Summers", title = "Testing Web Enabled Simulation at Scale Using Metamorphic Testing", booktitle = "Proceedings of the International Conference on Software Engineering, ICSE 2021", year = "2021", editor = "Arie {van Deursen} and Tao Xie and Natalia Juristo Oscar Dieste", pages = "140--149", month = "25-28 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Metamorphic Testing, Oracle Problem, Scalability, Testing, Test Flakiness, Web-Enabled Simulation", URL = "https://research.fb.com/publications/testing-web-enabled-simulation-at-scale-using-metamorphic-testing/", URL = "https://research.fb.com/wp-content/uploads/2021/03/Testing-Web-Enabled-Simulation-at-Scale-Using-Metamorphic-Testing.pdf", video_url = "https://www.youtube.com/watch?v=pNKqyn-90Ig", DOI = "doi:10.1109/ICSE-SEIP52600.2021.00023", size = "10 pages", abstract = "We report on Facebook deployment of MIA (Metamorphic Interaction Automaton). MIA is used to test Facebook's Web Enabled Simulation, built on a web infrastructure of hundreds of millions of lines of code. MIA tackles the twin problems of test flakiness and the unknowable oracle problem. It uses metamorphic testing to automate continuous integration and regression test execution. MIA also plays the role of a test bot, automatically commenting on all relevant changes submitted for code review. It currently uses a suite of over 40 metamorphic test cases. Even at this extreme scale, a non-trivial metamorphic test suite subset yields outcomes within 20 minutes (sufficient for continuous integration and review processes). Furthermore, our offline mode simulation reduces test flakiness from approximately 50percent (of all online tests) to 0percent (offline). Metamorphic testing has been widely-studied for 22 years. This paper is the first reported deployment into an industrial continuous integration system.", } @InProceedings{DBLP:conf/ease/AhlgrenBDDGGHLL21, author = "John Ahlgren and Kinga Bojarczuk and Sophia Drossopoulou and Inna Dvortsova and Johann George and Natalija Gucevska and Mark Harman and Maria Lomeli and Simon M. Lucas and Erik Meijer and Steve Omohundro and Rubmary Rojas and Silvia Sapora and Norm Zhou", title = "Facebook's Cyber-Cyber and Cyber-Physical Digital Twins", booktitle = "EASE 2021: Evaluation and Assessment in Software Engineering", year = "2021", editor = "Ruzanna Chitchyan and Jingyue Li and Barbara Weber and Tao Yue", address = "Trondheim, Norway", month = jun # " 21-24", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Web Enabled Simulation, Digital Twin, facebook, meta, social media, online, software engineering", timestamp = "Mon, 21 Jun 2021 12:29:10 +0200", biburl = "https://dblp.org/rec/conf/ease/AhlgrenBDDGGHLL21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://research.facebook.com/publications/facebooks-cyber-cyber-and-cyber-physical-digital-twins/", URL = "https://discovery.ucl.ac.uk/id/eprint/10139789/1/EASE21.pdf", URL = "https://doi.org/10.1145/3463274.3463275", DOI = "doi:10.1145/3463274.3463275", size = "9 pages", abstract = "A cyber/cyber digital twin is a simulation of a software system. By contrast, a cyber-physical digital twin is a simulation of a non-software (physical) system. Although cyberphysical digital twins have received a lot of recent attention, their cyber--cyber counterparts have been comparatively overlooked. In this paper we show how the unique properties of cyber cyber digital twins open up exciting opportunities for research and development. Like all digital twins, the cyber--cyber digital twin is both informed by and informs the behaviour of the twin it simulates. It is therefore a software system that simulates another software system, making it conceptually truly a twin, blurring the distinction between the simulated and the simulator. Cyber-cyber digital twins can be twins of other cyber--cyber digital twins, leading to a hierarchy of twins. As we shall see, these apparently philosophical observations have practical ramifications for the design, implementation and deployment of digital twins at Meta.", } @Unpublished{ahlschwede:2000:ugppm, author = "John Ahlschwede", title = "Using Genetic Programming to Play Mancala", year = "2000", note = "http://www.corngolem.com/john/gp/index.html", keywords = "genetic algorithms, genetic programming", abstract = "This paper will explain what genetic programming is, what mancala is, how I used genetic programming to evolve mancala-playing programs, and the results I got from doing so.", } @InProceedings{ahluwalia:1996:ccpGP, author = "Manu Ahluwalia and Terence C. Fogarty", title = "Co-Evolving Hierarchical Programs Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "419", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "1 page", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap58.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{Ahluwalia:1997:, author = "Manu Ahluwalia and Larry Bull and Terence C. Fogarty", title = "Co-evolving Functions in Genetic Programming: A Comparison in ADF Selection Strategies", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "3--8", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Ahluwalia_1997_.pdf", size = "6 pages", notes = "GP-97", } @InProceedings{ahluwalia:1997:cfGPea, author = "Manu Ahluwalia and Larry Bull and Terence C. Fogarty", title = "Co-evolving Functions in Genetic Programming: An Emergent Approach using ADFs and GLiB", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "1--6", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{ahluwalia:1998:cfGP:ADF+GLiB, author = "M. Ahluwalia and L. Bull", title = "Co-evolving Functions in Genetic Programming: Dynamic ADF Creation using GLiB", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "809--818", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", DOI = "doi:10.1007/BFb0040753", notes = "EP-98. University of the West of England, UK", } @InProceedings{ahluwalia:1999:AGPCS, author = "Manu Ahluwalia and Larry Bull", title = "A Genetic Programming-based Classifier System", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "11--18", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, classifier systems", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/ahluwalia_1999_agpcs.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{ahluwalia:1999:CFGPCK, author = "Manu Ahluwalia and Larry Bull", title = "Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "947--952", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-413.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-413.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @PhdThesis{Ahluwalia:thesis, author = "Manu Ahluwalia", title = "Co-evolving functions in genetic programming", school = "University of the West of England at Bristol", year = "2000", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322427", notes = "uk.bl.ethos.322427", } @Article{Ahluwalia:2001:SA, author = "Manu Ahluwalia and Larry Bull", title = "Coevolving functions in genetic programming", journal = "Journal of Systems Architecture", volume = "47", pages = "573--585", year = "2001", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, ADF, Classification, EDF, Feature selection/extraction, Hierarchical programs, Knn, Speciation", ISSN = "1383-7621", URL = "https://uwe-repository.worktribe.com/output/1090581", URL = "http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3", DOI = "doi:10.1016/S1383-7621(01)00016-9", size = "13 pages", abstract = "In this paper we introduce a new approach to the use of automatically defined functions (ADFs) within genetic programming. The technique consists of evolving a number of separate sub-populations of functions which can be used by a population of evolving main programs. We present and refine a set of mechanisms by which the number and constitution of the function sub-populations can be defined and compare their performance on two well-known classification tasks. A final version of the general approach, for use explicitly on classification tasks, is then presented. It is shown that in all cases the coevolutionary approach performs better than traditional genetic programming with and without ADFs.", } @InProceedings{Ahmad:2012:GECCO, author = "Arbab Masood Ahmad and Gul Muhammad Khan and Sahibzada Ali Mahmud and Julian Francis Miller", title = "Breast cancer detection using cartesian genetic programming evolved artificial neural networks", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "1031--1038", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, real world applications, Algorithms, Design, Performance, Breast Cancer, Fine Needle Aspiration, FNA, ANN, Artificial Neural Network, Neuro-evolution", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330307", publisher = "ACM", publisher_address = "New York, NY, USA", size = "8 pages", abstract = "A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1percent for Type-I (classifying benign sample falsely as malignant) and 0.5percent for Type-II (classifying malignant sample falsely as benign).", notes = "Also known as \cite{2330307} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Ahmad:2012:FIT, author = "Arbab Masood Ahmad and Gul Muhammad Khan", booktitle = "Frontiers of Information Technology (FIT), 2012 10th International Conference on", title = "Bio-signal Processing Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)", year = "2012", pages = "261--268", DOI = "doi:10.1109/FIT.2012.54", abstract = "The aim of this paper is to explore the application of Neuro-Evolutionary Techniques to the diagnosis of various diseases. We applied the evolutionary technique of Cartesian Genetic programming Evolved Artificial Neural Network (CG-PANN) for the detection of three important diseases. Some cases showed excellent results while others are in the process of experimentation. In the first case we worked on diagnosing the extent of Parkinson's disease using a computer based test. Experiments in this case are in progress. In the second case, we applied the Fine Needle Aspirate (FNA) data for Breast Cancer from the WDBC website to our network to classify the samples as either benign (non-cancerous) or malignant (cancerous). The results from these experiments were highly satisfactory. In the third case, we developed a modified form of Pan-Tompkins's algorithm to detect the fiducial points from ECG signals and extracted key features from them. The features shall be applied to our network to classify the signals for the different types of Arrhythmias. Experimentation is still in progress.", keywords = "genetic algorithms, genetic programming, cardiology, diseases, electrocardiography, feature extraction, medical signal processing, neural nets, signal classification, CG-PANN, Cartesian genetic programming evolved artificial neural network, ECG signal, FNA data, Pan-Tompkins algorithm, Parkinson disease, arrhythmia, benign cancer, bio-signal processing, breast cancer, electrocardiography, experimentation process, feature extraction, fiducial point, fine needle aspirate, malignant cancer, neuro-evolutionary technique, Artificial neural networks, Cancer, Diseases, Electrocardiography, Feature extraction, Training, Breast Cancer detection, CGPANN, Cardiac Arrhythmias, FNA, Parkinson's Disease", notes = "Also known as \cite{6424333}", } @InProceedings{conf/eann/AhmadKM13, author = "Arbab Masood Ahmad and Gul Muhammad Khan and Sahibzada Ali Mahmud", title = "Classification of Arrhythmia Types Using Cartesian Genetic Programming Evolved Artificial Neural Networks", editor = "Lazaros S. Iliadis and Harris Papadopoulos and Chrisina Jayne", booktitle = "Proceedings of 14th International Conference on Engineering Applications of Neural Networks (EANN 2013), Part {I}", year = "2013", volume = "383", series = "Communications in Computer and Information Science", pages = "282--291", address = "Halkidiki, Greece", month = sep # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, CGPANN, artificial neural network, neuro-evolution, CVD, cardiac arrhythmias, classification, fiducial points, LBBB beats, RBBB beats", isbn13 = "978-3-642-41012-3", bibdate = "2014-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eann/eann2013-1.html#AhmadKM13", URL = "http://dx.doi.org/10.1007/978-3-642-41013-0", DOI = "doi:10.1007/978-3-642-41013-0_29", abstract = "Cartesian Genetic programming Evolved Artificial Neural Network (CGPANN) is explored for classification of different types of arrhythmia and presented in this paper. Electrocardiography (ECG) signal is preprocessed to acquire important parameters and then presented to the classifier. The parameters are calculated from the location and amplitudes of ECG fiducial points, determined with a new algorithm inspired by Pan-Tompkins's algorithm [14]. The classification results are satisfactory and better than contemporary methods introduced in the field.", } @InProceedings{conf/ifip12/AhmadKM14, author = "Arbab Masood Ahmad and Gul Muhammad Khan and Sahibzada Ali Mahmud", title = "Classification of Mammograms Using Cartesian Genetic Programming Evolved Artificial Neural Networks", booktitle = "Proceedings 10th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Innovations, AIAI 2014", year = "2014", editor = "Lazaros S. Iliadis and Ilias Maglogiannis and Harris Papadopoulos", volume = "436", series = "IFIP Advances in Information and Communication Technology", pages = "203--213", address = "Rhodes, Greece, September 19-21, 2014", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, mammogram image classification, GLCM, CGPANN, haralick's parameters", isbn13 = "978-3-662-44654-6", DOI = "doi:10.1007/978-3-662-44654-6_20", URL = "http://dx.doi.org/10.1007/978-3-662-44654-6_20", bibdate = "2014-09-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ifip12/aiai2014.html#AhmadKM14", URL = "http://dx.doi.org/10.1007/978-3-662-44654-6", abstract = "We developed a system that classifies masses or microcalcifications observed in a mammogram as either benign or malignant. The system assumes prior manual segmentation of the image. The image segment is then processed for its statistical parameters and applied to a computational intelligence system for classification. We used Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN) for classification. To train and test our system we selected 2000 mammogram images with equal number of benign and malignant cases from the well-known Digital Database for Screening Mammography (DDSM). To find the input parameters for our network we exploited the overlay files associated with the mammograms. These files mark the boundaries of masses or microcalcifications. A Gray Level Co-occurrence matrix (GLCM) was developed for a rectangular region enclosing each boundary and its statistical parameters computed. Five experiments were conducted in each fold of a 10-fold cross validation strategy. Testing accuracy of 100 percent was achieved in some experiments.", } @InProceedings{Ahmad:2018:GECCOcomp, author = "Hammad Ahmad and Thomas Helmuth", title = "A comparison of semantic-based initialization methods for genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "1878--1881", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3208218", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "During the initialization step, a genetic programming (GP) system traditionally creates a population of completely random programs to populate the initial population. These programs almost always perform poorly in terms of their total error---some might not even output the correct data type. In this paper, we present new methods for initialization that attempt to generate programs that are somewhat relevant to the problem being solved and/or increase the initial diversity (both error and behavioural diversity) of the population prior to the GP run. By seeding the population---and thereby eliminating worthless programs and increasing the initial diversity of the population---we hope to improve the performance of the GP system. Here, we present two novel techniques for initialization (Lexicase Seeding and Pareto Seeding) and compare them to a previous method (Enforced Diverse Populations) and traditional, non-seeded initialization. Surprisingly, we found that none of the initialization m", notes = "Also known as \cite{3208218} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{DBLP:conf/asplos/Ahmad0W22, author = "Hammad Ahmad and Yu Huang and Westley Weimer", title = "{CirFix}: automatically repairing defects in hardware design code", booktitle = "ASPLOS 2022: 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems", year = "2022", editor = "Babak Falsafi and Michael Ferdman and Shan Lu and Thomas F. Wenisch", pages = "990--1003", address = "Lausanne, Switzerland", month = "28 " # feb # "- 4 " # mar, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, automated program repair, APR, hardware designs, HDL benchmark", timestamp = "Wed, 02 Mar 2022 18:22:59 +0100", biburl = "https://dblp.org/rec/conf/asplos/Ahmad0W22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1145/3503222.3507763", DOI = "doi:10.1145/3503222.3507763", code_url = "https://github.com/hammad-a/verilog_repair", size = "14 pages", abstract = "CirFix, is a framework for automatically repairing defects in hardware designs implemented in languages like Verilog. We propose a novel fault localization approach based on assignments to wires and registers, and a fitness function tailored to the hardware domain to bridge the gap between software-level automated program repair and hardware descriptions. We also present a benchmark suite of 32 defect scenarios corresponding to a variety of hardware projects. Overall, CirFix produces plausible repairs for 21/32 and correct repairs for 16/32 of the defect scenarios. This repair rate is comparable to that of successful program repair approaches for software, indicating CirFix is effective at bringing over the benefits of automated program repair to the hardware domain for the first time.", } @InProceedings{DBLP:conf/ppsn/AhmadCFW22, author = "Hammad Ahmad and Padraic Cashin and Stephanie Forrest and Westley Weimer", title = "Digging into Semantics: Where Do Search-Based Software Repair Methods Search?", booktitle = "Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II", year = "2022", editor = "Guenter Rudolph and Anna V. Kononova and Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tusar", volume = "13399", series = "Lecture Notes in Computer Science", pages = "3--18", address = "Dortmund, Germany", month = sep # " 10-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Semantic search spaces, Program repair, Patch diversity, Daikon, Defects4J", timestamp = "Tue, 16 Aug 2022 16:15:42 +0200", biburl = "https://dblp.org/rec/conf/ppsn/AhmadCFW22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", isbn13 = "978-3-031-14720-3", URL = "https://web.eecs.umich.edu/~weimerw/p/weimer-asplos2022.pdf", DOI = "doi:10.1007/978-3-031-14721-0_1", abstract = "Search-based methods are a popular approach for automatically repairing software bugs, a field known as automated program repair (APR). There is increasing interest in empirical evaluation and comparison of different APR methods, typically measured as the rate of successful repairs on benchmark sets of buggy programs. Such evaluations, however, fail to explain why some approaches succeed and others fail. Because these methods typically use syntactic representations, i.e., source code, we know little about how the different methods explore their semantic spaces, which is relevant for assessing repair quality and understanding search dynamics. We propose an automated method based on program semantics, which provides quantitative and qualitative information about different APR search-based techniques. Our approach requires no manual annotation and produces both mathematical and human-understandable insights. In an empirical evaluation of 4 APR tools and 34 defects, we investigate the relationship between search-space exploration, semantic diversity and repair success, examining both the overall picture and how the tools search unfolds. Our results suggest that population diversity alone is not sufficient for finding repairs, and that searching in the right place is more important than searching broadly, highlighting future directions for the research community.", notes = "Semantic distance between code mutants estimated by comparing their invariant sets using Canberra distance. 2D visualisation. CapGem, GenProg, SimFix, TBar. Java. p12 four to twenty times more syntactic variability than (useful?) semantic variation. (ie Java syntax-to-semantics is many-to-one mapping.) PPSN2022", } @Article{Ahmad:2000:CCGc, author = "Ishfaq Ahmad", title = "Genetic Programming In Clusters", journal = "IEEE Concurrency", volume = "8", number = "3", pages = "10--11, 13", month = jul # "\slash " # sep, year = "2000", CODEN = "IECMFX", ISSN = "1092-3063", bibdate = "Tue Jan 16 11:59:57 2001", keywords = "genetic algorithms, genetic programming", ISSN = "1092-3063", publisher = "IEEE Computer Society", address = "Los Alamitos, CA, USA", URL = "http://csdl.computer.org/comp/mags/pd/2000/03/p3toc.htm", acknowledgement = ack-nhfb, DOI = "doi:10.1109/MCC.2000.10016", } @InProceedings{conf/sac/AhmadRRJ19, author = "Qadeer Ahmad and Atif Rafiq and Muhammad Adil Raja and Noman Javed", title = "Evolving {MIMO} multi-layered artificial neural networks using grammatical evolution", booktitle = "Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019", publisher = "ACM", year = "2019", editor = "Chih-Cheng Hung and George A. Papadopoulos", pages = "1278--1285", address = "Limassol, Cyprus", month = apr # " 8-12", keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN", isbn13 = "978-1-4503-5933-7", DOI = "doi:10.1145/3297280.3297408", bibdate = "2019-05-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2019.html#AhmadRRJ19", } @Article{Ahmadi:2021:AWM, author = "Farshad Ahmadi and Saeid Mehdizadeh and Babak Mohammadi and Quoc Bao Pham and Thi Ngoc Canh Doan and Ngoc Duong Vo", title = "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation", journal = "Agricultural Water Management", year = "2021", volume = "244", pages = "106622", keywords = "genetic algorithms, genetic programming, gene expression programming, empirical models, intelligent water drops, reference evapotranspiration, support vector regression", ISSN = "0378-3774", bibsource = "OAI-PMH server at oai.repec.org", oai = "oai:RePEc:eee:agiwat:v:244:y:2021:i:c:s0378377420321697", URL = "https://www.sciencedirect.com/science/article/pii/S0378377420321697", DOI = "doi:10.1016/j.agwat.2020.106622", abstract = "Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimisation algorithm, namely intelligent water drops (IWD) (i.e., SVR{$-$}IWD). Two various types of scenarios were considered, including the climatic data- and antecedent ET0 data-based patterns. In the climatic data-based models, the effective climatic parameters were recognised by using two pre-processing techniques consisting of {$\tau$} Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as using the {$\tau$} Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves{$-$}Samani (H{$-$}S) and Priestley{$-$}Taylor (P{$-$}T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.", } @Article{journals/eaai/AhmadizarSAT15, author = "Fardin Ahmadizar and Khabat Soltanian and Fardin AkhlaghianTab and Ioannis Tsoulos", title = "Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm", journal = "Engineering Applications of Artificial Intelligence", year = "2015", volume = "39", bibdate = "2015-02-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/eaai/eaai39.html#AhmadizarSAT15", pages = "1--13", month = mar, keywords = "genetic algorithms, genetic programming, grammatical evolution, Neural networks, ANN, Adaptive penalty approach, Classification problems", ISSN = "0952-1976", URL = "http://www.sciencedirect.com/science/article/pii/S0952197614002759", URL = "http://dx.doi.org/10.1016/j.engappai.2014.11.003", abstract = "The most important problems with exploiting artificial neural networks (ANNs) are to design the network topology, which usually requires an excessive amount of expert's effort, and to train it. In this paper, a new evolutionary-based algorithm is developed to simultaneously evolve the topology and the connection weights of ANNs by means of a new combination of grammatical evolution (GE) and genetic algorithm (GA). GE is adopted to design the network topology while GA is incorporated for better weight adaptation. The proposed algorithm needs to invest a minimal expert's effort for customisation and is capable of generating any feedforward ANN with one hidden layer. Moreover, due to the fact that the generalisation ability of an ANN may decrease because of over fitting problems, the algorithm uses a novel adaptive penalty approach to simplify ANNs generated through the evolution process. As a result, it produces much simpler ANNs that have better generalization ability and are easy to implement. The proposed method is tested on some real world classification datasets, and the results are statistically compared against existing methods in the literature. The results indicate that our algorithm outperforms the other methods and provides the best overall performance in terms of the classification accuracy and the number of hidden neurons. The results also present the contribution of the proposed penalty approach in the simplicity and generalisation ability of the generated networks.", notes = "also known as \cite{AHMADIZAR20151}", } @InProceedings{Ahmed:2015:ieeeICIP, author = "Faisal Ahmed and Padma Polash Paul and Marina L. Gavrilova", booktitle = "2015 IEEE International Conference on Image Processing (ICIP)", title = "Evolutionary fusion of local texture patterns for facial expression recognition", year = "2015", pages = "1031--1035", abstract = "This paper presents a simple, yet effective facial feature descriptor based on evolutionary synthesis of different local texture patterns. Unlike the traditional face descriptors that exploit visually-meaningful facial features, the proposed method adopts a genetic programming-based feature fusion approach that uses different local texture patterns and a set of linear and nonlinear operators in order to synthesise new features. The strength of this approach lies in fusing the advantages of different state-of-the-art local texture descriptors and thus, obtaining more robust composite features. Recognition performance of the proposed method is evaluated using the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, facial features synthesised based on the proposed approach yield an improved recognition performance, as compared to some well-known face feature descriptors.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIP.2015.7350956", month = sep, notes = "Also known as \cite{7350956}", } @Article{ahmed:2020:IJDC, author = "Moataz Ahmed and Moustafa El-Gindy and Haoxiang Lang", title = "A novel genetic-programming based differential braking controller for an 8x8 combat vehicle", journal = "International Journal of Dynamics and Control", year = "2020", volume = "8", number = "4", keywords = "genetic algorithms, genetic programming, Stability control, Direct yaw control, Differential braking, Adaptive neuro-fuzzy, Fuzzy logic", URL = "http://link.springer.com/article/10.1007/s40435-020-00693-0", DOI = "doi:10.1007/s40435-020-00693-0", size = "15 pages", abstract = "Lateral stability of multi-axle vehicle's was not considered and studied widely despite its advantages and use in different fields such as transportation, commercial, and military applications. In this research, a novel adaptive Direct Yaw moment Control based on Genetic-Programming (GPDB) is developed and compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In addition, a phase-plane analysis of the vehicles nonlinear model is also discussed to introduce the activation criteria to the proposed controller in order to prevent excessive control effort. The controller is evaluated through a series of severe maneuvers in the simulator. The developed GPDB resulting in comparable performance to the ANFIS controller with better implementation facility and design procedure, where a single equation replaces multiple fuzzy rules. The results show fidelity and the ability of the developed controller to stabilize the vehicle near limit-handling driving conditions", } @PhdThesis{Aboelfadl_Ahmed_Moataz, author = "Moataz Aboelfadl Ahmed", title = "Integrated Chassis Control Strategies For Multi-Wheel Combat Vehicle", school = "Department of Automotive and Mechatronics Engineering Faculty of Engineering and Applied Science, University of Ontario Institute of Technology", year = "2021", address = "Oshawa, Ontario, Canada", month = nov, keywords = "genetic algorithms, genetic programming, Chassis control, Lateral stability, Intelligent control, Multi-axle, Combat vehicles", URL = "https://hdl.handle.net/10155/1380", URL = "https://ir.library.ontariotechu.ca/handle/10155/1380", URL = "https://ir.library.ontariotechu.ca/bitstream/handle/10155/1380/Aboelfadl_Ahmed_Moataz.pdf", size = "227 pages", abstract = "Combat vehicles are exposed to high risks due to their high ground clearance and nature of operation in harsh environments. This requires robust stability controllers to cope with the rapid change and uncertainty of driving conditions on various terrains. Moreover, it is required to enhance vehicle stability and increase safety to reduce accidents fatality probability. This research focuses on investigating the effectiveness of different lateral stability controllers and their integration in enhancing the cornering performance of an 8x8 combat vehicle when driving at limited handling conditions. In this research, a new Active Rear Steering (ARS) stability controller for an 8x8 combat vehicle is introduced. This technique is extensively investigated to show its merits and effectiveness for human and autonomous operation. For human operation, the ARS is developed using Linear Quadratic Regulator (LQR) control method, which is compared with previous techniques. Furthermore, the controller is extended and tested for working in a rough and irregular road profile using a novel adaptive Integral Sliding Mode Controller (ISMC). In the case of autonomous operation, a frequency domain analysis is conducted to show the benefits of considering the steering of the rear axles in the path-following performance at different driving conditions. The study compared two different objectives for the controller; the first is including the steering of the rear axles in the path following controller, while the second is to integrate it as a stability controller with a front-steering path-following controller. In addition, this research introduces a novel Differential Braking (DB) controller. The proposed control prevents the excessive use of braking forces and consequently the longitudinal dynamics deterioration. Besides, it introduces an effective DB controller with less dependency and sensitivity to the reference yaw model. Eventually, two various Integrated Chassis Controllers (ICC) are developed and compared. The first is developed by integrating the ISMC-ARS with the DB controller using a fuzzy logic controller. The second ICC integrates the ISMC-ARS with a developed robust Torque Vectoring Controller (TVC). This integration is designed based on a performance map that shows the effective region of each controller using a new technique based on Machine Learning (ML).", notes = "Ontario Tech University Supervisor: Moustafa El-Gindy", } @InProceedings{DBLP:conf/ausai/AhmedZP12, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Genetic Programming for Biomarker Detection in Mass Spectrometry Data", booktitle = "25th Joint Conference Australasian Conference on Artificial Intelligence, AI 2012", year = "2012", editor = "Michael Thielscher and Dongmo Zhang", volume = "7691", series = "Lecture Notes in Computer Science", pages = "266--278", address = "Sydney, Australia", month = dec # " 4-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-35100-6", DOI = "doi:10.1007/978-3-642-35101-3_23", abstract = "Classification of mass spectrometry (MS) data is an essential step for biomarker detection which can help in diagnosis and prognosis of diseases. However, due to the high dimensionality and the small sample size, classification of MS data is very challenging. The process of biomarker detection can be referred to as feature selection and classification in terms of machine learning. Genetic programming (GP) has been widely used for classification and feature selection, but it has not been effectively applied to biomarker detection in the MS data. In this study we develop a GP based approach to feature selection, feature extraction and classification of mass spectrometry data for biomarker detection. In this approach, we firstly use GP to reduce the redundant features by selecting a small number of important features and constructing high-level features, then we use GP to classify the data based on selected features and constructed features. This approach is examined and compared with three well known machine learning methods namely decision trees, naive Bayes and support vector machines on two biomarker detection data sets. The results show that the proposed GP method can effectively select a small number of important features from thousands of original features for these problems, the constructed high-level features can further improve the classification performance, and the GP method outperforms the three existing methods, namely naive Bayes, SVMs and J48, on these problems.", } @InProceedings{Ahmed:2013:evobio, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach", booktitle = "11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, {EvoBIO 2013}", year = "2013", editor = "Leonardo Vanneschi and William S. Bush and Mario Giacobini", month = apr # " 3-5", series = "LNCS", volume = "7833", publisher = "Springer Verlag", organisation = "EvoStar", address = "Vienna, Austria", pages = "43--55", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-37188-2", DOI = "doi:10.1007/978-3-642-37189-9_5", abstract = "Biomarker discovery using mass spectrometry (MS) data is very useful in disease detection and drug discovery. The process of biomarker discovery in MS data must start with feature selection as the number of features in MS data is extremely large (e.g. thousands) while the number of samples is comparatively small. In this study, we propose the use of genetic programming (GP) for automatic feature selection and classification of MS data. This GP based approach works by using the features selected by two feature selection metrics, namely information gain (IG) and relief-f (REFS-F) in the terminal set. The feature selection performance of the proposed approach is examined and compared with IG and REFS-F alone on five MS data sets with different numbers of features and instances. Naive Bayes (NB), support vector machines (SVMs) and J48 decision trees (J48) are used in the experiments to evaluate the classification accuracy of the selected features. Meanwhile, GP is also used as a classification method in the experiments and its performance is compared with that of NB, SVMs and J48. The results show that GP as a feature selection method can select a smaller number of features with better classification performance than IG and REFS-F using NB, SVMs and J48. In addition, GP as a classification method also outperforms NB and J48 and achieves comparable or slightly better performance than SVMs on these data sets.", } @InProceedings{Ahmed:2013:CEC, article_id = "1253", author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Enhanced Feature Selection for Biomarker Discovery in LC-MS Data using GP", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "584--591", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557621", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Ahmed:evoapps14, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data", booktitle = "17th European Conference on the Applications of Evolutionary Computation", year = "2014", editor = "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora", series = "LNCS", volume = "8602", publisher = "Springer", pages = "915--927", address = "Granada", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-45522-7", DOI = "doi:10.1007/978-3-662-45523-4_74", abstract = "Alignment of samples from Liquid chromatography-mass spectrometry (LC-MS) measurements has a significant role in the detection of biomarkers and in metabolomic studies.The machine drift causes differences between LC-MS measurements, and an accurate alignment of the shifts introduced to the same peptide or metabolite is needed. In this paper, we propose the use of genetic programming (GP) for multiple alignment of LC-MS data. The proposed approach consists of two main phases. The first phase is the peak matching where the peaks from different LC-MS maps (peak lists) are matched to allow the calculation of the retention time deviation. The second phase is to use GP for multiple alignment of the peak lists with respect to a reference. In this paper, GP is designed to perform multiple-output regression by using a special node in the tree which divides the output of the tree into multiple outputs. Finally, the peaks that show the maximum correlation after dewarping the retention times are selected to form a consensus aligned map.The proposed approach is tested on one proteomics and two metabolomics LC-MS datasets with different number of samples. The method is compared to several benchmark methods and the results show that the proposed approach outperforms these methods in three fractions of the protoemics dataset and the metabolomics dataset with a larger number of maps. Moreover, the results on the rest of the datasets are highly competitive with the other methods", notes = "EvoApplications2014 held in conjunction with EuroGP'2014, EvoCOP2014, EvoBIO2014, and EvoMusArt2014", } @InProceedings{Ahmed:2014:CEC, title = "A New {GP}-Based Wrapper Feature Construction Approach to Classification and Biomarker Identification", author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", pages = "2756--2763", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary programming, Biometrics, bioinformatics and biomedical applications", DOI = "doi:10.1109/CEC.2014.6900317", abstract = "Mass spectrometry (MS) is a technology used for identification and quantification of proteins and metabolites. It helps in the discovery of proteomic or metabolomic biomarkers, which aid in diseases detection and drug discovery. The detection of biomarkers is performed through the classification of patients from healthy samples. The mass spectrometer produces high dimensional data where most of the features are irrelevant for classification. Therefore, feature reduction is needed before the classification of MS data can be done effectively. Feature construction can provide a means of dimensionality reduction and aims at improving the classification performance. In this paper, genetic programming (GP) is used for construction of multiple features. Two methods are proposed for this objective. The proposed methods work by wrapping a Random Forest (RF) classifier to GP to ensure the quality of the constructed features. Meanwhile, five other classifiers in addition to RF are used to test the impact of the constructed features on the performance of these classifiers. The results show that the proposed GP methods improved the performance of classification over using the original set of features in five MS data sets.", notes = "WCCI2014", } @InProceedings{Ahmed:2014:GECCOa, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue", title = "Multiple feature construction for effective biomarker identification and classification using genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "249--256", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598292", DOI = "doi:10.1145/2576768.2598292", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Biomarker identification, i.e., detecting the features that indicate differences between two or more classes, is an important task in omics sciences. Mass spectrometry (MS) provide a high throughput analysis of proteomic and metabolomic data. The number of features of the MS data sets far exceeds the number of samples, making biomarker identification extremely difficult. Feature construction can provide a means for solving this problem by transforming the original features to a smaller number of high-level features. This paper investigates the construction of multiple features using genetic programming (GP) for biomarker identification and classification of mass spectrometry data. In this paper, multiple features are constructed using GP by adopting an embedded approach in which Fisher criterion and p-values are used to measure the discriminating information between the classes. This produces nonlinear high-level features from the low-level features for both binary and multi-class mass spectrometry data sets. Meanwhile, seven different classifiers are used to test the effectiveness of the constructed features. The proposed GP method is tested on eight different mass spectrometry data sets. The results show that the high-level features constructed by the GP method are effective in improving the classification performance in most cases over the original set of features and the low-level selected features. In addition, the new method shows superior performance in terms of biomarker detection rate.", notes = "Also known as \cite{2598292} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Ahmed:2014:GECCOcomp, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Prediction of detectable peptides in MS data using genetic programming", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, biological and biomedical applications: Poster", pages = "37--38", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598421", DOI = "doi:10.1145/2598394.2598421", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The use of mass spectrometry to verify and quantify biomarkers requires the identification of the peptides that can be detectable. In this paper, we propose the use of genetic programming (GP) to measure the detection probability of the peptides. The new GP method is tested and verified on two different yeast data sets with increasing complexity and shows improved performance over other state-of-art classification and feature selection algorithms.", notes = "Also known as \cite{2598421} Distributed at GECCO-2014.", } @Article{Ahmed:2014:CS, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng", title = "Improving Feature Ranking for Biomarker Discovery in Proteomics Mass Spectrometry Data using Genetic Programming", journal = "Connection Science", year = "2014", volume = "26", number = "3", pages = "215--243", keywords = "genetic algorithms, genetic programming, biomarker discovery, feature selection, classification", ISSN = "0954-0091", DOI = "doi:10.1080/09540091.2014.906388", size = "29 pages", abstract = "Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.", } @InProceedings{conf/seal/AhmedZPX14, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue", title = "Genetic Programming for Measuring Peptide Detectability", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#AhmedZPX14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "593--604", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @InProceedings{conf/evoW/AhmedZPX16, author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng and Bing Xue", title = "A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "106--122", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2016-03-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#AhmedZPX16", isbn13 = "978-3-319-31204-0", DOI = "doi:10.1007/978-3-319-31204-0_8", abstract = "Mass spectrometry is currently the most commonly used technology in biochemical research for proteomic analysis. The main goal of proteomic profiling using mass spectrometry is the classification of samples from different clinical states. This requires the identification of proteins or peptides (biomarkers) that are expressed differentially between different clinical states. However, due to the high dimensionality of the data and the small number of samples, classification of mass spectrometry data is a challenging task. Therefore, an effective feature manipulation algorithm either through feature selection or construction is needed to enhance the classification performance and at the same time minimise the number of features. Most of the feature manipulation methods for mass spectrometry data treat this problem as a single objective task which focuses on improving the classification performance. This paper presents two new methods for biomarker detection through multi-objective feature selection and feature construction. The results show that the proposed multi-objective feature selection method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. Moreover, the multi-objective feature construction algorithm further improves the performance over the multi-objective feature selection algorithm. This paper is the first multi-objective genetic programming approach for biomarker detection in mass spectrometry data", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @Article{ahmed:2021:Materials, author = "Waleed Ahmed and Hussien Hegab and Atef Mohany and Hossam Kishawy", title = "Analysis and Optimization of Machining Hardened Steel {AISI} 4140 with {Self-Propelled} Rotary Tools", journal = "Materials", year = "2021", volume = "14", number = "20", keywords = "genetic algorithms, genetic programming, modeling, machining, optimization, rotary tools", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/14/20/6106", DOI = "doi:10.3390/ma14206106", abstract = "It is necessary to improve the machinability of difficult-to-cut materials such as hardened steel, nickel-based alloys, and titanium alloys as these materials offer superior properties such as chemical stability, corrosion resistance, and high strength to weight ratio, making them indispensable for many applications. Machining with self-propelled rotary tools (SPRT) is considered one of the promising techniques used to provide proper tool life even under dry conditions. In this work, an attempt has been performed to analyse, model, and optimise the machining process of AISI 4140 hardened steel using self-propelled rotary tools. Experimental analysis has been offered to (a) compare the fixed and rotary tools performance and (b) study the effect of the inclination angle on the surface quality and tool wear. Moreover, the current study implemented some artificial intelligence-based approaches (i.e., genetic programming and NSGA-II) to model and optimise the machining process of AISI 4140 hardened steel with self-propelled rotary tools. The feed rate, cutting velocity, and inclination angle were the selected design variables, while the tool wear, surface roughness, and material removal rate (MRR) were the studied outputs. The optimal surface roughness was obtained at a cutting speed of 240 m/min, an inclination angle of 20?, and a feed rate of 0.1 mm/rev. In addition, the minimum flank tool wear was observed at a cutting speed of 70 m/min, an inclination angle of 10?, and a feed rate of 0.15 mm/rev. Moreover, different weights have been assigned for the three studied outputs to offer different optimised solutions based on the designer's interest (equal-weighted, finishing, and productivity scenarios). It should be stated that the findings of the current work offer valuable recommendations to select the optimised cutting conditions when machining hardened steel AISI 4140 within the selected ranges.", notes = "also known as \cite{ma14206106}", } @Article{AHMED:2023:isatra, author = "Umair Ahmed and Fakhre Ali and Ian Jennions", title = "Acoustic monitoring of an aircraft auxiliary power unit", journal = "ISA Transactions", year = "2023", ISSN = "0019-0578", DOI = "doi:10.1016/j.isatra.2023.01.014", URL = "https://www.sciencedirect.com/science/article/pii/S0019057823000149", keywords = "genetic algorithms, genetic programming, Aircraft, Auxiliary power unit, Condition monitoring, Acoustics, Signal processing, Machine learning, Sensors, Feature extraction, Fault detection, Microphones", abstract = "In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft's operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University's Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines", } @Article{Ahmed:JBHI, author = "Usman Ahmed and Jerry Chun-Wei Lin and Gautam Srivastava", journal = "IEEE Journal of Biomedical and Health Informatics", title = "Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease", year = "2023", abstract = "Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Using real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naive Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is used to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends.", keywords = "genetic algorithms, genetic programming, Diseases, Medical diagnostic imaging, Medical services, Heart, Predictive models, Machine learning, Decision trees, Artificial intelligence, Sensor readings, Heart disease, Long-term lung disease", DOI = "doi:10.1109/JBHI.2023.3270888", ISSN = "2168-2208", notes = "Also known as \cite{10121013}", } @Article{AHMED:2023:suscom, author = "Usman Ahmed and Jerry Chun-Wei Lin and Gautam Srivastava", title = "Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases", journal = "Sustainable Computing: Informatics and Systems", volume = "38", pages = "100868", year = "2023", ISSN = "2210-5379", DOI = "doi:10.1016/j.suscom.2023.100868", URL = "https://www.sciencedirect.com/science/article/pii/S2210537923000239", keywords = "genetic algorithms, genetic programming, Machine learning, Sensor data, Cardiovascular disease, Chronic respiratory disease. TPOT", abstract = "Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naive Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention", } @InProceedings{conf/fgit/AhnOO11, author = "Chang Wook Ahn and Sanghoun Oh and Moonyoung Oh", title = "A Genetic Programming Approach to Data Clustering", booktitle = "Proceedings of the International Conference on Multimedia, Computer Graphics and Broadcasting (MulGraB 2011) Part {II}", editor = "Tai-Hoon Kim and Hojjat Adeli and William I. Grosky and Niki Pissinou and Timothy K. Shih and Edward J. Rothwell and Byeong Ho Kang and Seung-Jung Shin", year = "2011", volume = "263", series = "Communications in Computer and Information Science", pages = "123--132", address = "Jeju Island, Korea", month = dec # " 8-10", publisher = "Springer", note = "Held as Part of the Future Generation Information Technology Conference, {FGIT} 2011, in Conjunction with {GDC} 2011", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-27186-1", DOI = "doi:10.1007/978-3-642-27186-1_15", size = "10 pages", abstract = "This paper presents a genetic programming (GP) to data clustering. The aim is to accurately classify a set of input data into their genuine clusters. The idea lies in discovering a mathematical function on clustering regularities and then use the rule to make a correct decision on the entities of each cluster. To this end, GP is incorporated into the clustering procedures. Each individual is represented by a parsing tree on the program set. Fitness function evaluates the quality of clustering with regard to similarity criteria. Crossover exchanges sub-trees between parental candidates in a positionally independent fashion. Mutation introduces (in part) a new sub-tree with a low probability. The variation operators (i.e., crossover, mutation) offer an effective search capability to obtain the improved quality of solution and the enhanced speed of convergence. Experimental results demonstrate that the proposed approach outperforms a well-known reference.", affiliation = "School of Information & Communication Engineering, Sungkyunkwan University, Suwon, 440-746 Korea", bibdate = "2011-12-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/fgit/mulgrab2011-2.html#AhnOO11", } @Article{Aho97, author = "Hannu Ahonen and Paulo A. {de Souza Jr.} and Vijayendra Kumar Garg", title = "A genetic algorithm for fitting Lorentzian line shapes in Mossbauer spectra", journal = "Nuclear Instruments and Methods in Physics Research B", year = "1997", volume = "124", pages = "633--638", month = "5 " # may, email = "souza@iacgu7.chemie.uni-mainz.de", keywords = "genetic algorithms", ISSN = "0168583X", abstract = "A genetic algorithm was implemented for finding an approximative solution to the problem of fitting a combination of Lorentzian lines to a measured Mossbauer spectrum. This iterative algorithm exploits the idea of letting several solutions (individuals) compete with each other for the opportunity of being selected to create new solutions (reproduction). Each solution was represend as a string of binary digits (chromossome). In addition, the bits in the new solutions may be switched randomly from zero to one or conversely (mutation). The input of the program that implements the genetic algorithm consists of the measured spectrum, the maximum velocity, the peak positions and the expected number of Lorentzian lines in the spectrum. Each line is represented with the help of three variables, which correspond to its intensity, full line width at hald maxima and peak position. An additional parameter was associated to the background level in the spectrum. A chi-2 test was used for determining the quality of each parameter combination (fitness). The results obtained seem to be very promising and encourage to further development of the algorithm and its implementation.", } @InProceedings{Ahsan:2020:IBCAST, author = "Usama Ahsan and Fayyaz ul Amir Afsar Minhas", booktitle = "2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST)", title = "{AutoQP:} Genetic Programming for Quantum Programming", year = "2020", pages = "378--382", abstract = "Quantum computing is a new era in the field of computation which makes use of quantum mechanical phenomena such as superposition, entanglement, and quantum annealing. It is a very promising field and has given a new paradigm to efficiently solve complex computational problems. However, programming quantum computers is a difficult task. In this research, we have developed a system called AutoQP which can write quantum computer code through genetic programming on a classical computer provided the input and expected output of a quantum program. We have tested AutoQP on two different quantum algorithms: Deutsch Problem and the Bernstein-Vazirani problem. In our experimental analysis, AutoQP was able to generate quantum programs for solving both problems. The code generated by AutoQP was successfully tested on actual IBM quantum computers as well. It is expected that the proposed system can be very useful for the general development of quantum programs based on the IBM gate model. The source code for the proposed system is available at the URL: https://github.com/usamaahsan93/AutoQP.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IBCAST47879.2020.9044554", ISSN = "2151-1411", month = jan, notes = "Also known as \cite{9044554}", } @Article{DBLP:journals/itiis/AhvanooeyLWW19, author = "Milad Taleby Ahvanooey and Qianmu Li and Ming Wu and Shuo Wang", title = "A Survey of Genetic Programming and Its Applications", journal = "{KSII} Trans. Internet Inf. Syst.", volume = "13", number = "4", pages = "1765--1794", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3837/tiis.2019.04.002", DOI = "doi:10.3837/tiis.2019.04.002", timestamp = "Thu, 25 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/itiis/AhvanooeyLWW19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Lifeng_Ai_Thesis, author = "Lifeng Ai", title = "{QoS-aware} web service composition using genetic algorithms", school = "Queensland University of Technology", year = "2011", address = "Australia", month = jun, keywords = "genetic algorithms, quality of service, web services, composite web services, optimisation", URL = "http://eprints.qut.edu.au/46666/1/Lifeng_Ai_Thesis.pdf", URL = "http://eprints.qut.edu.au/46666/", size = "pages", abstract = "Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition, by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers' expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.", notes = "also know as \cite{quteprints46666} ID Code: 46666 Supervisor: Tang, Maolin & Fidge, Colin", } @InProceedings{Aichour:2007:NICSO, author = "Malek Aichour and Evelyne Lutton", title = "Cooperative Co-evolution Inspired Operators for Classical GP Schemes", booktitle = "Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)", year = "2007", pages = "169--178", editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta", volume = "129", series = "Studies in Computational Intelligence", address = "Acireale, Italy", month = "8-10 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-78986-4", DOI = "doi:10.1007/978-3-540-78987-1_16", abstract = "This work is a first step toward the design of a cooperative-coevolution GP for symbolic regression, which first output is a selective mutation operator for classical GP. Cooperative co-evolution techniques rely on the imitation of cooperative capabilities of natural populations and have been successfully applied in various domains to solve very complex optimisation problems. It has been proved on several applications that the use of two fitness measures (local and global) within an evolving population allow to design more efficient optimization schemes. We currently investigate the use of a two-level fitness measurement for the design of operators, and present in this paper a selective mutation operator. Experimental analysis on a symbolic regression problem give evidence of the efficiency of this operator in comparison to classical subtree mutation", notes = "http://www.dmi.unict.it/nicso2007/ http://www.dmi.unict.it/nicso2007/NICSO2007-program.pdf", } @InProceedings{Ain:2022:ICDMW, author = "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", booktitle = "2022 IEEE International Conference on Data Mining Workshops (ICDMW)", title = "A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification", year = "2022", pages = "378--387", abstract = "Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes them expensive in real-time situations. Such methods achieve only moderate classification accuracy due to the limited model capacity and computational resources. In addition, most existing studies focus on improving classification accuracy using only raw images or the entire set of original attributes and remain unable to identify hidden patterns or causal information necessary to discriminate breast density classes. It is challenging to find high-quality knowledge when some attributes defining the data space are redundant or irrelevant. In this study, we present a novel attribute construction method using genetic programming (GP) for the task of breast density classification. To extract informative features from the raw mammographic images, wavelet decomposition, local binary patterns, and histogram of oriented gradients are used to include texture, local and global image properties. The study evaluates the goodness of the proposed method on two benchmark real-world mammographic image datasets and compares the results of the proposed GP method with eight conventional classification methods. The experimental results reveal that the proposed method significantly outperforms most of the commonly used classification methods in binary and multi-class classification tasks. Furthermore, the study shows the potential of G P for mammographic breast density classification by interpreting evolved attributes that highlight important breast density characteristics.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICDMW58026.2022.00057", ISSN = "2375-9259", month = nov, notes = "Also known as \cite{10031110}", } @Article{Ain:2022:ieeeTC, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming", year = "2022", abstract = "Developing a computer-aided diagnostic system for detecting various skin malignancies from images has attracted many researchers. Unlike many machine-learning approaches, such as artificial neural networks, genetic programming (GP) automatically evolves models with flexible representation. GP successfully provides effective solutions using its intrinsic ability to select prominent features (i.e., feature selection) and build new features (i.e., feature construction). Existing approaches have used GP to construct new features from the complete set of original features and the set of operators. However, the complete set of features may contain redundant or irrelevant features that do not provide useful information for classification. This study aims to develop a two-stage GP method, where the first stage selects prominent features, and the second stage constructs new features from these selected features and operators, such as multiplication in a wrapper approach to improve the classification performance. To include local, global, texture, color, and multiscale image properties of skin images, GP selects and constructs features extracted from local binary patterns and pyramid-structured wavelet decomposition. The accuracy of this GP method is assessed using two real-world skin image datasets captured from the standard camera and specialized instruments, and compared with commonly used classification algorithms, three state of the art, and an existing embedded GP method. The results reveal that this new approach of feature selection and feature construction effectively helps improve the performance of the machine-learning classification algorithms. Unlike other black-box models, the evolved models by GP are interpretable; therefore, the proposed method can assist dermatologists to identify prominent features, which has been shown by further analysis on the evolved models.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCYB.2022.3182474", ISSN = "2168-2275", notes = "Also known as \cite{9819829}", } @Article{AIN:2022:eswa, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Genetic programming for automatic skin cancer image classification", journal = "Expert Systems with Applications", volume = "197", pages = "116680", year = "2022", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2022.116680", URL = "https://www.sciencedirect.com/science/article/pii/S0957417422001634", keywords = "genetic algorithms, genetic programming, Image classification, Dimensionality reduction, Feature selection, Feature construction", abstract = "Developing a computer-aided diagnostic system for detecting various types of skin malignancies from images has attracted many researchers. However, analyzing the behaviors of algorithms is as important as developing new systems in order to establish the effectiveness of a system in real-time situations which impacts greatly how well it can assist the dermatologist in making a diagnosis. Unlike many machine learning approaches such as Artificial Neural Networks, Genetic Programming (GP) automatically evolves models with its dynamic representation and flexibility. This study aims at analyzing recently developed GP-based approaches to skin image classification. These approaches have used the intrinsic feature selection and feature construction ability of GP to effectively construct informative features from a variety of pre-extracted features. These features encompass local, global, texture, color and multi-scale image properties of skin images. The performance of these GP methods is assessed using two real-world skin image datasets captured from standard camera and specialized instruments, and compared with six commonly used classification algorithms as well as existing GP methods. The results reveal that these constructed features greatly help improve the performance of the machine learning classification algorithms. Unlike {"}black-box{"} algorithms like deep neural networks, GP models are interpretable, therefore, our analysis shows that these methods can help dermatologists identify prominent skin image features. Further, it can help researchers identify suitable feature extraction methods for images captured from a specific instrument. Being fast, these methods can be deployed for making a quick and effective diagnosis in actual clinic situations", } @InProceedings{ain:2023:GECCOcomp, author = "Qurrat Ul Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "A New Genetic Programming Representation for Feature Learning in Skin Cancer Detection", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "707--710", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature learning, feature extraction, melanoma detection, image classification: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590550", size = "4 pages", abstract = "The process of automatically extracting informative high-level features from skin cancer images is enhanced by integrating well-developed feature descriptors into learning algorithms. This paper develops a new genetic programming-based feature learning approach to automatically select and combine six well-developed descriptors to extract high-level features for skin cancer image classification. The new approach can automatically learn various global features for image classification. The experimental results show that the new approach achieves significantly better classification performance than the baseline approach and six commonly used feature descriptors on two real-world skin image datasets.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{ain:2023:AusDM, author = "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and Mengjie Zhang", title = "Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming", booktitle = "Australasian Conference on Data Science and Machine Learning, AusDM 2023", year = "2023", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-99-8696-5_18", DOI = "doi:10.1007/978-981-99-8696-5_18", notes = "Published in 2024", } @InProceedings{ain:2024:CEC, author = "Qurrat UI Ain and Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Exploring Genetic Programming Models in {Computer-Aided} Diagnosis of Skin Cancer Images", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Representation learning, Visualization, Computational modeling, Feature extraction, Skin, Lesions, Image Classification, Skin Cancer Detection", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612105", abstract = "Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-depth analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of using feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyses the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learnt during the evolutionary process.", notes = "also known as \cite{10612105} WCCI 2024", } @InProceedings{aiyarak:1997:GPtootn, author = "P. Aiyarak and A. S. Saket and M. C. Sinclair", title = "Genetic Programming Approaches for Minimum Cost Topology Optimisation of Optical Telecommunication Networks", booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1997", editor = "Ali Zalzala", pages = "415--420", address = "University of Strathclyde, Glasgow, UK", publisher_address = "Savoy Place, London, WC2R 0BL, UK", month = "1-4 " # sep, publisher = "IEE", email = "mcs@essex.ac.uk", keywords = "genetic algorithms, genetic programming, telecommunication networks, topology", ISBN = "0-85296-693-8", URL = "http://uk.geocities.com/markcsinclair/ps/galesia97_aiy.ps.gz", DOI = "doi:10.1049/cp:19971216", size = "6 pages", abstract = "This paper compares the relative efficiency of three approaches for the minimum-cost topology optimisation of the COST 239 European Optical Network (EON) using genetic programming. The GP was run for the central nine nodes using three approaches: relational function set, decision trees, and connected nodes. Only the best two, decision trees and connected nodes, were run for the full EON. The results are also compared with earlier genetic algorithm work on the EON.", notes = "Also known as \cite{681062} GALESIA'97", } @InProceedings{Ajcevic:2013:ISPA, author = "Milos Ajcevic and Andrea {De Lorenzo} and Agostino Accardo and Alberto Bartoli and Eric Medvet", booktitle = "8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013)", title = "A novel estimation methodology for tracheal pressure in mechanical ventilation control", year = "2013", month = "4-6 " # sep, address = "Trieste, Italy", pages = "695--699", keywords = "genetic algorithms, genetic programming, biomechanics, biomedical electronics, biomedical equipment, diseases, injuries, medical control systems, patient treatment, physiological models, air flow pressure, air flow properties, barotrauma, endotracheal tubes, estimation methodology, high-frequency percussive ventilation, mechanical ventilation control, nonconventional mechanical ventilatory strategy, pathological conditions, patient airway, patient treatment, state-of-the-art baseline models, tracheal pressure, ventilator circuit, volutrauma, Electron tubes, Lungs, Physiology, Pressure measurement, Testing, Ventilation", DOI = "doi:10.1109/ISPA.2013.6703827", size = "5 pages", abstract = "High-frequency percussive ventilation (HFPV) is a non-conventional mechanical ventilatory strategy which has proved useful in the treatment of a number of pathological conditions. HFPV usually involves the usage of endotracheal tubes (EET) connecting the ventilator circuit to the airway of the patient. The pressure of the air flow insufflated by HFPV must be controlled very accurately in order to avoid barotrauma and volutrauma. Since the actual tracheal pressure cannot be measured, a model for estimating such a pressure based on the EET properties and on the air flow properties that can actually be measured in clinical practice is necessary. In this work we propose a novel methodology, based on Genetic Programming, for synthesising such a model. We experimentally evaluated our models against the state-of-the-art baseline models, crafted by human experts, and found that our models for estimating tracheal pressure are significantly more accurate.", notes = "Also known as \cite{6703827}", } @PhdThesis{Ajcevic:thesis, author = "Milos Ajcevic", title = "Personalized setup of high frequency percussive ventilator by estimation of respiratory system viscoelastic parameters", school = "Universita degli studi di Trieste", year = "2013/2014", year = "2013", address = "Italy", keywords = "genetic algorithms, genetic programming, High Frequency Percussive Ventilation, Respiratory signal processing, Parameter identification", URL = "http://hdl.handle.net/10077/10976", URL = "https://www.openstarts.units.it/bitstream/10077/10976/1/Ajcevic_PhD.pdf", size = "95 pages", abstract = "High Frequency Percussive Ventilation (HFPV) is a non-conventional ventilatory modality which has proven highly effective in patients with severe gas exchange impairment. However, at the present time, HFPV ventilator provides only airway pressure measurement. The airway pressure measurements and gas exchange analysis are currently the only parameters that guide the physician during the HFPV ventilator setup and treatment monitoring. The evaluation of respiratory system resistance and compliance parameters in patients undergoing mechanical ventilation is used for lung dysfunctions detection, ventilation setup and treatment effect evaluation. Furthermore, the pressure measured by ventilator represents the sum of the endotracheal tube pressure drop and the tracheal pressure. From the clinical point of view, it is very important to take into account the real amount of pressure dissipated by endotracheal tube to avoid lung injury. HFPV is pressure controlled logic ventilation, thus hypoventilation and hyperventilation cases are possible because of tidal volume variations in function of pulmonary and endotracheal tube impedance. This thesis offers a new approach for HFPV ventilator setup in accordance with protective ventilatory strategy and optimization of alveolar recruitment using estimation of the respiratory mechanics parameters and endotracheal pressure drop. Respiratory system resistance and compliance parameters were estimated, firstly in vitro and successively in patients undergoing HFPV, applying least squares regression on Dorkin high frequency model starting from measured respiratory signals. The Blasius model was identified as the most adequate to estimate pressure drop across the endotracheal tube during HFPV. Beside measurement device was developed in order to measure respiratory parameters in patients undergoing HFPV. The possibility to tailor HFPV ventilator setup, using respiratory signals measurement and estimation of respiratory system resistance, compliance and endotracheal tube pressure drop, provided by this thesis, opens a new prospective to this particular ventilatory strategy, improving its beneficial effects and minimizing ventilator-induced lung damage.", notes = "In vitro estimation of tracheal pressure by Genetic programming Supervisor: Agostino Accardo ING-INF/06 10077_10976", } @Article{Ajibode:2020:A, author = "Adekunle Akinjobi Ajibode and Ting Shu and Zuohua Ding", title = "Evolving Suspiciousness Metrics From Hybrid Data Set for Boosting a Spectrum Based Fault Localization", journal = "IEEE Access", year = "2020", volume = "8", pages = "198451--198467", keywords = "genetic algorithms, genetic programming, Measurement, Debugging, Boosting, Debugging, fault localization, SBFL", DOI = "doi:10.1109/ACCESS.2020.3035413", ISSN = "2169-3536", abstract = "Spectrum Based Fault Localization (SBFL) uses different metrics called risk evaluation formula to guide and pinpoint faults in debugging process. The accuracy of a specific SBFL method may be limited by the used formulae and program spectra. However, it has been demonstrated recently that Genetic Programming could be used to automatically design formulae directly from the program spectra. Therefore, this article presents Genetic Programming approach for proposing risk evaluation formula with the inclusion of radicals to evolve suspiciousness metric directly from the program spectra. 92 faults from Unix utilities of SIR repository and 357 real faults from Defect4J repository were used. The approach combines these data sets, used 2percent of the total faults (113) to evolve the formulae and the remaining 7percent (336) to validate the effectiveness of the metrics generated by our approach. The proposed approach then uses Genetic Programming to run 30 evolution to produce different 30 metrics. The GP-generated metrics consistently out-performed all the classic formulae in both single and multiple faults, especially OP2 on average of 2.2percent in single faults and 3.4percent in multiple faults. The experiment results conclude that the combination of Hybrid data set and radical is a good technique to evolve effective formulae for spectra-based fault localization.", notes = "Also known as \cite{9247185}", } @InCollection{akalin:2002:DCOFSGGP, author = "Frederick R. Akalin", title = "Developing a Computer-Controller Opponent for a First-Person Simulation Game using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2002:gagp}", } @Article{akbari-alashti:2015:WRM, author = "Habib Akbari-Alashti and Omid Bozorg Haddad and Miguel A. Marino", title = "Application of Fixed Length Gene Genetic Programming {(FLGGP)} in Hydropower Reservoir Operation", journal = "Water Resources Management", year = "2015", volume = "29", number = "9", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-015-1003-1", DOI = "doi:10.1007/s11269-015-1003-1", } @InProceedings{Akbarzadeh:2008:fuzz, author = "Vahab Akbarzadeh and Alireza Sadeghian and Marcus V. {dos Santos}", title = "Derivation of Relational Fuzzy Classification Rules Using Evolutionary Computation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1689--1693", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1819-0", file = "FS0398.pdf", DOI = "doi:10.1109/FUZZY.2008.4630598", ISSN = "1098-7584", keywords = "genetic algorithms, genetic programming, constrained-syntax genetic programming, evolutionary computation, knowledge-based systems, mutation-based evolutionary algorithm, relational fuzzy classification rules, fuzzy set theory, knowledge based systems", abstract = "An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These two populations co-evolve to better classify the underlying dataset. Unlike other approaches that use fuzzification of continuous attributes of the dataset for discovering fuzzy classification rules, the system presented here fuzzifies the relational operators ``greater than'' and ``less than'' using evolutionary methods. For testing our system, the system is applied to the Iris dataset. Our experimental results show that our system outperforms previous evolutionary and non-evolutionary systems on accuracy of classification and derivation of interrelation between the attributes of the Iris dataset. The resulting fuzzy rules of the system can be directly used in knowledge-based systems.", notes = "Also known as \cite{4630598} WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Akbarzadeh:1997:jce, author = "M.-R. Akbarzadeh-T. and E. Tunstel and M. Jamshidi", title = "Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy", booktitle = "Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment", year = "1997", pages = "373--377", address = "Albuquerque, NM, USA", month = "26-29 " # apr, organisation = "WERC Waste-management Education & Research Consortium New Mexico State University Box 30001, Department WERC Las Cruces, NM 88003-8001, USA HSRC Great Plains/Rocky Mountain Hazardous Substance Research Center Kansas State University 101 Ward Hall Manhattan, KS 66506-2502, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf", size = "5 pages", abstract = "Genetic Algorithms (GA) and Genetic Programs (GP) are two of the most widely used evolution strategies for parameter optimisation of complex systems. GAs have shown a great deal of success where the representation space is a string of binary or real-valued numbers. At the same time, GP has demonstrated success with symbolic representation spaces and where structure among symbols is explored. This paper discusses weaknesses and strengths of GA and GP in search of a combined and more evolved optimization algorithm. This combination is especially attractive for problem domains with non-homogeneous parameters. In particular, a fuzzy logic membership function is represented by numerical strings, whereas rule-sets are represented by symbols and structural connectives. Two examples are provided which exhibit how GA and GP are best used in optimising robot performance in manipulating hazardous waste. The first example involves optimisation for a fuzzy controller for a flexible robot using GA and the second example illustrates usage of GP in optimizing an intelligent navigation algorithm for a mobile robot. A novel strategy for combining GA and GP is presented.", } @InProceedings{Akbarzadeh:1998:wcci, author = "M. R. Akbarzadeh-T. and E. Tunstel and K. Kumbla and M. Jamshidi", title = "Soft computing paradigms for hybrid fuzzy controllers: experiments and applications", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "1200--1205", volume = "2", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, neurocontrollers, fuzzy control, hierarchical systems, mobile robots, path planning, brushless DC motors, machine control, manipulators, soft computing paradigms, hybrid fuzzy controllers, neural networks, genetic programs, fuzzy logic-based schemes, added intelligence, adaptation, learning ability, direct drive motor, genetic algorithm-fuzzy hierarchical controller, flexible robot link, genetic programming-fuzzy behavior-based controller, mobile robot navigation task", ISBN = "0-7803-4863-X", URL = "http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/fieee98.pdf", URL = "http://ieeexplore.ieee.org/iel4/5612/15018/00686289.pdf?isNumber=15018", DOI = "doi:10.1109/FUZZY.1998.686289", size = "6 pages", abstract = "Neural networks (NN), genetic algorithms (GA), and genetic programs (GP) are often augmented with fuzzy logic-based schemes to enhance artificial intelligence of a given system. Such hybrid combinations are expected to exhibit added intelligence, adaptation, and learning ability. In the paper, implementation of three hybrid fuzzy controllers are discussed and verified by experimental results. These hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to a flexible robot link, and a GP-fuzzy behavior-based controller applied to a mobile robot navigation task. It is experimentally shown that all three architectures are capable of significantly improving the system response.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @Article{Akbarzadeh-T:2000:CEE, author = "M.-R. Akbarzadeh-T. and K. Kumbla and E. Tunstel and M. Jamshidi", title = "Soft computing for autonomous robotic systems", journal = "Computers and Electrical Engineering", volume = "26", pages = "5--32", year = "2000", number = "1", keywords = "genetic algorithms, genetic programming, Soft computing, Neural networks, Fuzzy logic, Robotic control, Articial intelligence", URL = "http://www.sciencedirect.com/science/article/B6V25-3Y6GXY5-2/1/6a6f9ff946815d4e95fe3884c98e74e5", URL = "http://citeseer.ist.psu.edu/373353.html", size = "28 pages", abstract = "Neural networks (NN), genetic algorithms (GA), and genetic programming (GP) are augmented with fuzzy logic-based schemes to enhance artificial intelligence of automated systems. Such hybrid combinations exhibit added reasoning, adaptation, and learning ability. In this expository article, three dominant hybrid approaches to intelligent control are experimentally applied to address various robotic control issues which are currently under investigation at the NASA Center for Autonomous Control Engineering. The hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to position control of a flexible robot link, and a GP-fuzzy behavior based controller applied to a mobile robot navigation task. Various strong characteristics of each of these hybrid combinations are discussed and used in these control architectures. The NN-fuzzy architecture takes advantage of NN for handling complex data patterns, the GA-fuzzy architecture uses the ability of GA to optimize parameters of membership functions for improved system response, and the GP-fuzzy architecture uses the symbolic manipulation capability of GP to evolve fuzzy rule-sets.", notes = "citeseer 373353 version not identical to published version", } @InProceedings{Akbarzadeh:2003:ICNAFIPS, author = "M.-R. Akbarzadeh-T. and I. Mosavat and S. Abbasi", title = "Friendship Modeling for Cooperative Co-Evolutionary Fuzzy Systems: A Hybrid GA-GP Algorithm", booktitle = "Proceedings of the 22nd International Conference of North American Fuzzy Information Processing Society, NAFIPS 2003", year = "2003", pages = "61--66", month = "24-26 " # jul, keywords = "genetic algorithms, genetic programming, Artificial neural networks, Chaos, Computational modelling, Convergence, Evolutionary computation, Fuzzy logic, Fuzzy systems, Genetic programming, Humans, Stochastic processes, cooperative systems, fuzzy systems, groupware, modelling, table lookup, time series, chaotic time series prediction, cooperative co-evolutionary fuzzy systems, friendship modeling, function evaluations, fuzzy lookup tables, hybrid GA-GP algorithm, membership functions, rules sets", DOI = "doi:10.1109/NAFIPS.2003.1226756", size = "6 pages", abstract = "A novel approach is proposed to combine the strengths of GA and GP to optimise rule sets and membership functions of fuzzy systems in a co-evolutionary strategy in order to avoid the problem of dual representation in fuzzy systems. The novelty of proposed algorithm is twofold. One is that GP is used for the structural part (Rule sets) and GA for the string part (Membership functions). The goal is to reduce/eliminate the problem of competing conventions by co-evolving pieces of the problem separately and then in combination. Second is exploiting the synergism between rules sets and membership functions by imitating the effect of 'matching' and friendship in cooperating teams of humans, thereby significantly reducing the number of function evaluations necessary for evolution. The method is applied to a chaotic time series prediction problem and compared with the standard fuzzy table look-up scheme. demonstrate several significant improvements with the proposed approach; specifically, four times higher fitness and more steady fitness improvements as compared with epochal improvements observed in GP.", } @Misc{Akiba:2024:GGP, author = "Takuya Akiba", title = "Evolutionary Optimization of Model Merging Recipes", booktitle = "2nd GECCO workshop on Graph-based Genetic Programming", year = "2024", editor = "Dennis G. Wilson and Roman Kalkreuth and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Yuri Lavinas", address = "Melbourne", series = "GECCO '24", month = "14 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Invited talk", keywords = "genetic algorithms, genetic programming, ANN, LLM", URL = "https://arxiv.org/abs/2403.13187", URL = "https://sakana.ai/evolutionary-model-merge/", URL = "https://graphgp.com/program/#evolutionary-optimization-of-model-merging-recipes", code_url = "https://github.com/SakanaAI/evolutionary-model-merge", notes = "Abstract only. No paper in GECCO 2024 proceedings. GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Akira:1999:AJ, author = "Yoshida Akira", title = "Multiple-Organisms Learning and Evolution by Genetic Programming", booktitle = "Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems", year = "1999", editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen", address = "School of Computer Science Australian Defence Force Academy, Canberra, Australia", month = "22-25 " # nov, email = "akira-yo@is.aist-nara.ac.jp", keywords = "genetic algorithms, genetic programming", notes = "Broken Nov 2011 http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html Nara Advanced Institute of Science and Technology http://www.f.ait.kyushu-u.ac.jp/achievements/pub1999.html", } @InProceedings{akira:2000:moelGP, author = "Yoshida Akira", title = "Intraspecific Evolution of Learning by Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "209--224", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_15", abstract = "Spatial dynamic pattern formations or trails can be observed in a simple square world where individuals move to look for scattered foods. They seem to show the emergence of co-operation, job separation, or division of territories when genetic programming controls the reproduction, mutation, crossing over of the organisms. We try to explain the co-operative behaviours among multiple organisms by means of density of organisms and their environment. Next, we add some interactions between organisms, and between organism and their environment to see that the more interaction make the convergence of intraspecific learning faster. At last, we study that MDL-based fitness evaluation is effective for improvement of generalisation of genetic programming.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{journals/ijossp/AkourM17, title = "Software Defect Prediction Using Genetic Programming and Neural Networks", author = "Mohammed Akour and Wasen Yahya Melhem", journal = "International Journal of Open Source Software and Processes", year = "2017", number = "4", volume = "8", pages = "32--51", keywords = "genetic algorithms, genetic programming, ANN, SBSE", ISSN = "1942-3926", DOI = "doi:10.4018/IJOSSP.2017100102", abstract = "This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.", notes = "Mohammed Akour (Department of Computer Information Systems, Yarmouk University, Irbid, Jordan) and Wasen Yahya Melhem (Yarmouk university, Irbid, Jordan)", } @Article{Al-Saati:2014:mosul, author = "Najla Akram Al-Saati and Taghreed Riyadh Alreffaee", title = "Software Effort Estimation Using Multi Expression Programming", journal = "AL-Rafidain Journal of Computer Sciences and Mathematics", year = "2014", volume = "11", number = "2", pages = "53--71", keywords = "genetic algorithms, genetic programming, Effort Estimation, Multi Expression Programming", publisher = "Mosul University", ISSN = "1815-4816", eissn = "2311-7990", URL = "https://csmj.mosuljournals.com/article_163756.html", URL = "https://csmj.mosuljournals.com/article__2d593a444328ad02601f0d083038e400163756.pdf", DOI = "doi:10.33899/csmj.2014.163756", size = "19 pages", abstract = "The process of finding a function that can estimate the effort of software systems is considered to be the most important and most complex process facing systems developers in the field of software engineering. The accuracy of estimating software effort forms an essential part of the software development phases. A lot of experts applied different ways to find solutions to this issue, such as the COCOMO and other methods. Recently, many questions have been put forward about the possibility of using Artificial Intelligence to solve such problems, different scientists made ​​several studies about the use of techniques such as Genetic Algorithms and Artificial Neural Networks to solve estimation problems. We use one of the Linear Genetic Programming methods (Multi Expression programming) which apply the principle of competition between equations encrypted within the chromosomes to find the best formula for resolving the issue of software effort estimation. As for to the test data, benchmark known datasets are employed taken from previous projects, the results are evaluated by comparing them with the results of Genetic Programming (GP) using different fitness functions. The gained results indicate the surpassing of the employed method in finding more efficient functions for estimating about 7 datasets each consisting of many projects.", notes = "In Arabic", } @Article{Akram:2017:ijrr, author = "Najla Akram AL-Saati and Taghreed Riyadh Alreffaee", title = "Using Multi Expression Programming in Software Effort Estimation", journal = "International Journal of Recent Research and Review", year = "2017", volume = "X", number = "2", pages = "1--10", month = jun, keywords = "genetic algorithms, genetic programming, Multi Expression Programming, SBSE, Software Effort, Estimation, Software Engineering", ISSN = "2277-8322", URL = "http://www.ijrrr.com/papers10-2/paper1-Using%20Multi%20Expression%20Programming%20in%20Software%20Effort%20Estimation.pdf", URL = "http://www.ijrrr.com/issues10-2.htm", size = "10 pages", abstract = "Estimating the effort of software systems is an essential topic in software engineering, carrying out an estimation process reliably and accurately for a software forms a vital part of the software development phases. Many researchers have used different methods and techniques hopping to find solutions to this issue, such techniques include COCOMO, SEER-SEM,SLIM and others. Recently, Artificial Intelligent techniques are being used to solve such problems; different studies have been issued focusing on techniques such as Neural Networks NN, Genetic Algorithms GA, and Genetic Programming GP. This work uses one of the linear variations of GP, namely: Multi Expression Programming (MEP) aiming to find the equation that best estimates the effort of software. Benchmark datasets (based on previous projects) are used learning and testing. Results are compared with those obtained by GP using different fitness functions. Results show that MEP is far better in discovering effective functions for the estimation of about 6 datasets each comprising several projects.", notes = "See also \cite{Akram:2018:arxiv}", } @Misc{Akram:2018:arxiv, author = "Najla Akram Al-Saati and Taghreed Riyadh Alreffaee", title = "Using Multi Expression Programming in Software Effort Estimation", howpublished = "arXiv", year = "2018", month = "30 " # apr, keywords = "genetic algorithms, genetic programming, SBSE, ANN, software effort, estimation, multi expression programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1805.html#abs-1805-00090", URL = "http://arxiv.org/abs/1805.00090", size = "10 pages", abstract = "Estimating the effort of software systems is an essential topic in software engineering, carrying out an estimation process reliably and accurately for a software forms a vital part of the software development phases. Many researchers have used different methods and techniques hopping to find solutions to this issue, such techniques include COCOMO, SEER-SEM,SLIM and others. Recently, Artificial Intelligent techniques are being used to solve such problems; different studies have been issued focusing on techniques such as Neural Networks NN, Genetic Algorithms GA, and Genetic Programming GP. This work uses one of the linear variations of GP, namely: Multi Expression Programming (MEP) aiming to find the equation that best estimates the effort of software. Benchmark datasets (based on previous projects) are used learning and testing. Results are compared with those obtained by GP using different fitness functions. Results show that MEP is far better in discovering effective functions for the estimation of about 6 datasets each comprising several projects.", notes = "Published as International Journal of Recent Research and Review, Vol. X, Issue 2, June 2017 ISSN 2277-8322 \cite{Akram:2017:ijrr}. journals/corr/abs-1805-00090", } @Article{Aksu:2012:AAPS, author = "Buket Aksu and Anant Paradkar and Marcel Matas and Ozgen Ozer and Tamer Guneri and Peter York", title = "Quality by Design Approach: Application of Artificial Intelligence Techniques of Tablets Manufactured by Direct Compression", journal = "AAPS PharmSciTech", year = "2012", volume = "13", number = "4", pages = "1138--1146", month = sep # "~06", keywords = "genetic algorithms, genetic programming, gene expression programming, artificial neural networks, ANNs, GEP, optimisation, quality by design (qbd)", DOI = "doi:10.1208/s12249-012-9836-x", URL = "http://dx.doi.org/10.1208/s12249-012-9836-x", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3513460", URL = "http://www.ncbi.nlm.nih.gov/pubmed/22956056", language = "English", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", oai = "oai:pubmedcentral.nih.gov:3513460", publisher = "American Association of Pharmaceutical Scientists", abstract = "The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. Quality by Design, mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the knowledge space is a summary of all process knowledge obtained during product development, and the design space is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasise the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modelling and optimisation of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause--effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimisation studies using artificial neural networks and neurofuzzy logic programs.", } @InProceedings{conf/ecsqaru/AkyolYE07, title = "A Genetic Programming Classifier Design Approach for Cell Images", author = "Aydin Akyol and Yusuf Yaslan and Osman Kaan Erol", booktitle = "Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU", bibdate = "2007-09-17", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ecsqaru/ecsqaru2007.html#AkyolYE07", publisher = "Springer", year = "2007", volume = "4724", editor = "Khaled Mellouli", isbn13 = "978-3-540-75255-4", pages = "878--888", series = "Lecture Notes in Computer Science", address = "Hammamet, Tunisia", month = oct # " 31 - " # nov # " 2", keywords = "genetic algorithms, genetic programming, cell classification, classifier design, pollen classification", DOI = "doi:10.1007/978-3-540-75256-1_76", abstract = "This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is evaluated on challenging test configurations. We reached at 96percent success rate on the average together with significant improvement in the speed of convergence.", notes = "cf email GP mailing list Mon, 24 Dec 2007 13:42:59 +0200 You may find MATLAB codes from my Web Page below: www3.itu.edu.tr/~okerol you may refer to: O.K. Erol, I.Eksin, 2006. A New Optimization Method: Big-Bang Big-Crunch, Advences in Engineering Software, Elsevier, Vol 37, No.2, 106-111 doi:10.1016/j.advengsoft.2005.04.005", } @Article{al-afandi:2021:Algorithms, author = "Jalal Al-Afandi and Andras Horvath", title = "Adaptive Gene Level Mutation", journal = "Algorithms", year = "2021", volume = "14", number = "1", keywords = "genetic algorithms, genetic programming, evolution strategies, adaptive mutation, evolutionary programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/14/1/16", DOI = "doi:10.3390/a14010016", size = "18 pages", abstract = "Genetic Algorithms are stochastic optimisation methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.", notes = "also known as \cite{a14010016} Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, 1083 Budapest, Hungary", } @MastersThesis{Al-Afeef:mastersthesis, author = "Ala' S. Al-Afeef", title = "Image Reconstructing in Electrical Capacitance Tomography of Manufacturing Processes Using Genetic Programming", school = "Al-Balqa Applied University", year = "2010", address = "Al-Salt, Jordan", month = jul, email = "alaa.afeef@gmail.com", keywords = "genetic algorithms, genetic programming, Image Reconstructing, Electrical Capacitance Tomography", URL = "https://sites.google.com/site/alaaalfeef/home/Alaa_afeef_Thesis_Final.pdf", size = "137", abstract = "Electrical capacitance tomography is considered the most attractive technique for industrial process imaging because of its low construction cost, safety, fast data acquisition , non-invasiveness, non-intrusiveness, simple structure, wide application field and suitability for most kinds of flask and vessels, however, the low accuracy of the reconstructed images is the main limitation of implementing an ECT system. In order to improve the imaging accuracy, one may 1) increase the number of measurements by raising number of electrodes, 2) improve the reconstruction algorithm so that more information can be extracted from the captured data, however, increasing the number of electrodes has a limited impact on the imaging accuracy improvement. This means that, in order to improve the reconstructed image, more accurate reconstruction algorithms must be developed. In fact, ECT image reconstruction is still an inefficiently resolved problem because of many limitations, mainly the Soft-field and Ill-condition characteristic of ECT. Although there are many algorithms to solve the image reconstruction problem, these algorithms are not yet able to present a single model that can relate between image pixels and capacitance measurements in a mathematical relationship. The originality of this thesis lies in introducing a new technique for solving the non-linear inverse problem in ECT based on Genetic Programming (GP) to handle the ECT imaging for conductive materials. GP is a technique that has not been applied to ECT. GP found to be efficient in dealing with the Non-linear relation between the measured capacitance and permittivity distribution in ECT. This thesis provides new implemented software that can handle the ECT based GP problem with a user-friendly interface. The developed simulation results are promising.", } @InProceedings{Al-Afeef:2010:ISDA, author = "Alaa Al-Afeef and Alaa F. Sheta and Adnan Al-Rabea", title = "Image reconstruction of a metal fill industrial process using Genetic Programming", booktitle = "10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010", year = "2010", pages = "12--17", address = "Cairo", month = "29 " # nov # "-1 " # dec, email = "alaa.afeef@gmail.com", keywords = "genetic algorithms, genetic programming, electrical capacitance tomography, ill-condition characteristic, image reconstruction, industrial process imaging, metal fill industrial process, soft-field characteristic, image reconstruction, industrial engineering, tomography, Process Tomography", isbn13 = "978-1-4244-8134-7", URL = "http://sites.google.com/site/alaaalfeef/home/8.pdf", DOI = "doi:10.1109/ISDA.2010.5687299", size = "6 pages", abstract = "Electrical Capacitance Tomography (ECT) is one of the most attractive technique for industrial process imaging because of its low construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. However, image reconstruction based ECT suffers many limitations. They include the Soft-field and Ill-condition characteristic of ECT. The basic idea of the ECT for image reconstruction for a metal fill problem is to model the image pixels as a function of the capacitance measurements. Developing this relationship represents a challenge for systems engineering community. In this paper, we presents our innovative idea on solving the non-linear inverse problem for conductive materials of the ECT using Genetic Programming (GP). GP found to be a very efficient algorithm in producing a mathematical model of image pixels in the form of Lisp expression. The reported results are promising.", notes = "Also known as \cite{5687299}", } @Book{AfeefBook2011, author = "Alaa Al-Afeef and Alaa Sheta and Adnan Rabea", title = "Image Reconstruction of a Manufacturing Process: A Genetic Programming Approach", publisher = "Lambert Academic Publishing", year = "2011", edition = "1", month = apr, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3844325690", URL = "https://www.morebooks.de/store/gb/book/image-reconstruction-of-a-manufacturing-process/isbn/978-3-8443-2569-0", URL = "http://www.amazon.co.uk/Image-Reconstruction-Manufacturing-Process-Programming/dp/3844325697", abstract = "Product Description Evolutionary Computation (EC) is one of the most attractive techniques in the area of Computer Science. EC includes Genetic Algorithms (GAs), Genetic Programming (GP), Evolutionary Strategy (ES) and Evolutionary Programming (EP). GP have been widely used to solve a variety of problems in image enhancement, analysis and segmentation. This book explores the use of GP as a powerful approach to solve the image reconstruction problem for Lost Foam Casting (LFC) manufacturing process. The data set was collected using the Electrical Capacitance Tomography (ECT) technique. ECT is one of the most attractive technique for industrial process imaging because of its low construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. GP found to be a very efficient algorithm in producing a mathematical model of image pixels in a form of Lisp expression. A Graphical User Interface (GUI) Toolbox based Matlab was developed to help analysing and visualising the reconstructed images based GP problem. The reported results are promising.", size = "100 pages", } @Article{Al-Bastaki:2010:JAI, title = "{GADS} and Reusability", author = "Y. Al-Bastaki and W. Awad", year = "2010", journal = "Journal of Artificial Intelligence", volume = "3", number = "2", pages = "67--77", keywords = "genetic algorithms, genetic programming, GADS, reusability", URL = "http://docsdrive.com/pdfs/ansinet/jai/2010/67-72.pdf", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19945450\&date=2010\&volume=3\&issue=2\&spage=67", ISSN = "19945450", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:8a4dfe5674530875df3b83ea84856118", publisher = "Asian Network for Scientific Information", size = "6 pages", abstract = "Genetic programming is a domain-independent method that genetically breeds population of computer programs to solve problems. Genetic programming is considered to be a machine learning technique used to optimise a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task. There are a number of representation methods to illustrate these programs, such as LISP expressions and integer lists. This study investigated the effectiveness of genetic programming in solving the symbolic regression problem where, the population programs are expressed as integer sequences rather than lisp expressions. This study also introduced the concept of reusable program to genetic algorithm for developing software.", notes = "BNF grammar, ADF, linear GP", } @InProceedings{Al-Hajj:2016:ICRERA, author = "Rami Al-Hajj and Ali Assi and Farhan Batch", booktitle = "2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)", title = "An evolutionary computing approach for estimating global solar radiation", year = "2016", pages = "285--290", month = "20-23 " # nov, address = "Birmingham, UK", keywords = "genetic algorithms, genetic programming, Decision support systems, Evolutionary computation, Hand-held computers, climatological data, evolutionary computation, global solar radiation", DOI = "doi:10.1109/ICRERA.2016.7884553", abstract = "This paper presents a non-linear regression model based on an evolutionary computing technique namely the genetic programming for estimating solar radiation. This approach aims to estimate the best formula that represents the function for estimating the global solar radiation on horizontals with respect to the measured climatological data. First, we present a reference approach to find one global formula that models the relation among the solar radiation amount and a set of weather factors. In the second step, we present an enhanced approach that consists of multi formulas of regression in a parallel structure. The performance of the proposed approaches has been evaluated using statistical analysis measures. The obtained results were promising and comparable to those obtained by other empirical and neural models conducted by other research groups.", notes = "Also known as \cite{7884553}", } @Article{al-hajj:2021:Processes, author = "Rami Al-Hajj and Ali Assi and Mohamad Fouad and Emad Mabrouk", title = "A Hybrid {LSTM-Based} Genetic Programming Approach for {Short-Term} Prediction of Global Solar Radiation Using Weather Data", journal = "Processes", year = "2021", volume = "9", number = "7", keywords = "genetic algorithms, genetic programming", ISSN = "2227-9717", URL = "https://www.mdpi.com/2227-9717/9/7/1187", DOI = "doi:10.3390/pr9071187", abstract = "The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance's consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models' outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.", notes = "also known as \cite{pr9071187}", } @InProceedings{DBLP:conf/acpr/Al-HelaliCXZ19, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", editor = "Shivakumara Palaiahnakote and Gabriella Sanniti di Baja and Liang Wang and Wei Qi Yan", title = "Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data", booktitle = "Pattern Recognition - 5th Asian Conference, {ACPR} 2019, Auckland, New Zealand, November 26-29, 2019, Revised Selected Papers, Part {II}", series = "Lecture Notes in Computer Science", volume = "12047", pages = "566--579", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-41299-9_44", DOI = "doi:10.1007/978-3-030-41299-9_44", timestamp = "Mon, 24 Feb 2020 18:06:33 +0100", biburl = "https://dblp.org/rec/conf/acpr/Al-HelaliCXZ19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/ausai/Al-Helali00Z19, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", editor = "Jixue Liu and James Bailey", title = "Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data", booktitle = "{AI} 2019: Advances in Artificial Intelligence - 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2-5, 2019, Proceedings", series = "Lecture Notes in Computer Science", volume = "11919", pages = "523--535", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-35288-2_42", DOI = "doi:10.1007/978-3-030-35288-2_42", timestamp = "Mon, 25 Nov 2019 16:31:45 +0100", biburl = "https://dblp.org/rec/conf/ausai/Al-Helali00Z19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Al-Helali:2019:SSCI, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data", year = "2019", pages = "2395--2402", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9002861", abstract = "Dealing with missing values is one of the challenges in symbolic regression on many real-world data sets. One of the popular approaches to address this challenge is to use imputation. Traditional imputation methods are usually performed based on the predictive features without considering the original target variable. In this work, a genetic programming-based wrapper imputation method is proposed, which wrappers a regression method to consider the target variable when constructing imputation models for the incomplete features. In addition to the imputation performance, the regression performance is considered for evaluating the imputation models. Genetic programming (GP) is used for building the imputation models and decision tree (DT) is used for evaluating the regression performance during the GP evolutionary process. The experimental results show that the proposed method has a significant advance in enhancing the symbolic regression performance compared with some state-of- the-art imputation methods.", notes = "Also known as \cite{9002861}", } @InProceedings{Al-Helali:2020:SSCI, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Data Imputation for Symbolic Regression with Missing Values: A Comparative Study", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2093--2100", abstract = "Symbolic regression via genetic programming is considered as a crucial machine learning tool for empirical modelling. However, in reality, it is common for real-world data sets to have some data quality problems such as noise, outliers, and missing values. Although several approaches can be adopted to deal with data incompleteness in machine learning, most studies consider the classification tasks, and only a few have considered symbolic regression with missing values. In this work, the performance of symbolic regression using genetic programming on real-world data sets that have missing values is investigated. This is done by studying how different imputation methods affect symbolic regression performance. The experiments are conducted using thirteen real-world incomplete data sets with different ratios of missing values. The experimental results show that although the performance of the imputation methods differs with the data set, CART has a better effect than others. This might be due to its ability to deal with categorical and numerical variables. Moreover, the superiority of the use of imputation methods over the commonly used deletion strategy is observed.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308216", month = dec, notes = "Also known as \cite{9308216}", } @InProceedings{Al-Helali:2020:EuroGP, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "1--17", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Symbolic regression, Incomplete data, Feature selection, Imputation, Model complexity", isbn13 = "978-3-030-44093-0", video_url = "https://www.youtube.com/watch?v=zeZvFJElkBM", DOI = "doi:10.1007/978-3-030-44094-7_1", abstract = "Missing values bring several challenges when learning from real-world data sets. Imputation is a widely adopted approach to estimating missing values. However, it has not been adequately investigated in symbolic regression. When imputing the missing values in an incomplete feature, the other features that are used in the prediction process are called imputation predictors. In this work, a method for imputation predictor selection using regularized genetic programming (GP) models is presented for symbolic regression tasks on incomplete data. A complexity measure based on the Hessian matrix of the phenotype of the evolving models is proposed. It is employed as a regularizer in the fitness function of GP for model selection and the imputation predictors are selected from the selected models. In addition to the baseline which uses all the available predictors, the proposed selection method is compared with two GP-based feature selection variations: the standard GP feature selector and GP with feature selection pressure. The trends in the results reveal that in most cases, using the predictors selected by regularized GP models could achieve a considerable reduction in the imputation error and improve the symbolic regression performance as well.", notes = "fitness = error + lamda * complexity. (Lamda fixed linear weighting) Model complexity based on second order partial derivaties (not tree size). Form n by n Hessian matrix, where n is number of inputs (terminal set size). Each element is 2nd order partial dertivative of GP tree with respect to input i and input j. Hessian is symetric matrix. Matrix C is Hessian with all constant terms set to zero. Complexity = determinant of C divided by the determinant of H. (calculated with python package SymPy). Division replaced by analytic quotiant \cite{Ni:2012:ieeeTEC}. Data missing at random. Five OpenML benchmarks. Compare python DEAP with Linear Regression, PMM, KNN (R package Simpulation). http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Al-Helali:2020:CEC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Genetic Programming with Noise Sensitivity for Imputation Predictor Selection in Symbolic Regression with Incomplete Data", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24344", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185526", abstract = "This paper presents a feature selection method that incorporates a sensitivity-based single feature importance measure in a context-based feature selection approach. The single-wise importance is based on the sensitivity of the learning performance with respect to adding noise to the predictive features. Genetic programming is used as a context-based selection mechanism, where the selection of features is determined by the change in the performance of the evolved genetic programming models when the feature is injected with noise. Imputation is a key strategy to mitigate the data incompleteness problem. However, it has been rarely investigated for symbolic regression on incomplete data. In this work, an attempt to contribute to filling this gap is presented. The proposed method is applied to selecting imputation predictors (features/variables) in symbolic regression with missing values. The evaluation is performed on real-world data sets considering three performance measures: imputation accuracy, symbolic regression performance, and features' reduction ability. Compared with the benchmark methods, the experimental evaluation shows that the proposed method can achieve an enhanced imputation, improve the symbolic regression performance, and use smaller sets of selected predictors.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185526}", } @InProceedings{Al-Helali:2020:CEC2, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming-based Transformation for Transfer Learning in Symbolic Regression with Highly Incomplete Data", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24250", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185670", abstract = "Transfer learning has been considered a key solution for the problem of learning when there is a lack of knowledge in some target domains. Its idea is to benefit from the learning on different (but related in some way) domains that have adequate knowledge and transfer what can improve the learning in the target domains. Although incompleteness is one of the main causes of knowledge shortage in many machine learning real-world tasks, it has received a little effort to be addressed by transfer learning. In particular, to the best of our knowledge, there is no single study to use transfer learning for the symbolic regression task when the underlying data are incomplete. The current work addresses this point by presenting a transfer learning method for symbolic regression on data with high ratios of missing values. A multi-tree genetic programming algorithm based feature-based transformation is proposed for transferring data from a complete source domain to a different, incomplete target domain. The experimental work has been conducted on real-world data sets considering different transfer learning scenarios each is determined based on three factors: missingness ratio, domain difference, and task similarity. In most cases, the proposed method achieved positive transductive transfer learning in both homogeneous and heterogeneous domains. Moreover, even with less significant success, the obtained results show the applicability of the proposed approach for inductive transfer learning.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand", } @InProceedings{Al-Helali:2020:GECCO, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming for Feature Construction-Based Domain Adaptation in Symbolic Regression with Incomplete Data", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390160", DOI = "doi:10.1145/3377930.3390160", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "913--921", size = "9 pages", keywords = "genetic algorithms, genetic programming, transfer tearning, incomplete data, symbolic regression", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Nowadays, transfer learning has gained a rapid popularity in tasks with limited data available. While traditional learning limits the learning process to knowledge available in a specific (target) domain, transfer learning can use parts of knowledge extracted from learning in a different (source) domain to help learning in the target domain. This concept is of special importance when there is a lack of knowledge in the target domain. Consequently, since data incompleteness is a serious cause of knowledge shortage in real-world learning tasks, it can be typically addressed using transfer learning. One way to achieve that is feature construction-based domain adaptation. However, although it is considered as a powerful feature construction algorithm, Genetic Programming has not been fully for domain adaptation. In this work, a multi-tree genetic programming method is proposed for feature construction-based domain adaptation. The main idea is to construct a transformation from the source feature space to the target feature space, which maps the source domain close to the target domain. This method is used for symbolic regression with missing values. The experimental work shows encouraging potential of the proposed approach when applied to real-world tasks considering different transfer learning scenarios.", notes = "Nominated for Best Paper. multi-tree GP. R packages. Missing data created randomly. Also known as \cite{10.1145/3377930.3390160} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{DBLP:journals/soco/Al-Helali00021, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "A new imputation method based on genetic programming and weighted {KNN} for symbolic regression with incomplete data", journal = "Soft Computing", volume = "25", number = "8", pages = "5993--6012", year = "2021", month = apr, keywords = "genetic algorithms, genetic programming, Symbolic regression, Incomplete data, KNN, Imputation", ISSN = "1432-7643", URL = "https://doi.org/10.1007/s00500-021-05590-y", DOI = "doi:10.1007/s00500-021-05590-y", timestamp = "Wed, 07 Apr 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/soco/Al-Helali00021.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task and only a limited number of studies for symbolic regression with missing values exist. a new imputation method for symbolic regression with incomplete data is proposed. The method aims to improve both the effectiveness and efficiency of imputing missing values for symbolic regression. This method is based on genetic programming (GP) and weighted K-nearest neighbors (KNN). It constructs GP-based models using other available features to predict the missing values of incomplete features. The instances used for constructing such models are selected using weighted KNN. The experimental results on real-world data sets show that the proposed method outperforms a number of state-of-the-art methods with respect to the imputation accuracy, the symbolic regression performance, and the imputation time.", } @Article{Al-Helali:ieeeTEC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Multi-Tree Genetic Programming with New Operators for Transfer Learning in Symbolic Regression with Incomplete Data", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "6", pages = "1049--1063", month = dec, keywords = "genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Transfer Learning, Evolutionary Learning", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3079843", size = "15 pages", abstract = "Lack of knowledge is a common consequence of data incompleteness when learning from real-world data. To deal with such a situation, this work uses transfer learning to re-use knowledge from different (yet related) but complete domains. Due to its powerful feature construction ability, genetic programming is used to construct feature-based transformations that map the feature space of the source domain to that of the target domain such that their differences are reduced. Particularly, this work proposes a new multi-tree genetic programming-based feature construction approach to transfer learning in symbolic regression with missing values. It transfers knowledge related to the importance of the features and instances in the source domain to the target domain to improve the learning performance. Moreover, new genetic operators are developed to encourage minimising the distribution discrepancy between the transformed domain and the target domain. A new probabilistic crossover is developed to make the well-constructed trees in the individuals more likely to be mated than the other trees. A new mutation operator is designed to give more probability for the poorly-constructed trees to be mutated. The experimental results show that the proposed method not only achieves better performance compared with different traditional learning methods but also advances two recent transfer learning methods on real-world data sets with various incompleteness and learning scenarios.", notes = "also known as \cite{9429709}", } @InProceedings{DBLP:conf/ausai/Al-Helali00Z20, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", editor = "Marcus Gallagher and Nour Moustafa and Erandi Lakshika", title = "Genetic Programming-Based Selection of Imputation Methods in Symbolic Regression with Missing Values", booktitle = "{AI} 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, {AI} 2020, Canberra, ACT, Australia, November 29-30, 2020, Proceedings", series = "Lecture Notes in Computer Science", volume = "12576", pages = "163--175", publisher = "Springer", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-64984-5_13", DOI = "doi:10.1007/978-3-030-64984-5_13", timestamp = "Mon, 15 Feb 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/conf/ausai/Al-Helali00Z20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Al-Helali:2021:CEC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "{GP} with a Hybrid Tree-vector Representation for Instance Selection and Symbolic Regression on Incomplete Data", year = "2021", editor = "Yew-Soon Ong", pages = "604--611", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Data incompleteness is a pervasive problem in symbolic regression, and machine learning in general. Unfortunately, most symbolic regression methods are only applicable when the given data is complete. One common approach to handling this situation is data imputation. It works by estimating missing values based on existing data. However, which existing data should be used for imputing the missing values? The answer to this question is important when dealing with incomplete data. To address this question, this work proposes a mixed tree-vector representation for genetic programming to perform instance selection and symbolic regression on incomplete data. In this representation, each individual has two components: an expression tree and a bit vector. While the tree component constructs symbolic regression models, the vector component selects the instances that are used to impute missing values by the weighted k-nearest neighbour (WKNN) imputation method. The complete imputed instances are then used to evaluate the GP-based symbolic regression model. The obtained experimental results show the applicability of the proposed method on real-world data sets with different missingness scenarios. When compared with existing methods, the proposed method not only produces more effective symbolic regression models but also achieves more efficient imputations.", keywords = "genetic algorithms, genetic programming, Computational modeling, Machine learning, Evolutionary computation, Regression tree analysis, Symbolic Regression, Incomplete Data, Imputation, Instance Selection", DOI = "doi:10.1109/CEC45853.2021.9504767", notes = "Also known as \cite{9504767}", } @Article{Al-Helali:ETCI, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", title = "Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression", year = "2024", volume = "8", number = "3", pages = "2269--2282", month = jun, keywords = "genetic algorithms, genetic programming, Feature extraction, Data models, Computational modelling, Task analysis, Predictive models, Machine learning, Feature selection, high dimensionality, symbolic regression", ISSN = "2471-285X", DOI = "doi:10.1109/TETCI.2024.3369407", abstract = "Symbolic regression is increasingly important for discovering mathematical models for various prediction tasks. It works by searching for the arithmetic expressions that best represent a target variable using a set of input features. However, as the number of features increases, the search process becomes more complex. To address high-dimensional symbolic regression, this work proposes a genetic programming for feature selection method based on the impact of feature removal on the performance of SR models. Unlike existing Shapely value methods that simulate feature absence at the data level, the proposed approach suggests removing features at the model level. This approach circumvents the production of unrealistic data instances, which is a major limitation of Shapely value and permutation-based methods. Moreover, after calculating the importance of the features, a cut-off strategy, which works by injecting a number of random features and using their importance to automatically set a threshold, is proposed for selecting important features. The experimental results on artificial and real-world high-dimensional data sets show that, compared with state-of-the-art feature selection methods using the permutation importance and Shapely value, the proposed method not only improves the SR accuracy but also selects smaller sets of features.", notes = "Also known as \cite{10466603}", } @Article{Al-Helali:CYB, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Cybernetics", title = "Multitree Genetic Programming With Feature-Based Transfer Learning for Symbolic Regression on Incomplete Data", year = "2024", volume = "54", number = "7", pages = "4014--4027", month = jul, keywords = "genetic algorithms, genetic programming, Task analysis, Feature extraction, Data models, Transfer learning, Contracts, Adaptation models, Routing, incomplete data, symbolic regression (SR), transfer learning (TL)", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2023.3270319", abstract = "Data incompleteness is a serious challenge in real-world machine-learning tasks. Nevertheless, it has not received enough attention in symbolic regression (SR). Data missingness exacerbates data shortage, especially in domains with limited available data, which in turn limits the learning ability of SR algorithms. Transfer learning (TL), which aims to transfer knowledge across tasks, is a potential solution to solve this issue by making amends for the lack of knowledge. However, this approach has not been adequately investigated in SR. To fill this gap, a multitree genetic programming-based TL method is proposed in this work to transfer knowledge from complete source domains (SDs) to incomplete related target domains (TDs). The proposed method transforms the features from a complete SD to an incomplete TD. However, having many features complicates the transformation process. To mitigate this problem, we integrate a feature selection mechanism to eliminate unnecessary transformations. The method is examined on real-world and synthetic SR tasks with missing values to consider different learning scenarios. The obtained results not only show the effectiveness of the proposed method but also show its training efficiency compared with the existing TL methods. Compared to state-of-the-art methods, the proposed method reduced an average of more than 2.58percent and 4percent regression error on heterogeneous and homogeneous domains, respectively.", notes = "Also known as \cite{10120936}", } @Article{Al-Helali:EC, author = "Baligh Al-Helali and Qi Chen and Bing Xue and Mengjie Zhang", title = "Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data", journal = "Evolutionary Computation", month = nov # " 21 2024", note = "Just Accepted", keywords = "genetic algorithms, genetic programming, Symbolic Regression, Incomplete Data, Feature Selection, High-dimensionality", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00362", } @PhdThesis{WaleedAljandal2009, author = "Waleed A. Aljandal", title = "Itemset size-sensitive interestingness measures for association rule mining and link prediction", school = "Department of Computing and Information Sciences, Kansas State University", year = "2009", address = "Manhattan, Kansas, USA", month = may, keywords = "genetic algorithms, data Mining, Association Rule, Interestingness Measures, Link Prediction", URL = "https://krex.k-state.edu/dspace/handle/2097/1245", URL = "https://krex.k-state.edu/dspace/bitstream/handle/2097/1245/WaleedAljandal2009.pdf", size = "144 pages", abstract = "Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is to discover factors from data that can improve the precision, recall, and accuracy of association rules found using interestingness measures and frequent itemset mining. Such factors can be calibrated using validation data and applied to rank candidate rules in domain-dependent tasks such as link existence prediction. In addition, I use interestingness measures themselves as numerical features to improve link existence prediction. The focus of this dissertation is on developing and testing an analytical framework for association rule interestingness measures, to make them sensitive to the relative size of itemsets. I survey existing interestingness measures and then introduce adaptive parametric models for normalizing and optimizing these measures, based on the size of itemsets containing a candidate pair of co-occurring entities. The central thesis of this work is that in certain domains, the link strength between entities is related to the rarity of their shared memberships (i.e., the size of itemsets in which they co-occur), and that a data-driven approach can capture such properties by normalizing the quantitative measures used to rank associations. To test this hypothesis under different levels of variability in itemset size, I develop several test bed domains, each containing an association rule mining task and a link existence prediction task. The definitions of itemset membership and link existence in each domain depend on its local semantics. My primary goals are: to capture quantitative aspects of these local semantics in normalization factors for association rule interestingness measures; to represent these factors as quantitative features for link existence prediction, to apply them to significantly improve precision and recall in several real-world domains; and to build an experimental framework for measuring this improvement, using information theory and classification-based validation.", notes = "Not on GP Supervisor William H. Hsu", } @Article{ThunderStormGP, author = "Ruba Al-Jundi and Mais Yasen and Nailah Al-Madi", title = "Thunderstorms Prediction using Genetic Programming", journal = "International Journal of Information Systems and Computer Sciences", year = "2018", volume = "7", number = "1", note = "Special Issue of ICSIC 2017, Held during 23-24 September 2017 in Amman Arab University, Amman, Jordan", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Machine Learning, Weather Prediction.", publisher = "WARSE", ISSN = "2319-7595", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Thunderstorm_Prediction.pdf", URL = "http://www.warse.org/IJISCS/static/pdf/Issue/icsic2017sp14.pdf", size = "7 pages", abstract = "Thunderstorms prediction is a major challenge for efficient flight planning and air traffic management. As the inaccurate forecasting of weather poses a danger to aviation, it increases the need to build a good prediction model. Genetic Programming (GP) is one of the evolutionary computation techniques that is used for classification process. Genetic Programming has proven its efficiency especially for dynamic and nonlinear classification. This research proposes a thunderstorm prediction model that makes use of Genetic Programming and takes real data of Lake Charles Airport (LCH) as a case study. The proposed model is evaluated using different metrics such as recall, F-measure and compared with other well-known classifiers. The results show that Genetic Programming got higher recall value of predicting thunderstorms in comparison with the other classifiers.", notes = "Lake Charles Metar and SYNOP data (LCH) broken aug 2018 http://www.warse.org/IJISCS/archives", } @InProceedings{Al-Madi:2012:NaBIC, author = "N. Al-Madi and S. A. Ludwig", booktitle = "Proceedings of the Fourth World Congress on Nature and Biologically Inspired Computing, NaBIC 2012", title = "Adaptive genetic programming applied to classification in data mining", year = "2012", pages = "79--85", keywords = "genetic algorithms, genetic programming, data mining, pattern classification, adaptive GP, adaptive genetic programming, classification accuracies, crossover rates, data mining, mutation rates, Accuracy, Evolutionary computation, Sociology, Standards, Statistics, Adaptive Genetic Programming, Classification, Evolutionary Computation", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Adaptive_Genetic_Programming_applied_to_Classification_in_Data_Mining.pdf", DOI = "doi:10.1109/NaBIC.2012.6402243", abstract = "Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems, however, it suffers from a long run time. In addition, there are many parameters that need to be set before the GP is run. In this paper, we propose an adaptive GP that automatically determines the best parameters of a run, and executes the classification faster than standard GP. This adaptive GP has three variations. The first variant consists of an adaptive selection process ensuring that the produced solutions in the next generation are better than the solutions in the previous generation. The second variant adapts the crossover and mutation rates by modifying the probabilities ensuring that a solution with a high fitness is protected. And the third variant is an adaptive function list that automatically changes the functions used by deleting the functions that do not favourably contribute to the classification. These proposed variations were implemented and compared to the standard GP. The results show that a significant speedup can be achieved by obtaining similar classification accuracies.", notes = "Also known as \cite{6402243}", } @InProceedings{Al-Madi:2013:SSCI, author = "Nailah Al-Madi and Simone A. Ludwig", title = "Improving genetic programming classification for binary and multiclass datasets", booktitle = "IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013", year = "2013", editor_ssci-2013 = "P. N. Suganthan", editor = "Barbara Hammer and Zhi-Hua Zhou and Lipo Wang and Nitesh Chawla", pages = "166--173", address = "Singapore", month = "16-19 " # apr, keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Classification, Multiclass, Binary Classification", isbn13 = "978-1-4673-5895-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/improving_GP.pdf", DOI = "doi:10.1109/CIDM.2013.6597232", size = "8 pages", abstract = "Genetic Programming (GP) is one of the evolutionary computation techniques that is used for the classification process. GP has shown that good accuracy values especially for binary classifications can be achieved, however, for multiclass classification unfortunately GP does not obtain high accuracy results. In this paper, we propose two approaches in order to improve the GP classification task. One approach (GP-K) uses the K-means clustering technique in order to transform the produced value of GP into class labels. The second approach (GP-D) uses a discretization technique to perform the transformation. A comparison of the original GP, GP-K and GP-D was conducted using binary and multiclass datasets. In addition, a comparison with other state-of-the-art classifiers was performed. The results reveal that GP-K shows good improvement in terms of accuracy compared to the original GP, however, it has a slightly longer execution time. GP-D also achieves higher accuracy values than the original GP as well as GP-K, and the comparison with the state-of-the-art classifiers reveal competitive accuracy values.", notes = "CIDM 2013, Broken March 2023 http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/CIDM2013.htm Also known as \cite{6597232}", } @InProceedings{AL-Madi:2013:GECCOcomp, author = "Nailah Al-Madi and Simone A. Ludwig", title = "Segment-based genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "133--134", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Segment-Based_Genetic_Programming.pdf", DOI = "doi:10.1145/2464576.2464648", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP.", notes = "Also known as \cite{2464648} Distributed at GECCO-2013.", } @InProceedings{Al-Madi:2013:nabic, author = "Nailah Al-Madi and Simone A. Ludwig", title = "Scaling Genetic Programming for Data Classification using {MapReduce} Methodology", booktitle = "5th World Congress on Nature and Biologically Inspired Computing", year = "2013", editor = "Simone Ludwig and Patricia Melin and Ajith Abraham and Ana Maria Madureira and Kendall Nygard and Oscar Castillo and Azah Kamilah Muda and Kun Ma and Emilio Corchado", pages = "132--139", address = "Fargo, USA", month = "12-14 " # aug, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Evolutionary computation, data classification, Parallel Processing, MapReduce, Hadoop", isbn13 = "978-1-4799-1415-9", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/MRGP.pdf", URL = "http://www.mirlabs.net/nabic13/proceedings/html/paper34.xml", DOI = "doi:10.1109/NaBIC.2013.6617851", size = "8 pages", abstract = "Genetic Programming (GP) is an optimisation method that has proved to achieve good results. It solves problems by generating programs and applying natural operations on these programs until a good solution is found. GP has been used to solve many classifications problems, however, its drawback is the long execution time. When GP is applied on the classification task, the execution time proportionally increases with the dataset size. Therefore, to manage the long execution time, the GP algorithm is parallelised in order to speed up the classification process. Our GP is implemented based on the MapReduce methodology (abbreviated as MRGP), in order to benefit from the MapReduce concept in terms of fault tolerance, load balancing, and data locality. MRGP does not only accelerate the execution time of GP for large datasets, it also provides the ability to use large population sizes, thus finding the best result in fewer numbers of generations. MRGP is evaluated using different population sizes ranging from 1,000 to 100,000 measuring the accuracy, scalability, and speedup", notes = "USB only?, IEEE Catalog Number: CFP1395H-POD Also known as \cite{6617851}", } @PhdThesis{Al-Madi:thesis, author = "Nailah Shikri Al-Madi", title = "Improved genetic programming techniques for data classification", school = "Computer Science, North Dakota State University", year = "2013", address = "Fargo, North Dakota, USA", month = dec, keywords = "genetic algorithms, genetic programming, Artificial intelligence, Computer science, Applied sciences, Data classification, Data mining, MRGP", URL = "https://library.ndsu.edu/ir/handle/10365/27097", URL = "https://library.ndsu.edu/ir/bitstream/handle/10365/27097/Improved%20Genetic%20Programming%20Techniques%20For%20Data%20Classification.pdf", broken = "http://gradworks.umi.com/36/14/3614489.html", URL = "http://search.proquest.com/docview/1518147523", size = "123 pages", abstract = "Evolutionary algorithms are one category of optimisation techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. Data mining is a relatively broad field that deals with the automatic knowledge discovery from databases and it is one of the most developed fields in the area of artificial intelligence. Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems. GP solves classification problems as an optimization tasks, where it searches for the best solution with highest accuracy. However, GP suffers from some weaknesses such as long execution time, and the need to tune many parameters for each problem. Furthermore, GP can not obtain high accuracy for multiclass classification problems as opposed to binary problems. In this dissertation, we address these drawbacks and propose some approaches in order to overcome them. Adaptive GP variants are proposed in order to automatically adapt the parameter settings and shorten the execution time. Moreover, two approaches are proposed to improve the accuracy of GP when applied to multiclass classification problems. In addition, a Segment-based approach is proposed to accelerate the GP execution time for the data classification problem. Furthermore, a parallelisation of the GP process using the MapReduce methodology was proposed which aims to shorten the GP execution time and to provide the ability to use large population sizes leading to a faster convergence. The proposed approaches are evaluated using different measures, such as accuracy, execution time, sensitivity, specificity, and statistical tests. Comparisons between the proposed approaches with the standard GP, and with other classification techniques were performed, and the results showed that these approaches overcome the drawbacks of standard GP by successfully improving the accuracy and execution time.", notes = "Advisor: Simone A. Ludwig ProQuest, UMI Dissertations Publishing, 2014. 3614489", } @Article{Al-Madi:2016:GPEM, author = "Nailah Al-Madi", title = "Mike Preuss: Multimodal optimization by means of evolutionary algorithms", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "3", pages = "315--316", month = sep, note = "Book review", keywords = "genetic algorithms", ISSN = "1389-2576", URL = "https://rdcu.be/dR8cf", DOI = "doi:10.1007/s10710-016-9272-x", size = "2 pages", } @Article{Al-Maqaleh:2007:isi, author = "Basheer M. Al-Maqaleh and Kamal K. Bharadwaj", title = "Genetic Programming Approach to Hierarchical Production Rule Discovery", journal = "International Science Index", year = "2007", volume = "1", number = "11", pages = "531--534", keywords = "genetic algorithms, genetic programming, hierarchy, knowledge discovery in database, subsumption matrix. k", publisher = "World Academy of Science, Engineering and Technology", index = "International Science Index 11, 2007", bibsource = "http://waset.org/Publications", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.308.1481", ISSN = "1307-6892", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.1481", URL = "http://waset.org/publications/10022", size = "4 pages", abstract = "Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalised rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.", } @InProceedings{Al-Maqaleh:2012:ACCT, author = "Basheer Mohamad Ahmad Al-Maqaleh", title = "Genetic Algorithm Approach to Automated Discovery of Comprehensible Production Rules", booktitle = "Second International Conference on Advanced Computing Communication Technologies (ACCT 2012)", year = "2012", month = jan, pages = "69--71", size = "3 pages", abstract = "In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. This paper presents a classification algorithm based on GA approach that discovers comprehensible rules in the form of PRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a PR. For the proposed scheme a suitable and effective fitness function and appropriate genetic operators are proposed for the suggested representation. Experimental results are presented to demonstrate the performance of the proposed algorithm.", keywords = "genetic algorithms, GA, KDD, PR, automated discovery, chromosome encoding, comprehensible production rules, genetic algorithm approach, genetic operators, knowledge discovery in databases, production rules, data mining, database management systems", DOI = "doi:10.1109/ACCT.2012.57", notes = "Faculty of Computer Sciences & Information Systems Thamar University, Thamar, Republic of Yemen. Also known as \cite{6168335}", } @Article{Al-Rahamneh:2011:JSEA, author = "Zainab Al-Rahamneh and Mohammad Reyalat and Alaa F. Sheta and Sulieman Bani-Ahmad and Saleh Al-Oqeili", title = "A New Software Reliability Growth Model: Genetic-Programming-Based Approach", journal = "Journal of Software Engineering and Applications", year = "2011", volume = "4", number = "8", pages = "476--481", month = aug, publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, SBSE, software reliability, modelling, software faults", ISSN = "19453116", URL = "http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jsea.2011.48054", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19453116\&date=2011\&volume=04\&issue=08\&spage=476", DOI = "doi:10.4236/jsea.2011.48054", size = "6 pages", abstract = "A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is; the nature of each project makes it difficult to build a model which can generalise. In this paper we propose the use of Genetic Programming (GP) as an evolutionary computation approach to handle the software reliability modelling problem. GP deals with one of the key issues in computer science which is called automatic programming. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve problems. GP will be used to build a SRGM which can predict accumulated faults during the software testing process. We evaluate the GP developed model and compare its performance with other common growth models from the literature. Our experiments results show that the proposed GP model is superior compared to Yamada S-Shaped, Generalised Poisson, NHPP and Schneidewind reliability models.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:05291f1c8d43b618b364d9e2fbc587cc", } @InProceedings{Al-Ratrout:2010:SSD, author = "Serein Al-Ratrout and Francois Siewe and Omar Al-Dabbas and Mai Al-Fawair", title = "Hybrid Multi-Agent Architecture (HMAA) for meeting scheduling", booktitle = "2010 7th International Multi- Conference on Systems, Signals and Devices", year = "2010", address = "Amman, Jordan", month = "27-30 " # jun, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, multiagent, meeting scheduling, heuristic", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.1011.3891", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.3891", URL = "http://www.cse.dmu.ac.uk/%7Efsiewe/papers/serein_siewe_2010.pdf", DOI = "doi:10.1109/SSD.2010.5585505", size = "6 pages", abstract = "This paper presents a novel multi-agent architecture for meeting scheduling. The proposed architecture is a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. Moreover, the paper investigates the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. Three experimental groups are conducted in order to test the feasibility of the proposed architecture. The results show that the performance of the proposed architecture is better than those of many existing meeting scheduling frameworks. Moreover, it has been proved that HMAA preserves small agents' mobility (i.e. the ability to run on small devices) while implementing evolutionary algorithms.", notes = "Serein Al-Ratrout Department of Software Engineering, Alzytoonah University, Jordan Francois Siewe Software Technology Research Laboratory, Demontfort University, UK Omar Al-Dabbas Faculty of Engineering, Al-Balqa Applied University, Jordan Mai Al-Fawair Department of Software Engineering, Alzytoonah University, Jordan Also known as \cite{5585505}", } @Article{Al-Saati:2018:IJCA, author = "Najla Akram Al-Saati and Taghreed Riyadh Al-Reffaee", title = "Employing Gene Expression Programming in Estimating Software Effort", journal = "International Journal of Computer Applications", year = "2018", volume = "182", number = "8", pages = "1--8", month = aug, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Effort Estimation, Software Engineering, Artificial Intelligence", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "http://www.ijcaonline.org/archives/volume182/number8/29837-2018917619", URL = "https://www.ijcaonline.org/archives/volume182/number8/alsaati-2018-ijca-917619.pdf", DOI = "doi:10.5120/ijca2018917619", size = "8 pages", abstract = "The problem of estimating the effort for software packages is one of the most significant challenges encountering software designers. The precision in estimating the effort or cost can have a huge impact on software development. Various methods have been investigated in order to discover good enough solutions to this problem; lately evolutionary intelligent techniques are explored like Genetic Algorithms, Genetic Programming, Neural Networks, and Swarm Intelligence. In this work, Gene Expression Programming (GEP) is investigated to show its efficiency in acquiring equations that best estimates software effort. Datasets employed are taken from previous projects. The comparisons of learning and testing results are carried out with COCOMO, Analogy, GP and four types of Neural Networks, all show that GEP outperforms all these methods in discovering effective functions for the estimation with robustness and efficiency.", notes = "Also known as \cite{10.5120/ijca2018917619} www.ijcaonline.org Software Engineering Dept. College of Computer Sciences and Mathematics, University of Mosul, Iraq", } @Misc{journals/corr/abs-2001-09923, author = "Najla Akram Al-Saati", title = "Applying Gene Expression Programming for Solving One-Dimensional Bin-Packing Problems", howpublished = "arXiv", year = "2020", month = nov, keywords = "genetic algorithms, genetic programming, gene expression programming", volume = "abs/2001.09923", bibdate = "2020-01-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2001.html#abs-2001-09923", URL = "https://arxiv.org/abs/2001.09923", } @InProceedings{Al-Sahaf:2019:GECCOcomp, author = "Harith. Al-Sahaf and Ian Welch", title = "A genetic programming approach to feature selection and construction for ransomware, phishing and spam detection", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "332--333", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322083", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322083} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Al-Sahaf:2019:JRSNZ, author = "Harith Al-Sahaf and Ying Bi and Qi Chen and Andrew Lensen and Yi Mei and Yanan Sun and Binh Tran and Bing Xue and Mengjie Zhang", title = "A survey on evolutionary machine learning", journal = "Journal of the Royal Society of New Zealand", year = "2019", volume = "49", number = "2", pages = "205--228", note = "The 2019 Annual Collection of Reviews", keywords = "genetic algorithms, genetic programming, TPOT, AI, ANN, EML, GPU, EMO, autoML, artificial intelligence, machine learning, evolutionary computation, classification, regression, clustering, combinatorial optimisation, deep learning, transfer learning, ensemble learning", publisher = "Taylor \& Francis", URL = "https://doi.org/10.1080/03036758.2019.1609052", DOI = "doi:10.1080/03036758.2019.1609052", size = "24 pages", abstract = "Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e. evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning.", } @InProceedings{DBLP:conf/ijcci/ShormanFCGA18, author = "Amaal R. {Al Shorman} and Hossam Faris and Pedro A. Castillo and Juan Julian Merelo Guervos and Nailah Al-Madi", editor = "Christophe Sabourin and Juan Julian Merelo Guervos and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", title = "The Influence of Input Data Standardization Methods on the Prediction Accuracy of Genetic Programming Generated Classifiers", booktitle = "Proceedings of the 10th International Joint Conference on Computational Intelligence, {IJCCI} 2018, Seville, Spain, September 18-20, 2018", pages = "79--85", publisher = "SciTePress", year = "2018", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0006959000790085", DOI = "doi:10.5220/0006959000790085", timestamp = "Thu, 26 Sep 2019 16:43:57 +0200", biburl = "https://dblp.org/rec/conf/ijcci/ShormanFCGA18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @PhdThesis{Al-Zubaidi:thesis, author = "Wisam Haitham Abbood {Al-Zubaidi}", title = "Multi-objective search-based approach for software project management", school = "University of Wollongong", year = "2019", address = "Wollongong, NSW 2522, Australia", month = "31 " # mar, keywords = "genetic algorithms, genetic programming, SBSE, Iteration Planning, Agile Development, Effort Estimation, MOGP", URL = "https://ro.uow.edu.au/theses1/690/", URL = "https://ro.uow.edu.au/theses1/690/Al-Zubaidi_thesis.pdf", URL = "https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1678&context=theses1", size = "254 pages", abstract = "Project management covers the entire lifecycle of software, underpinning the success or failure of many software projects. Managing modern software projects often follows the incremental and iterative process where a software product is incrementally developed through a number of iterations. In each iteration, the development team needs to complete a number of issues, each of which can be implementing a new feature for the software, modifying an existing functionality, fixing a bug or conducting some other project tasks. Although this agile approach reduces the risk of project failures, managing projects at the level of issues and iterations is still highly difficult due to the inherent dynamic nature of software, especially in large-scale software projects. Challenges in this context can be in many forms such as making accurate estimations of the resolution time and effort of resolving issues or selecting suitable issues for upcoming iterations. These integral parts of planning is highly challenging since many factors need considering such as customer business value and the team historical estimations, capability and performance. Challenges also exist at the implementation level, such as managing the reviewing of code changes made to resolve issues. There is currently a serious lack of automated support which help project managers and software development teams address those challenges. This thesis aims to fill those gaps. We leverage a huge amount of historical data in software projects to generate valuable insight for dealing with those challenges in managing iterations and issues. We reformulate those project management problems as search-based optimization problems and employ a range of evolutionary meta-heuristics search techniques to solve them. The search is simultaneously guided by a number of multiple fitness functions that express different objectives (e.g. customer business value, developer expertise and workload, and complexity of estimation models) and constraints (e.g. a team historical capability and performance) in the context of modern software projects. Using this approach, we build novel models for estimating issue resolution time and effort, suggesting appropriate issues for upcoming iterations in iteration planning and recommending suitable reviewers for code changes made to resolve issues. An extensive empirical evaluation on a range of large software projects (including Mesos, Usergrid, Aurora, Slider, Kylin, Mahout, Common, Hdfs, MapReduce, Yarn, Apstud, Mule, Dnn, Timob, Tisud, Xd, Nexus, Android, LibreOffice, Qt, and Openstack) demonstrates the highly effective performance of our approach against other alternative techniques (improvement between 1.83 to 550 percent) to show the effectiveness of our approach.", notes = "supervisor: Hoa Khanh Dam", } @InProceedings{Alagesan:2008:AHS, author = "Shri Vidhya Alagesan and Sruthi Kannan and G. Shanthi and A. P. Shanthi and Ranjani Parthasarathi", title = "Intrinsic Evolution of Large Digital Circuits Using a Modular Approach", booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems, AHS '08", year = "2008", month = jun, pages = "19--26", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Xilinx Virtex II Pro board, evolvable hardware, large digital circuits, modular approach, modular developmental Cartesian genetic programming, scalability problem, software platform, time consuming fitness evaluation, digital circuits", DOI = "doi:10.1109/AHS.2008.52", abstract = "This work pioneers a generic and flexible approach to intrinsically evolve large digital circuits. One of the popular ways of handling the scalability problem prevalent in evolvable hardware (EHW) and evolve large circuits is to partition the circuit, evolve the individual partitions and then compact them. However, as the partition sizes become larger, this method also fails. This drawback is overcome by the modular developmental Cartesian genetic programming (MDCGP) technique, which still uses partitioning, but augments it further with horizontal and vertical reuse. The results obtained are promising and show that there is 100percent evolvability for 128-bit partitions, the largest partitions evolved so far. The fitness evaluation for the evolved partitions is done by downloading them onto Xilinx Virtex II Pro board. This work is the first step towards the development of a flexible evolvable framework which harnesses the power of hardware for the time consuming fitness evaluation and at the same time provides flexibility by carrying out the other parts using the easily modifiable software platform.", notes = "Also known as \cite{4584250}", } @Article{ALAGHBARI:2023:geoen, author = "Mohammed Al-Aghbari and Ashish {M. Gujarathi}", title = "Hybrid approach of using bi-objective genetic programming in well control optimization of waterflood management", journal = "Geoenergy Science and Engineering", volume = "228", pages = "211967", year = "2023", ISSN = "2949-8910", DOI = "doi:10.1016/j.geoen.2023.211967", URL = "https://www.sciencedirect.com/science/article/pii/S2949891023005547", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Bi-objective genetic programming, BioGP, NSGA-II, Net-flow method, Waterflood optimization, Reservoir simulation", abstract = "A new hybrid optimization approach is proposed by applying bi-objective genetic programming (BioGP) algorithm along with NSGA-II algorithm to expand the diversity of the Pareto solutions and speed up the convergence. The novel methodology is used in two distinct cases: the benchmark model for the Brugge field and a Middle Eastern oil-field sector model. The Brugge field includes twenty producing wells and ten injecting wells, but the real sector model has three injectors and four producers. The two primary objectives applied are to optimize the total volume of produced oil and reduce cumulative produced water. In the optimization process, the injection rate (qwi) and the bottom-hole pressure (BHP) are the control parameters for injection and producing wells, respectively. The hybrid technique of applying BioGP guided NSGA-II in the Brugge field model demonstrated a 50percent acceleration in the convergence speed when compared to the NSGA-II solution. The calculated Pareto solutions for the Middle-Eastern sector model by the proposed methodology at various generations exhibited better diversity and convergence in comparison to the NSGA-II solutions. The highest cumulative produced oil of 550.45 times 103 m3 is obtained by the proposed hybrid methodology in comparison to the NSGA-II's highest cumulative of 522 times 103 m3. The two solution points A' and B' achieved using the BioGP guided NSGA-II have lower WOR by 17percent and 15percent, respectively, than A and B solutions established by NSGA-II alone. Pareto solution ranking is performed using the net flow method (NFM) and the best optimum solution determined for BioGP guided NSGA-II is 532.38 times 103 m3 oil using equal-based weight compared to 505.44 times 103 m3 using the entropy-based weights of 41percent oil & 59percent water. Overall, the optimal Pareto solutions achieved by the proposed methodology of using BioGP guided NSGA-II algorithm has better diversity with improvement in convergence speed in comparison to the NSGA-II", } @InProceedings{Alajas:2022:IEMTRONICS, author = "Oliver John Alajas and Ronnie Concepcion and Argel Bandala and Edwin Sybingco and Ryan Rhay Vicerra and Elmer P. Dadios and Christan Hail Mendigoria and Heinrick Aquino and Leonard Ambata and Bernardo Duarte", booktitle = "2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)", title = "Detection and Quantitative Prediction of Diplocarpon earlianum Infection Rate in Strawberry Leaves using Population-based Recurrent Neural Network", year = "2022", abstract = "Fragaria ananassa, a member of the rose family's flowering plants, commonly recognized as strawberry, is prone to Diplocarpon earlianum infection that causes leaf scorch. Assessment via visual inspection of strawberries by farmers is normally ineffective, destructive, and laborious. To address this challenge, the use of integrated computer vision and machine learning techniques was done to classify a healthy from a scorch-infected strawberry leaf image and to estimate the leaf region infection rate (LRIR). A dataset made up of 204 normally healthy and 161 scorch-infected strawberry leaf images was used. Images were initially preprocessed and segmented via graph-cut segmentation to extract the region of interest for feature extraction and selection. The hybrid combination of neighborhood and principal component analysis (NCA-PCA) was used to select desirable features. Multigene genetic programming (MGGP) was used to formulate the fitness function that will be essential for determining the optimized neuron configurations of the recurrent neural network (RNN) through genetic algorithm (GA), and cuckoo search algorithm (CSA), and artificial bee colony (ABC). Four classification machine learning models were configured in which the classification tree (CTree) bested other detection models with an accuracy of 10percent and exhibited the shortest inference time of 14.746 s. The developed ABC-RNN3 model outperformed GA-RNN3 and CSA-RNN3 in performing non-invasive LRIR prediction with an R2 value of 0.948. With the use of the NCA-PCA-CTree3-ABC-RNN3 hybrid model, for crop disease detection and infection rate prediction, plant disease assessment proved to be more efficient and labor cost-effective than manual disease inspection methods.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IEMTRONICS55184.2022.9795744", month = jun, notes = "Also known as \cite{9795744}", } @InProceedings{Alajas:2022:HNICEM, author = "Oliver John Alajas and Ronnie {Concepcion II} and Argel Bandala and Edwin Sybingco and Elmer Dadios and Christan Hail Mendigoria and Heinrick Aquino", title = "Grape Phaeomoniella chlamydospora Leaf Blotch Recognition and Infected Area Approximation Using Hybrid Linear Discriminant Analysis and Genetic Programming", booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", year = "2022", address = "Boracay Island, Philippines", month = "1-4 " # dec, keywords = "genetic algorithms, genetic programming, Support vector machines, SVM, Image segmentation, Visualization, Image recognition, Computational modelling, Pipelines, Process control, image processing, plant disease detection, machine learning, computer vision, soft computing, black measles", ISSN = "2770-0682", isbn13 = "978-1-6654-6493-2", DOI = "doi:10.1109/HNICEM57413.2022.10109613", size = "6 pages", abstract = "Grapes, scientifically called Vitis vinifera, are vulnerable against Phaeomoniella chlamydospora, the microorganism that causes Esca (black measles) to the leaves, trunks, cordons, and fruit of a young vineyard. Manual visual examination via the naked eye can prove to be challenging especially if done in large-scale vineyards. To address this issue, merging the use of computer vision, image processing, and machine learning was employed as a means of performing blotch identification and leaf blotch area prediction. The dataset is made up of 543 images, comprised of healthy and Esca infected leaves which were captured by an RGB camera. Images were preprocessed and segmented to isolate the diseased pixels and compute the ground truth pixel area. Desirable leaf signatures (G, B, contrast, H, R, S, a*, b*, Cb, and Cr) derived from the feature extraction process using a classification tree. The LDA12 was able to accurately distinguish the healthy from the blotch-infected leaves with a whopping 98.77percent accuracy compared to NB, KNN, and SVM. The MGSR12, with an R2 of 0.9208, topped other models such as RTree, GPR, and RLinear. The hybrid CTree-LDA12-MGSR12 algorithm proved to be ideal in performing leaf health classification and blotched area assessment of grape phenotypes which is important in plant disease identification and fungal spread prevention.", notes = "Also known as \cite{10109613}", } @TechReport{Alander:1995:ibGP, author = "Jarmo T. Alander", title = "An Indexed Bibliography of Genetic Programming", institution = "Department of Information Technology and Industrial Management, University of Vaasa", year = "1995", type = "Report Series no", number = "94-1-GP", address = "Finland", URL = "ftp://ftp.uwasa.fi/cs/report94-1/gaGPbib.ps.Z", keywords = "genetic algorithms, genetic programming", abstract = "220 references. Indexed by subject, publication type and author", notes = "http url reference not working Jan 95. ftp ok. Part of Alander's index of genetic algorithm publications (older versions, ie up to ~1993, are available via ftp, see ENCORE sites). New version dated May 18, 1995. See also Jarmo T. Alander. An indexed bibliography of genetic algorithms: Years 1957-1993. Art of CAD Ltd., Vaasa (Finland), 1994. (over 3000 GA references).", size = "46 pages", } @Book{Alander:1994:bib, author = "Jarmo T. Alander", title = "An Indexed Bibliography of Genetic Algorithms: Years 1957--1993", year = "1994", publisher = "Art of CAD ltd", address = "Vaasa, Finland", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.4481&rep=rep1&type=pdf", notes = "All GAs some 3000+ references", } @InProceedings{ga96fAlander, annote = "*on,*FIN,genetic programming,mathematics /algebra", author = "Jarmo T. Alander and Ghodrat Moghadampour and Jari Ylinen", title = "2nd order equation", pages = "215--218", year = "1996", editor = "Jarmo T. Alander", booktitle = "Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)", series = "Proceedings of the University of Vaasa, Nro. 13", publisher = "University of Vaasa", address = "Vaasa (Finland)", month = "19.-23.~" # aug, organisation = "Finnish Artificial Intelligence Society", keywords = "genetic algorithms, genetic programming, mathematics, algebra", URL = "ftp://ftp.uwasa.fi/cs/2NWGA/Ghodrat2.ps.Z", size = "4 pages", abstract = "In this work we have tried to use genetic programming to solve the simple second order equation", notes = "2NWGA.bib gives title as 'Solving the second order equation using genetic programming' lil-gp evolution of formular for quadratic roots. lil-gp does not seem to be robust to find the solution formula of 2nd order equation", } @Article{ALANZI:2024:jer, author = "Hamdan Alanzi and Hamoud Alenezi and Oladayo Adeyi and Abiola J. Adeyi and Emmanuel Olusola and Chee-Yuen Gan and Olusegun Abayomi Olalere", title = "Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica", journal = "Journal of Engineering Research", year = "2024", ISSN = "2307-1877", DOI = "doi:10.1016/j.jer.2024.02.020", URL = "https://www.sciencedirect.com/science/article/pii/S2307187724000476", keywords = "genetic algorithms, genetic programming, leaf, bioactive extract, Heat-assisted technology, multi gene genetic programming, reliability assessment", abstract = "This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100percent) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50degreeC), solid-to-liquid ratio (1:20-1:60g/mL), and extraction time (45-180min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50percent ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565percent), TPC (67.116mg GAE/g), and AA (3.68583uM AAE/g) were determined as OT of 41.61degreeC, S:L of 1:60g/mL, and ET of 180min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985percent certainty for TPC, 97.569percent for EY, and 98.661percent for AA values", } @Article{ALARFAJ:2024:cscm, author = "Mohammed Alarfaj and Hisham Jahangir Qureshi and Muhammad Zubair Shahab and Muhammad Faisal Javed and Md Arifuzzaman and Yaser Gamil", title = "Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete", journal = "Case Studies in Construction Materials", volume = "20", pages = "e02836", year = "2024", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2023.e02836", URL = "https://www.sciencedirect.com/science/article/pii/S2214509523010173", keywords = "genetic algorithms, genetic programming, Gene expression programming, Fiber reinforced Recycled Aggregate Concrete, Machine Learning, Sustainability, Eco-friendly Concrete, Spilt Tensile Strength, Deep neural networks, ANN, Optimizable gaussian process regression", abstract = "The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2, RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3percent and 13.5percent higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3percent and 9.21percent better than DNN1 and GEP1 respectively in terms of R2. DNN2 model performed 20.32percent and 31.5percent better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1percent and 31.5percent better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively used in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and use hybrid models to further enhance the accuracy and reliability of the models", } @Article{ALASKAR:2023:cscm, author = "Abdulaziz Alaskar and Ghasan Alfalah and Fadi Althoey and Mohammed Awad Abuhussain and Muhammad Faisal Javed and Ahmed Farouk Deifalla and Nivin A. Ghamry", title = "Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature", journal = "Case Studies in Construction Materials", volume = "18", pages = "e02199", year = "2023", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2023.e02199", URL = "https://www.sciencedirect.com/science/article/pii/S2214509523003790", abstract = "The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning of concrete components for obtaining the required compressive strength (C-S) at elevated temperatures is a complicated and time-taking process. However, using evolutionary programming techniques such as gene expression programming (GEP) and multi-expression programming (MEP) provides the accurate prediction of concrete C-S. This article presents the genetic programming-based models (such as gene expression programming (GEP) and multi-expression programming (MEP)) for forecasting the concrete compressive strength (C-S) at elevated temperatures. In this regard, 207 C-S values at elevated temperatures were obtained from previous studies. In the model's development, C-S was considered as the output parameter with the nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, and gravels. The efficacy and accuracy of the GEP and MEP-based models were assessed by using statistical measures such as mean absolute error (MAE), correlation coefficient (R2), and root mean square error (RMSE). Moreover, models were also evaluated for external validation using different validation criteria recommended by previous studies. In comparing GEP and MEP models, GEP gave higher R2 and lower RMSE and MAE values of 0.854, 5.331 MPa, and 0.018 MPa respectively, indicating a strong correlation between actual and anticipated outputs. Thus, the GEP-based model was used further for sensitivity analysis, which revealed that cement is the most influencing factor. In addition, the proposed GEP model provides simple mathematical expression that can be easily implemented in practice", } @Article{ALATEFI:2024:cherd, author = "Saad Alatefi and Okorie Ekwe Agwu and Reda Abdel Azim and Ahmad Alkouh and Iskandar Dzulkarnain", title = "Development of multiple explicit data-driven models for accurate prediction of {CO2} minimum miscibility pressure", journal = "Chemical Engineering Research and Design", year = "2024", ISSN = "0263-8762", DOI = "doi:10.1016/j.cherd.2024.04.033", URL = "https://www.sciencedirect.com/science/article/pii/S0263876224002351", keywords = "genetic algorithms, genetic programming, Artificial intelligence, CO2, Explicit models, Gas flooding, Minimum miscibility pressure", abstract = "multiple data-driven models for predicting CO2 minimum miscibility pressure (MMP). The aim is to address the issue of existing models lacking explicit presentation. With a database of 155 data points, five models were developed using artificial neural network (ANN), multigene genetic programming (MGGP), support vector regression (SVR), multivariate adaptive regression splines (MARS), and multiple linear regression (MLR). Comparative analysis was conducted using statistical metrics (R2, MSE, MAE, RMSE), and sensitivity analysis was performed on input variables. The results showed that ANN and SVR had comparable predictive performance (ANN: R2 = 0.982, MSE = 0.00676, MAE = 0.9765, RMSE = 0.082), SVR (R2 = 0.935, MSE = 0.0041, MAE = 0.72, RMSE = 0.064) followed by MARS, MLR, and MGGP. Sensitivity analysis revealed that reservoir temperature was the most influential parameter across all models, except for the MLR algorithm where injected CO2 amount was crucial. These models can be used for a wide range of CO2 MMP ranging from 940psi to 5830psi, thus rendering them useful for any reservoir globally. These models offer improved accuracy and computational efficiency compared to existing ones, potentially reducing costs associated with laboratory experiments and providing rapid and precise CO2 MMP predictions", } @InProceedings{Alattas:2016:CT-IETA, author = "R. Alattas", booktitle = "2016 Annual Connecticut Conference on Industrial Electronics, Technology Automation (CT-IETA)", title = "Hybrid evolutionary designer of modular robots", year = "2016", abstract = "The majority of robotic design approaches start with designing morphology, then designing the robot control. Even in evolutionary robotics, the morphology tends to be fixed while evolving the robot control, which considered insufficient since the robot control and morphology are interdependent. Moreover, both control and morphology are highly interdependent with the surrounding environment, which affects the used optimisation strategies. Therefore, we propose in this paper a novel hybrid GP/GA method for designing autonomous modular robots that co-evolves the robot control and morphology and also considers the surrounding environment to allow the robot of achieving behaviour specific tasks and adapting to the environmental changes. The introduced method is automatically designing feasible robots made up of various modules. Then, our new evolutionary designer is evaluated using a benchmark problem in modular robotics, which is a walking task where the robot has to move a certain distance.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CT-IETA.2016.7868256", month = oct, notes = "Also known as \cite{7868256}", } @InProceedings{Alavi:2008:ICECT, author = "A. H. Alavi and A. A. Heshmati and H. Salehzadeh and A. H. Gandomi and A. Askarinejad", title = "Soft Computing Based Approaches for High Performance Concrete", booktitle = "Proceedings of the Sixth International Conference on Engineering Computational Technology", year = "2008", editor = "M. Papadrakakis and B. H. V. Topping", volume = "89", series = "Civil-Comp Proceedings", pages = "Paper 86", address = "Athens", publisher_address = "Stirlingshire, UK", month = "2-5 " # sep, publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, linear genetic programming, high performance concrete, multilayer perceptron, compressive strength, workability, mix design", isbn13 = "978-1905088263", ISSN = "1759-3433", URL = "http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3", URL = "http://www.amazon.co.uk/Proceedings-International-Conference-Engineering-Computational/dp/1905088264", DOI = "doi:10.4203/ccp.89.86", abstract = "High performance concrete (HPC) is a class of concrete that provides superior performance than those of conventional types. The enhanced performance characteristics of HPC are generally achieved by the addition of various cementitious materials and chemical and mineral admixtures to conventional concrete mix designs. These parameters considerably influence the compressive strength and workability properties of HPC mixes. An extensive understanding of the relation between these parameters and properties of the resulting matrix is required for developing a standard mix design procedure for HPC mix. To avoid testing several mix proportions to generate a successful mix and also simulating the behaviour of strength and workability improvement to an arbitrary degree of accuracy that often lead to savings in cost and time, it is idealistic to develop prediction models so that the performance characteristics of HPC mixes can be evaluated from the influencing parameters. Therefore, in this paper, linear genetic programming (LGP) is used for the first time in the literature to develop mathematical models to be able to predict the strength and slump flow of HPC mixes in terms of the variables responsible. Subsequently, the LGP based prediction results are compared with the results of proposed multilayer perceptron (MLP) in terms of prediction performance. Sand-cement ratio, coarse aggregate-cement ratio, water-cement ratio, percentage of silica fume and percentage of superplasticiser are used as the input variables to the models to predict the strength and slump flow of HPC mixes. A reliable database was obtained from the previously published literature in order to develop the models. The results of the present study, based on the values of performance measures for the models, demonstrated that for the prediction of compressive strength the optimum MLP model outperforms both the best team and the best single solution that have been created by LGP. It can be seen that for the slump flow the best LGP team solution has produced better results followed by the LGP best single solution and the MLP model. It can be concluded that LGPs are able to reach a prediction performance very close to or even better than the MLP model and as promising candidates can be used for solving such complex prediction problems.", notes = "A.H. Alavi1, A.A. Heshmati1, H. Salehzadeh1, A.H. Gandomi2 and A. Askarinejad3 1College of Civil Engineering, Iran University of Science & Technology (IUST), Tehran, Iran 2College of Civil Engineering, Tafresh University, Iran 3Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland", } @InProceedings{Alavi:2008:ICECT2, author = "A. H. Alavi and A. A. Heshmati and A. H. Gandomi and A. Askarinejad and M. Mirjalili", title = "Utilisation of Computational Intelligence Techniques for Stabilised Soil", booktitle = "Proceedings of the Sixth International Conference on Engineering Computational Technology", year = "2008", editor = "M. Papadrakakis and B. H. V. Topping", volume = "89", series = "Civil-Comp Proceedings", pages = "Paper 175", address = "Athens", publisher_address = "Stirlingshire, UK", month = "2-5 " # sep, publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, linear genetic programming, stabilised soil, multilayer perceptron, textural properties of soil, cement, lime, asphalt, unconfined compressive strength", isbn13 = "978-1905088263", ISSN = "1759-3433", URL = "http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3", URL = "http://www.amazon.co.uk/Proceedings-International-Conference-Engineering-Computational/dp/1905088264", DOI = "doi:10.4203/ccp.89.175", abstract = "In the present study, two branches of computational intelligence techniques namely, the multilayer perceptron (MLP) and linear genetic programming (LGP), are employed to simulate the complex behaviour of the strength improvement in a chemical stabilisation process. Due to a need to avoid extensive and cumbersome experimental stabilisation tests on soils on every new occasion, it was decided to develop mathematical models to be able to estimate the unconfined compressive strength (UCS) as a quality of the stabilised soil after both compaction and curing by using particle size distribution, liquid limit, plasticity index, linear shrinkage as the properties of natural soil before compaction and stabilisation and the quantities and types of stabiliser. A comprehensive and reliable set of data including 219 previously published UCS test results were used to develop the prediction models. Based on the values of performance measures for the models, it was observed that all models are able to predict the UCS value to an acceptable degree of accuracy. The results demonstrated that the optimum MLP model with one hidden layer and thirty six neurons outperforms both the best single and the best team program that have been created by LGP. It can also be concluded that the best team program evolved by LGP has a better performance than the best single evolved program. This investigation revealed that, on average, LGP is able to reach a prediction performance similar to the MLP model. Moreover, LGP as a white-box model provides the programs of an imperative language or machine language that can be inspected and evaluated to provide a better understanding of the underlying relationship between the different interrelated input and output data.", notes = "A.H. Alavi1, A.A. Heshmati1, A.H. Gandomi2, A. Askarinejad3 and M. Mirjalili4 1College of Civil Engineering, Iran University of Science and Technology, Tehran, Iran 2College of Civil Engineering, Tafresh University, Iran 3Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland 4Department of Civil & Earth Resources Engineering, Graduate School of Engineering, Kyoto University, Japan", } @Article{Alavi:2010:HP, author = "A. H. Alavi and A. H. Gandomi and M. Gandomi", title = "Comment on 'Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological Processes 22: 623-628'", journal = "Hydrological Processes", year = "2010", volume = "24", number = "6", pages = "798--799", month = "15 " # mar, keywords = "genetic algorithms, genetic programming, AIMGP, Discipulus", publisher = "John Wiley & Sons, Ltd.", ISSN = "1099-1085", URL = "http://onlinelibrary.wiley.com/doi/10.1002/hyp.7511/abstract", DOI = "doi:10.1002/hyp.7511", size = "1.5 pages", notes = "no abstract About \cite{Sivapragasam:2007:HP}. See also \cite{Sivapragasam:2010:HP}", } @Article{Alavi:2010:EwC, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Mohammad Ghasem Sahab and Mostafa Gandomi", title = "Multi Expression Programming: A New Approach to Formulation of Soil Classification", journal = "Engineering with Computers", year = "2010", volume = "26", number = "2", pages = "111--118", month = apr, email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, Multi expression programming, Soil classification, Formulation", DOI = "doi:10.1007/s00366-009-0140-7", size = "8 pages", abstract = "This paper presents an alternative approach to formulation of soil classification by means of a promising variant of genetic programming (GP), namely multi expression programming (MEP). Properties of soil, namely plastic limit, liquid limit, colour of soil, percentages of gravel, sand, and fine-grained particles are used as input variables to predict the classification of soils. The models are developed using a reliable database obtained from the previously published literature. The results demonstrate that the MEP-based formulae are able to predict the target values to high degree of accuracy. The MEP-based formulation results are found to be more accurate compared with numerical and analytical results obtained by other researchers.", notes = "M. Gandomi School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran", } @Article{Alavi:2010:GeoMechEng, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Mehdi Mousavi and Ali Mollahasani", title = "High-Precision Modeling of Uplift Capacity of Suction Caissons Using a Hybrid Computational Method", journal = "Geomechanics and Engineering", year = "2010", volume = "2", number = "4", pages = "253--280", month = dec, keywords = "genetic algorithms, genetic programming, suction caissons, uplift capacity, simulated annealing, nonlinear modelling", URL = "http://technopress.kaist.ac.kr/?page=container&journal=gae&volume=2&num=4", DOI = "doi:10.12989/gae.2010.2.4.253", abstract = "A new prediction model is derived for the uplift capacity of suction caissons using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA. The predictor variables included in the analysis are the aspect ratio of caisson, shear strength of clayey soil, load point of application, load inclination angle, soil permeability, and loading rate. The proposed model is developed based on well established and widely dispersed experimental results gathered from the literature. To verify the applicability of the proposed model, it is employed to estimate the uplift capacity of parts of the test results that are not included in the modelling process. Traditional GP and multiple regression analyses are performed to benchmark the derived model. The external validation of the GP/SA and GP models was further verified using several statistical criteria recommended by researchers. Contributions of the parameters affecting the uplift capacity are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the obtained trends are confirmed with some previous studies. Based on the results, the GP/SA-based solution is effectively capable of estimating the horizontal, vertical and inclined uplift capacity of suction caissons. Furthermore, the GP/SA model provides a better prediction performance than the GP, regression and different models found in the literature. The proposed simplified formulation can reliably be employed for the pre-design of suction caissons. It may be also used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.", } @Article{Alavi:2010:ijcamieec, author = "Amir Hossein Alavi and Amir Hossein Gandomi", title = "A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems", journal = "International Journal of Computer Aided Methods in Engineering-Engineering Computations", year = "2011", volume = "28", number = "3", pages = "242--274", email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com", keywords = "genetic algorithms, genetic programming, gene expression programming, multi expression programming, Linear-based genetic programming, Data mining, Data collection, Geotechnical engineering, Programming and algorithm theory, Systems analysis, Formulation", ISSN = "0264-4401", URL = "http://www.emeraldinsight.com/journals.htm?articleid=1912293", DOI = "doi:10.1108/02644401111118132", size = "33 pages", abstract = "Purpose- The complexity of analysis of geotechnical behaviour is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. In the present study, capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP) and multi expression programming (MEP) are illustrated by applying them to the formulation of several complex geotechnical engineering problems. Design/methodology/approach- LGP, GEP and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one their major advantages over most of the traditional constitutive modeling methods. Findings- In order to demonstrate the simulation capabilities of LGP, GEP and MEP, they were applied to the prediction of (i) relative crest settlement of concrete-faced rockfill dams, (ii) slope stability, (iii) settlement around tunnels, and (iv) soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behaviour for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practising engineers. Originality/value- The LGP, GEP and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.", } @InProceedings{Alavi:2010:HBE, author = "Amir Hossein Alavi and Amir Hossein Gandomi", title = "Nonlinear Modeling of Liquefaction Behavior of Sand-Silt Mixtures in terms of Strain Energy", booktitle = "Proceedings of the 8th International Symposium on Highway and Bridge Engineering, Technology and Innovation in Transportation Infrastructure, 2010", year = "2010", editor = "Rodian Scinteie and Costel Plescan", pages = "50--69", address = "Iasi, Romania", month = "10 " # dec, organisation = "Editura Societatii Academice Matei - Teiu Botez", keywords = "genetic algorithms, genetic programming, GPLAB, Discipulus, simulated annealing, capacity energy, Matlab", isbn13 = "978-606-582-000-5", URL = "http://www.intersections.ro/Conferences/HBE2010.pdf", size = "20 pages", notes = "http://www.intersections.ro/Conferences/ HBE2010@intersections.ro", } @Article{Alavi:2010:CBM, author = "Amir Hossein Alavi and Mahmoud Ameri and Amir Hossein Gandomi and Mohammad Reza Mirzahosseini", title = "Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method", journal = "Construction and Building Materials", year = "2011", volume = "25", number = "3", pages = "1338--1355", month = mar, keywords = "genetic algorithms, genetic programming, Asphalt concrete mixture, Flow number, Simulated annealing, Marshall mix design, Regression analysis", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2010.09.010", size = "18 pages", abstract = "A high-precision model was derived to predict the flow number of dense asphalt mixtures using a novel hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed constitutive model correlates the flow number of Marshall specimens with the percentages of filler, bitumen, voids in mineral aggregate, Marshall stability and flow. The comprehensive experimental database used for the development of the model was established upon a series of uniaxial dynamic creep tests conducted in this study. Generalised regression neural network and multiple regression-based analyses were performed to benchmark the GP/SA model. The contributions of the variables affecting the flow number were evaluated through a sensitivity analysis. A subsequent parametric study was carried out and the trends of the results were confirmed with the results of the experimental study. The results indicate that the proposed GP/SA model is effectively capable of evaluating the flow number of asphalt mixtures. The derived model is remarkably straightforward and provides an analysis tool accessible to practising engineers.", notes = "a School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran b College of Civil Engineering, Tafresh University, Tafresh, Iran c Transportation Research Institute (TRI), Tehran, Iran", } @Article{Alavi20101239, author = "A. H. Alavi and A. H. Gandomi and A. A. R. Heshmati", title = "Discussion on {"}Soft computing approach for real-time estimation of missing wave heights{"} by S.N. Londhe [Ocean Engineering 35 (2008) 1080-1089]", journal = "Ocean Engineering", year = "2010", volume = "37", number = "13", pages = "1239--1240", month = sep, ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2010.06.003", URL = "http://www.sciencedirect.com/science/article/B6V4F-50DXD90-1/2/b2489a1aebf49e771abca1b27d3b24b4", keywords = "genetic algorithms, genetic programming, Linear genetic programming, Tree structure, Wave forecasts", abstract = "The paper studied by Londhe (2008) \cite{Londhe20081080} uses genetic programming (GP) for estimation of missing wave heights. The paper includes some problems about the fundamental aspects and use of the GP approach. In this discussion, some controversial points of the paper are given.", } @Article{Alavi2011, author = "Amir Hossein Alavi and Pejman Aminian and Amir Hossein Gandomi and Milad {Arab Esmaeili}", title = "Genetic-based modeling of uplift capacity of suction caissons", journal = "Expert Systems with Applications", volume = "38", number = "10", pages = "12608--12618", year = "2011", month = "15 " # sep, ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/pii/S0957417411005653", URL = "http://www.sciencedirect.com/science/article/B6V03-52P1KNK-4/2/f33267200d0fc51ad7a086befe3a361c", DOI = "doi:10.1016/j.eswa.2011.04.049", keywords = "genetic algorithms, genetic programming, Gene expression programming, Suction caissons, Uplift capacity, Formulation", size = "11 pages", abstract = "In this study, classical tree-based genetic programming (TGP) and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are used to develop new prediction equations for the uplift capacity of suction caissons. The uplift capacity is formulated in terms of several inflecting variables. An experimental database obtained from the literature is employed to develop the models. Further, a conventional statistical analysis is performed to benchmark the proposed models. Sensitivity and parametric analyses are conducted to verify the results. TGP, LGP and GEP are found to be effective methods for evaluating the horizontal, vertical, and inclined uplift capacity of suction caissons. The TGP, LGP and GEP models reach a prediction performance better than or comparable with the models found in the literature.", } @Article{Alavi:2011:JEQE, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Minoo Modaresnezhad and Mehdi Mousavi", title = "New Ground-Motion Prediction Equations Using Multi Expression Programing", journal = "Journal of Earthquake Engineering", year = "2011", volume = "15", number = "4", pages = "511--536", keywords = "genetic algorithms, genetic programming, Multi-Expression Programming, Time-Domain Ground-Motion Parameters, Attenuation Relationship, Nonlinear Modelling", ISSN = "1363-2469", URL = "http://www.tandfonline.com/doi/abs/10.1080/13632469.2010.526752#.UlMR6NKc_G0", DOI = "doi:10.1080/13632469.2010.526752", size = "26 pages", abstract = "High-precision attenuation models were derived to estimate peak ground acceleration (PGA), velocity (PGV), and displacement (PGD) using a new variant of genetic programming, namely multi expression programming (MEP). The models were established based on an extensive database of ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the models were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. The results indicate that the MEP attenuation models are capable of effectively estimating the peak ground-motion parameters. The proposed models are able to reach a prediction performance comparable with the attenuation relationships found in the literature.", } @Article{Alavi2012541, author = "Amir Hossein Alavi and Amir Hossein Gandomi", title = "Energy-based numerical models for assessment of soil liquefaction", journal = "Geoscience Frontiers", volume = "3", number = "4", pages = "541--555", year = "2012", ISSN = "1674-9871", DOI = "doi:10.1016/j.gsf.2011.12.008", URL = "http://www.sciencedirect.com/science/article/pii/S167498711100137X", keywords = "genetic algorithms, genetic programming, Soil liquefaction, Capacity energy, Multi expression programming, Sand, Formulation", abstract = "This study presents promising variants of genetic programming (GP), namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils. Generalised LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction (capacity energy) and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.", } @InCollection{books/sp/chiong2012/AlaviGM12, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Ali Mollahasani", title = "A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil", booktitle = "Variants of Evolutionary Algorithms for Real-World Applications", publisher = "Springer", year = "2012", editor = "Raymond Chiong and Thomas Weise and Zbigniew Michalewicz", chapter = "9", pages = "343--376", keywords = "genetic algorithms, genetic programming, Chemical stabilisation, Simulated annealing, Nonlinear modelling", isbn13 = "978-3-642-23423-1", DOI = "doi:10.1007/978-3-642-23424-8_11", abstract = "This chapter presents a variant of genetic programming, namely linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA, to predict the performance characteristics of stabilised soil. LGP and LGP/SA relate the unconfined compressive strength (UCS), maximum dry density (MDD), and optimum moisture content (OMC) metrics of stabilised soil to the properties of the natural soil as well as the types and quantities of stabilizing additives. Different sets of LGP and LGP/SA-based prediction models have been separately developed. The contributions of the parameters affecting UCS, MDD, and OMC are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the trends of the results are compared with previous studies. A comprehensive set of data obtained from the literature has been used for developing the models. Experimental results confirm that the accuracy of the proposed models is satisfactory. In particular, the LGP-based models are found to be more accurate than the LGP/SA-based models.", affiliation = "School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran", bibdate = "2011-11-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/books/collections/Chiong2012.html#AlaviGM12", } @InCollection{Alavi:2013:MWGTE, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Ali Mollahasani and Jafar {Bolouri Bazaz}", title = "Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems", editor = "Xin-She Yang and Amir Hossein Gandomi and Siamak Talatahari and Amir Hossein Alavi", booktitle = "Metaheuristics in Water, Geotechnical and Transport Engineering", publisher = "Elsevier", address = "Oxford", year = "2013", pages = "289--310", chapter = "12", keywords = "genetic algorithms, genetic programming, Tree-based genetic programming, linear genetic programming, geotechnical engineering, prediction", isbn13 = "978-0-12-398296-4", DOI = "doi:10.1016/B978-0-12-398296-4.00012-X", URL = "http://www.sciencedirect.com/science/article/pii/B978012398296400012X", abstract = "This chapter presents new approaches for solving geotechnical engineering problems using classical tree-based genetic programming (TGP) and linear genetic programming (LGP). TGP and LGP are symbolic optimisation techniques that create computer programs to solve a problem using the principle of Darwinian natural selection. Generally, they are supervised, machine-learning techniques that search a program space instead of a data space. Despite remarkable prediction capabilities of the TGP and LGP approaches, the contents of reported applications indicate that the progress in their development is marginal and not moving forward. The present study introduces a state-of-the-art examination of TGP and LGP applications in solving complex geotechnical engineering problems that are beyond the computational capability of traditional methods. In order to justify the capabilities of these techniques, they are systematically employed to formulate a typical geotechnical engineering problem. For this aim, effective angle of shearing resistance (phi) of soils is formulated in terms of the physical properties of soil. The validation of the TGP and LGP models is verified using several statistical criteria. The numerical example shows the superb accuracy, efficiency, and great potential of TGP and LGP. The models obtained using TGP and LGP can be used efficiently as quick checks on solutions developed by more time consuming and in-depth deterministic analyses. The current research directions and issues that need further attention in the future are discussed. Keywords Tree-based genetic programming, linear genetic programming geotechnical engineering, prediction", notes = "Also known as \cite{Alavi2013289}", } @Article{Alavi:2014:NCA, author = "Amir Hossein Alavi and Amir Hossein Gandomi and Hadi {Chahkandi Nejad} and Ali Mollahasani and Azadeh Rashed", title = "Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems", journal = "Neural Computing and Applications", year = "2013", volume = "23", number = "6", pages = "1771--1786", month = nov, keywords = "genetic algorithms, genetic programming, gene expression programming, Soil deformation modulus, Expression programming techniques, Pressure meter test, Soil physical properties", publisher = "Springer-Verlag", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007%2Fs00521-012-1144-6", DOI = "doi:10.1007/s00521-012-1144-6", language = "English", size = "16 pages", abstract = "Providing precise estimations of soil deformation modulus is very difficult due to its dependence on many factors. In this study, gene expression programming (GEP) and multi-expression programming (MEP) systems are presented to derive empirical equations for the prediction of the pressuremeter soil deformation modulus. The employed expression programming (EP) systems formulate the soil deformation modulus in terms of the soil physical properties. Selection of the best models is on the basis of developing and controlling several models with different combinations of the affecting parameters. The proposed EP-based models are established upon 114 pressure meter tests on different soil types conducted in this study. The generalisation capabilities of the models are verified using several statistical criteria. Contributions of the variables influencing the soil modulus are evaluated through a sensitivity analysis. The GEP and MEP approaches accurately characterise the soil deformation modulus resulting in a very good prediction performance. The result indicates that moisture content and soil dry unit weight can efficiently represent the initial state and consolidation history of soil for determining its modulus.", } @Article{Alavi:2014:GF, author = "Amir H. Alavi and Ehsan Sadrossadat", title = "New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses", journal = "Geoscience Frontiers", year = "2014", keywords = "genetic algorithms, genetic programming, Rock mass properties, Ultimate bearing capacity, Shallow foundation, Prediction, Evolutionary computation", ISSN = "1674-9871", DOI = "doi:10.1016/j.gsf.2014.12.005", URL = "http://www.sciencedirect.com/science/article/pii/S1674987114001625", abstract = "Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterise the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.", } @Article{Alavi:2016:GSF, author = "Amir H. Alavi and Amir H. Gandomi and David J. Lary", title = "Progress of machine learning in geosciences: Preface", journal = "Geoscience Frontiers", year = "2016", volume = "7", number = "1", pages = "1--2", note = "Editorial", keywords = "genetic algorithms, genetic programming", ISSN = "1674-9871", URL = "http://www.sciencedirect.com/science/article/pii/S1674987115001243", DOI = "doi:10.1016/j.gsf.2015.10.006", size = "2 pages", notes = "Peer-review under responsibility of China University of Geosciences (Beijing)", } @Article{Alavi:2017:ACME, author = "Amir H. Alavi and Hassene Hasni and Imen Zaabar and Nizar Lajnef", title = "A new approach for modeling of flow number of asphalt mixtures", journal = "Archives of Civil and Mechanical Engineering", volume = "17", number = "2", pages = "326--335", year = "2017", ISSN = "1644-9665", DOI = "doi:10.1016/j.acme.2016.06.004", URL = "http://www.sciencedirect.com/science/article/pii/S1644966516300814", abstract = "Flow number of asphalt-aggregate mixtures is an explanatory parameter for the analysis of rutting potential of asphalt mixtures. In this study, a new model is proposed for the determination of flow number using a robust computational intelligence technique, called multi-gene genetic programming (MGGP). MGGP integrates genetic programming and classical regression to formulate the flow number of Marshall Specimens. A reliable experimental database is used to develop the proposed model. Different analyses are performed for the performance evaluation of the model. On the basis of a comparison study, the MGGP model performs superior to the models found in the literature.", keywords = "genetic algorithms, genetic programming, Asphalt mixture, Flow number, Marshall mix design", } @InProceedings{alba:1996:tGPrdflc, author = "Enrique Alba and Carlos Cotta and Jose M. Troya", title = "Type-Constrained Genetic Programming for Rule-Base Definition in Fuzzy Logic Controllers", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "255--260", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "6 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap31.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{alba:1999:ERASPSPDGA, author = "Enrique Alba and Carlos Cotta and Jose M. Troya", title = "Entropic and Real-Time Analysis of the Search with Panmictic, Structured, and Parallel Distributed Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "773", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-808.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{alba:1999:T, author = "Enrique Alba and Jose M. Troya", title = "Tackling epistasis with panmictic and structured genetic algorithms", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "1--7", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms, NK", notes = "GECCO-99LB", } @Article{alba:1999:edflcSGP, author = "Enrique Alba and Carlos Cotta and Jose M. Troya", title = "Evolutionary Design of Fuzzy Logic Controllers Using Strongly-Typed GP", journal = "Mathware \& Soft Computing", year = "1999", volume = "6", number = "1", pages = "109--124", keywords = "genetic algorithms, genetic programming, Type System, Fuzzy Logic Controller, Cart-Centering Problem", URL = "http://docto-si.ugr.es/Mathware/v6n1/PS/7-alba.ps.gz", abstract = "An evolutionary approach to the design of fuzzy logic controllers is presented in this paper. We propose the use of the genetic programming paradigm to evolve fuzzy rule-bases (internally represented as type-constrained syntactic trees). This model has been applied to the cart-centering problem, although it can be readily extended to other problems. The obtained results show that a good parameterization of the algorithm, and an appropriate evaluation function, can lead to near-optimal solutions.", notes = "Mathware and softcomputing http://docto-si.ugr.es/Mathware/ENG/mathware.html", } @Book{Alba05, author = "Enrique Alba", title = "Parallel Metaheuristics: A New Class of Algorithms", publisher = "John Wiley \& Sons", month = aug, year = "2005", address = "NJ, USA", ISBN = "0-471-67806-6", keywords = "genetic algorithms, genetic programming, book, text, general computer engineering", URL = "https://www.amazon.com/Parallel-Metaheuristics-New-Class-Algorithms/dp/0471678066/ref=sr_1_1", abstract = "This single reference on parallel metaheuristic presents modern and ongoing research information on using, designing, and analysing efficient models of parallel algorithms. Table of Contents Author Information Introduction. PART I: INTRODUCTION TO METAHEURISTICS AND PARALLELISM. 1. An Introduction to Metaheuristic Techniques. 2. Measuring the Performance of Parallel Metaheuristics. 3. New Technologies in Parallelism. 4. Metaheuristics and Parallelism. PART II: PARALLEL METAHEURISTIC MODELS. 5. Parallel Genetic Algorithms. 6. Spatially Structured Genetic Programming. 7. Parallel Evolution Strategies. 8. Parallel Ant Colony Algorithms. 9. Parallel Estimation of Distribution Algorithms. 10. Parallel Scatter Search. 11. Parallel Variable Neighbourhood Search. 12. Parallel Simulated Annealing. 13. Parallel Tabu Search. 14. Parallel GRASP. 15. Parallel Hybrid Metaheuristics. 16. Parallel Multi Objective. 17. Parallel Heterogeneous Metaheuristics. PART III: THEORY AND APPLICATIONS. 18. Theory of Parallel Genetic Algorithms. 19. Parallel Metaheuristics. 20. Parallel Metaheuristics in Telecommunications. 21. Bioinformatics and Parallel Metaheuristics. Index.", notes = "US 95.", size = "00584 pages", } @InProceedings{Albalushi:2023:SWC, author = "Muna Albalushi and Rasha {Al Jassim} and Karan Jetly and Raya {Al Khayari} and Hilal {Al Maqbali}", booktitle = "2023 IEEE Smart World Congress (SWC)", title = "Optimizing Diabetes Predictive Modeling with Automated Decision Trees", year = "2023", abstract = "This paper introduces Linear Genetic Programming for Optimising Decision Tree (LGP-OptTree), a novel form of Genetic Programming (GP) aimed at enhancing diabetes detection. LGP-OptTree is designed to optimise the attributes and hyperparameters of decision trees by using a unique genotype and phenotype structure. The proposed method is evaluated on the Pima dataset and compared with other techniques. By fine-tuning the attributes and hyperparameters of decision trees using LGP-OptTree, this study aims to improve the accuracy and efficacy of diabetes detection. A performance metric is used to determine the effectiveness of the proposed method with respect to other approaches. The contribution of this research lies in providing general healthcare professionals with a new approach for enhancing diabetes detection accuracy through decision trees.", keywords = "genetic algorithms, genetic programming, Measurement, Medical services, Predictive models, Prediction algorithms, Diabetes, Decision trees, Evolutionary Algorithm", DOI = "doi:10.1109/SWC57546.2023.10449077", month = aug, notes = "Also known as \cite{10449077}", } @InProceedings{Albarracin:2016:SIBGRAPI, author = "Juan Felipe {Hernandez Albarracin} and Jefersson Alex {dos Santos} and Ricardo {da S. Torres}", booktitle = "2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)", title = "Learning to Combine Spectral Indices with Genetic Programming", year = "2016", pages = "408--415", abstract = "This paper introduces a Genetic Programming-based method for band selection and combination, aiming to support remote sensing image classification tasks. Relying on ground-truth data, our method selects spectral bands and finds the arithmetic combination of those bands (i.e., spectral index) that best separates examples of different classes. Experimental results demonstrate that the proposed method is very effective in pixel-wise binary classification problems.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SIBGRAPI.2016.063", month = oct, notes = "Also known as \cite{7813062}", } @Article{albarracin:2020:RS, author = "Juan F. H. Albarracin and Rafael S. Oliveira and Marina Hirota and Jefersson A. {dos Santos} and Ricardo da S. Torres", title = "A Soft Computing Approach for Selecting and Combining Spectral Bands", journal = "Remote Sensing", year = "2020", volume = "12", number = "14", keywords = "genetic algorithms, genetic programming", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/12/14/2267", DOI = "doi:10.3390/rs12142267", abstract = "We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimisation problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learnt spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes.", notes = "also known as \cite{rs12142267}", } @InProceedings{Albinati:2014:SMGP, author = "Julio Albinati and Gisele L. Pappa and Fernando E. B. Otero and Luiz Otavio V. B. Oliveira", title = "A Study of Semantic Geometric Crossover Operators in Regression Problems", booktitle = "Semantic Methods in Genetic Programming", year = "2014", editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill", address = "Ljubljana, Slovenia", month = "13 " # sep, note = "Workshop at Parallel Problem Solving from Nature 2014 conference", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Albinati.pdf", size = "2 pages", notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014", } @InProceedings{Albinati:2015:EuroGP, author = "Julio Albinati and Gisele L. Pappa and Fernando E. B. Otero and Luiz Otavio V. B. Oliveira", title = "The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "3--15", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Crossover, Crossover mask optimisation", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1", abstract = "This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimise the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.", notes = "Nominated for EuroGP 2015 Best Paper. Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @Article{albrecht:2022:Polymers, author = "Hanny Albrecht and Wolfgang Roland and Christian Fiebig and Gerald Roman Berger-Weber", title = "Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes", journal = "Polymers", year = "2022", volume = "14", number = "17", pages = "Article No. 3455", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/17/3455", DOI = "doi:10.3390/polym14173455", abstract = "Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimising wall thickness distribution include adaptation of the mold block geometry and structure optimisation. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally—an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modelling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimising the wall thickness distribution.", notes = "also known as \cite{polym14173455}", } @InCollection{Albuquerque:2004:EMTP, author = "Ana Claudia M. L. Albuquerque and Jorge D. Melo and Adriao D. {Doria Neto}", title = "Evolutionary Computation and Parallel Processing Applied to the Design of Multilayer Perceptrons", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "181--203", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "8", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InProceedings{albuquerque:2000:irfl, author = "Paul Albuquerque and Bastien Chopard and Christian Mazza and Marco Tomassini", title = "On the Impact of the Representation on Fitness Landscapes", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "1--15", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_1", abstract = "In this paper we study the role of program representation on the properties of a type of Genetic Programming (GP) algorithm. In a specific case, which we believe to be generic of standard GP, we show that the way individuals are coded is an essential concept which impacts the fitness landscape. We give evidence that the ruggedness of the landscape affects the behavior of the algorithm and we find that, below a critical population, whose size is representation-dependent, premature convergence occurs.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{alcaraz:2019:JMMP, author = "Joselito Yam II {Alcaraz} and Kunal Ahluwalia and Swee-Hock Yeo", title = "Predictive Models of {Double-Vibropolishing} in Bowl System Using Artificial Intelligence Methods", journal = "Journal of Manufacturing and Materials Processing", year = "2019", volume = "3", number = "1", keywords = "genetic algorithms, genetic programming, vibratory finishing, double vibro-polishing, artificial intelligence, regression, neural network, ANN", ISSN = "2504-4494", URL = "https://www.mdpi.com/2504-4494/3/1/27", DOI = "doi:10.3390/jmmp3010027", abstract = "Vibratory finishing is a versatile and efficient surface finishing process widely used to finish components of various functionalities. Research efforts were focused in fundamental understanding of the process through analytical solutions and simulations. On the other hand, predictive modelling of surface roughness using computational intelligence (CI) methods are emerging in recent years, though CI methods have not been extensively applied yet to a new vibratory finishing method called double-vibropolishing. In this study, multi-variable regression, artificial neural networks, and genetic programming models were designed and trained with experimental data obtained from subjecting rectangular Ti-6Al-4V test coupons to double vibropolishing in a bowl system configuration. Model selection was done by comparing the mean-absolute percentage error and r-squared values from both training and testing datasets. Exponential regression was determined as the best model for the bowl double-vibropolishing system studied with a Test MAPE score of 6.1percent and a R-squared score of 0.99. A family of curves was generated using the exponential regression model as a potential tool in predicting surface roughness with time.", notes = "also known as \cite{jmmp3010027}", } @Article{alcazar:2024:IJMS, author = "Jackson J. Alcazar", title = "Thiophene Stability in Photodynamic Therapy: A Mathematical Model Approach", journal = "International Journal of Molecular Sciences", year = "2024", volume = "25", number = "5", pages = "Article No. 2528", month = "21 " # feb, note = "Special Issue Molecular Aspects of Photodynamic Therapy", keywords = "genetic algorithms, genetic programming, safe PDT, efficient PDT, thiophene-containing photosensitiser, singlet oxygen, conceptual DFT", ISSN = "1422-0067", URL = "https://www.mdpi.com/1422-0067/25/5/2528", DOI = "doi:10.3390/ijms25052528", code_url = "https://github.com/Jacksonalcazar/Thiophene-Reactivity-toward-Singlet-Oxygen", size = "18 pages", abstract = "Thiophene-containing photosensitizers are gaining recognition for their role in photodynamic therapy (PDT). However, the inherent reactivity of the thiophene moiety toward singlet oxygen threatens the stability and efficiency of these photosensitizers. This study presents a novel mathematical model capable of predicting the reactivity of thiophene toward singlet oxygen in PDT, using Conceptual Density Functional Theory (CDFT) and genetic programming. The research combines advanced computational methods, including various DFT techniques and symbolic regression, and is validated with experimental data. The findings underscore the capacity of the model to classify photosensitizers based on their photodynamic efficiency and safety, particularly noting that photosensitizers with a constant rate 1000 times lower than that of unmodified thiophene retain their photodynamic performance without substantial singlet oxygen quenching. Additionally, the research offers insights into the impact of electronic effects on thiophene reactivity. Finally, this study significantly advances thiophene-based photosensitizer design, paving the way for therapeutic agents that achieve a desirable balance between efficiency and safety in PDT.", notes = "also known as \cite{ijms25052528} Centro de Quimica Medica, Facultad de Medicina Clinica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile", } @InProceedings{Alchirch:2022:EuroGP, author = "Pantia-Marina Alchirch and Dimitrios I. Diochnos and Katia Papakonstantinopoulou", title = "Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence", booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", year = "2022", editor = "Eric Medvet and Gisele Pappa and Bing Xue", series = "LNCS", volume = "13223", publisher = "Springer Verlag", address = "Madrid, Spain", pages = "228--244", month = "20-22 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming: Poster, Evolvability, Monotone conjunctions, Distribution-specific learning, Bernoulli (p)**n distributions", isbn13 = "978-3-031-02055-1", DOI = "doi:10.1007/978-3-031-02056-8_15", abstract = "Recently it was shown, using the typical mutation mechanism that is used in evolutionary algorithms, that monotone conjunctions are provably evolvable under a specific set of Bernoulli (p)n distributions. A natural question is whether this mutation mechanism allows convergence under other distributions as well. Our experiments indicate that the answer to this question is affirmative and, at the very least, this mechanism converges under Bernoulli (p)n distributions outside of the known proved regime.", notes = "Athens University of Economics and Business (TESLAB), Athens, Greece http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022", } @InProceedings{Aldeia:2018:CEC, author = "Guilherme Aldeia and Fabricio {de Franca}", title = "Lightweight Symbolic Regression with the Interaction-Transformation Representation", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477951", abstract = "Symbolic Regression techniques stand out from other regression analysis tools because of the possibility of generating powerful but yet simple expressions. These simple expressions may be useful in many practical situations in which the practitioner wants to interpret the obtained results, fine tune the model, or understand the generating phenomena. Despite this possibility, the current state-of-the-art algorithms for Symbolic Regression usually require a high computational budget while having little guarantees regarding the simplicity of the returned expressions. Recently, a new Data Structure representation for mathematical expressions, called Interaction-Transformation (IT), was introduced together with a search-based algorithm named SymTree that surpassed a subset of the recent Symbolic Regression algorithms and even some state-of-the-art nonlinear regression algorithms, while returning simple expressions as a result. This paper introduces a lightweight tool based on this algorithm, named Lab Assistant. This tool runs on the client-side of any compatible Internet browser with JavaScript. Alongside this tool, two algorithms using the IT representation are introduced. Some experiments are performed in order to show the potential of the Lab Assistant to help practitioners, professors, researchers and students willing to experiment with Symbolic Regression. The results showed that this tool is competent to find the correct expression for many well known Physics and Engineering relations within a reasonable average time frame of a few seconds. This tool opens up lots of possibilities in Symbolic Regression research for low-cost devices to be used in applications where a high-end computer is not available.", notes = "WCCI2018", } @InProceedings{Aldeia:2020:CEC, author = "Guilherme Aldeia and Fabricio {de Franca}", title = "A Parametric Study of Interaction-Transformation Evolutionary Algorithm for Symbolic Regression", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24027", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185521", abstract = "The balance between approximation error and model complexity is an important trade-off for Symbolic Regression algorithms. This trade-off is achieved by means of specific operators for bloat control, modified operators, limits to the size of the generated expressions and multi-objective optimization. Recently, the representation Interaction-Transformation was introduced with the goal of limiting the search space to simpler expressions, thus avoiding bloating. This representation was used in the context of an Evolutionary Algorithm in order to find concise expressions resulting in small approximation errors competitive with the literature. Particular to this algorithm, two parameters control the complexity of the generated expression. This paper investigates the influence of those parameters w.r.t. the goodness-of-fit. Through some extensive experiments, we find that the maximum number of terms is more important to control goodness-of-fit but also that there is a limit to the extent that increasing its value renders any benefits. Second, the limit to the minimum and maximum value of the exponent has a smaller influence to the results and it can be set to a default value without impacting the final results.", notes = "https://wcci2020.org/ Federal University of ABC, Brazil", } @InProceedings{Aldeia:2021:GECCO, author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}", title = "Measuring Feature Importance of Symbolic Regression Models Using Partial Effects", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "750--758", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, XAI, explainable AI, symbolic regression, interaction-transformation, Supervised learning, SHAP, Shapley value", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459302", size = "9 pages", abstract = "In explainable AI, one aspect of a prediction's explanation is to measure each predictor's importance to the decision process.The importance can measure how much variation a predictor promotes locally or how much the predictor contributes to the deviation from a reference point (Shapley value). If we have the ground truth analytical model, we can calculate the former using the Partial Effect, calculated as the predictor's partial derivative. Also, we can estimate the latter by calculating the average partial effect multiplied by the difference between the predictor and the reference value. Symbolic Regression is a gray-box model for regression problems that returns an analytical model approximating the input data. Although it is often associated with interpretability, few works explore this property. We will investigate the use of Partial Effect with the analytical models generated by the Interaction-Transformation Evolutionary Algorithm symbolic regressor (ITEA). We show that the regression models returned by ITEA coupled with Partial Effect provide the closest explanations to the ground truth and a close approximation to Shapley values. These results openup new opportunities to explain symbolic regression modelscompared to the approximations provided by model agnostic approaches.", notes = "PE-ITEA AI-Feynman datasets See also \cite{Aldeia:2022:GPEM} UFABC, Santo Andre, SP, Brazil GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{aldeia:2022:SymReg, author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}", title = "{Interaction-Transformation} Evolutionary Algorithm with coefficients optimization", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "2274--2281", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Representation of mathematical functions, symbolic regression, coefficient optimization, benchmark, evolutionary algorithm", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3533987", size = "8 pages", abstract = "Symbolic Regression is the task of finding a mathematical expression to describe the relationship between one or more independent variables with a dependent variable. The search space can be vast and include any algebraic function; thus, finding optimal values for coefficients may not be a trivial task. The Interaction-Transformation representation alleviates this problem enforcing that the coefficients of the expression is part of a linear transformation, allowing the application of least squares. But this solution also limits the search space of the expressions. This paper proposes four different strategies to optimize the coefficients of the nonlinear part of the Interaction-Transformation representation. We benchmark the proposed strategies by applying the Interaction-Transformation Evolutionary Algorithm (ITEA) to six well-known data sets to evaluate four optimization heuristics combining linear and non-linear methods. The results show that optimizing the non-linear and linear coefficients separately was the best strategy to find better-performing expressions with a higher run-time and expression size. The non-linear optimization method alone was the worst-performing method.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{Aldeia:2022:GPEM, author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}", title = "Interpretability in symbolic regression: a benchmark of explanatory methods using the {Feynman} data set", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "3", pages = "309--349", month = sep, note = "Special Issue: Highlights of Genetic Programming 2021 Events", keywords = "genetic algorithms, genetic programming, Symbolic regression, Explanatory methods, Feature importance attribution, Benchmark", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-022-09435-x", code_url = "https://github.com/gAldeia/iirsBenchmark", abstract = "In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic regression. When using an explanatory method, and since interpretability lacks a rigorous definition, there is a need to evaluate and compare the quality and different explainers. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models. Experiments were performed using 100 physics equations with different interpretable and non-interpretable regression methods and popular explanation methods, evaluating the performance of the explainers performance with several explanation measures. In addition, we further analyzed four benchmarks from the GP community. The results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models that is capable of returning accurate models with appropriate explanations. Regarding the explainers, we observed that Partial Effects and SHAP were the most robust explanation models, with Integrated Gradients being unstable only with tree-based models. This benchmark is publicly available for further experiments.", } @InCollection{alderson:1999:TTCNDUEM, author = "David Alderson", title = "Toward a Technique for Cooperative Network Design Using Evolutionary Methods", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:1999:GAGPs}", } @Article{ALDREES:2024:jwpe, author = "Ali Aldrees and Majid Khan and Abubakr Taha Bakheit Taha and Mujahid Ali", title = "Evaluation of water quality indexes with novel machine learning and {SHapley} Additive {ExPlanation} ({SHAP)} approaches", journal = "Journal of Water Process Engineering", volume = "58", pages = "104789", year = "2024", ISSN = "2214-7144", DOI = "doi:10.1016/j.jwpe.2024.104789", URL = "https://www.sciencedirect.com/science/article/pii/S2214714424000199", keywords = "genetic algorithms, genetic programming, Gene expression programming, Water quality indexes, ANN, Deep neural networks, Optimizable Gaussian process regressor, SHAP", abstract = "Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional modeling approaches rely on extensive datasets with numerous unspecified inputs, leading to time-consuming WQI assessment procedures. Numerous studies have used machine learning (ML) methods for WQI analysis but often lack model interpretability. To address this issue, this study developed five interpretable predictive models, including two gene expression programming (GEP) models, two deep neural networks (DNN) models, and one optimizable Gaussian process regressor (OGPR) model for estimating electrical conductivity (EC) and total dissolved solids (TDS). For the model development, a total of 372 records on a monthly basis were collected in the Upper Indus River at two outlet stations. The efficacy and accuracy of the models were assessed using various statistical measures, such as correlation (R), mean square error (MAE), root mean square error (RMSE), and 5-fold cross-validation. The DNN2 model demonstrated outstanding performance compared to the other five models, exhibiting R-values closer to 1.0 for both EC and TDS. However, the genetic programming-based models, GEP1 and GEP2, exhibited comparatively lower accuracy in predicting the water quality indexes. The SHapely Additive exPlanation (SHAP) analysis revealed that bicarbonate, calcium, and sulphate jointly contribute approximately 78 percent to EC, while the combined presence of sodium, bicarbonate, calcium, and magnesium accounts for around 87 percent of TDS in water. Notably, the influence of pH and chloride was minimal on both water quality indexes. In conclusion, the study highlights the cost-effective and practical potential of predictive models for EC and TDS in assessing and monitoring river water quality", } @InProceedings{Aleb:2012:GECCOcomp, author = "Nassima Aleb and Samir Kechid", title = "A new framework for scalable genetic programming", booktitle = "GECCO 2012 Symbolic regression and modeling workshop", year = "2012", editor = "Steven Gustafson and Ekaterina Vladislavleva", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "487--492", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330859", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a novel framework for scalable multi-objective genetic programming. We introduce a new program modeling aiming at facilitating programs' creation, execution and improvement. The proposed modeling allows making symbolic executions in such a way to reduce drastically the time of programs' executions and to allow well-founded programs recombination.", notes = "Also known as \cite{2330859} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Aleb:2013:GECCOcomp, author = "Nassima Aleb and Samir Kechid", title = "An interpolation based crossover operator for genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1107--1112", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482689", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper presents a new crossover operator for genetic programming. We exploit two concepts of formal methods: Weakest precondition and Craig interpolation, to perform semantically aware crossover. Weakest preconditions are used to locate faulty parts of a program and Craig interpolation is used to correct these ones.", notes = "Also known as \cite{2482689} Distributed at GECCO-2013.", } @Article{Aleksandrov:2013:JCSSI, author = "A. V. Aleksandrov and S. V. Kazakov and A. A. Sergushichev and F. N. Tsarev and A. A. Shalyto", title = "The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior", journal = "Journal of Computer and Systems Sciences International", year = "2013", volume = "52", number = "3", pages = "410--425", month = may, publisher = "SP MAIK Nauka/Interperiodica", language = "English", keywords = "genetic algorithms, genetic programming, FSM", ISSN = "1064-2307", DOI = "doi:10.1134/S1064230713020020", size = "16 pages", abstract = "It is proposed to use evolutionary programming to generate finite state machines (FSMs) for controlling objects with complex behaviour. The well-know approach in which the FSM performance is evaluated by simulation, which is typically time consuming, is replaced with comparison of the object's behaviour controlled by the FSM with the behaviour of this object controlled by a human. A feature of the proposed approach is that it makes it possible to deal with objects that have not only discrete but also continuous parameters. The use of this approach is illustrated by designing an FSM controlling a model aircraft executing a loop-the-loop manoeuvre.", notes = " Original Russian Text A.V. Aleksandrov, S.V. Kazakov, A.A. Sergushichev, F.N. Tsarev, A.A. Shalyto, 2013, published in Izvestiya Akademii Nauk. Teoriya i Sistemy Upravleniya, 2013, No. 3, pp. 85-100. Translated by A. Klimontovich", } @Article{Alekseeva:2018:Algorithms, author = "Natalia Alekseeva and Ivan Tanev and Katsunori Shimohara", title = "Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions", journal = "Algorithms", year = "2018", volume = "11", number = "7", pages = "108", month = jul, note = "Special Issue Algorithms for PID Controller", keywords = "genetic algorithms, genetic programming, autonomous vehicles, automated steering, slippery road conditions, PID controllers", ISSN = "1999-4893", URL = "http://www.mdpi.com/1999-4893/11/7/108", URL = "http://www.mdpi.com/1999-4893/11/7/108/pdf", URL = "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902252618556627", DOI = "doi:10.3390/a11070108", size = "17 pages", abstract = "The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.", notes = "Department of Information Systems Design, Faculty of Engineering, Doshisha University 1-3, Tatara Miyakodani, Kyotanabe City, Kyoto 610-0394, Japan http://www.mdpi.com/journal/algorithms", } @InProceedings{Alekseeva:2019:AIM, author = "Natalia Alekseeva and Ivan Tanev and Katsunori Shimohara", booktitle = "2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)", title = "On the Emergence of Oscillations in the Evolved Autosteering of a Car on Slippery Roads", year = "2019", pages = "1371--1378", month = "8-12 " # jul, address = "Hong Kong", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-2494-0", DOI = "doi:10.1109/AIM.2019.8868610", ISSN = "2159-6255", abstract = "One of the important features of autonomous vehicles is their versatility to various traffic situations and road conditions. We explore the feasibility of using genetic programming to develop an adequate auto-steering of a car in slippery road conditions. We also investigate an important emergent property of the best-evolved steering solutions - the steering oscillations - and discuss how these oscillations contribute to the better controllability of the sliding car. We present the limitations and the technical challenges of the real world implementation of steering oscillations.", notes = "Also known as \cite{8868610}", } @Article{DBLP:journals/algorithms/AlekseevaTS20, author = "Natalia Alekseeva and Ivan Tanev and Katsunori Shimohara", title = "{PD} Steering Controller Utilizing the Predicted Position on Track for Autonomous Vehicles Driven on Slippery Roads", journal = "Algorithms", year = "2020", volume = "13", number = "2", pages = "id 48", month = feb, keywords = "genetic algorithms, genetic programming, autonomous vehicles, automated steering, slippery road conditions, PD controllers, predictive model", timestamp = "Thu, 19 Mar 2020 10:23:44 +0100", biburl = "https://dblp.org/rec/journals/algorithms/AlekseevaTS20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.mdpi.com/1999-4893/13/2/48/pdf", DOI = "doi:10.3390/a13020048", size = "17 pages", abstract = "Among the most important characteristics of autonomous vehicles are the safety and robustness in various traffic situations and road conditions. In this paper, we focus on the development and analysis of the extended version of the canonical proportional-derivative PD controllers that are known to provide a good quality of steering on non-slippery (dry) roads. However, on slippery roads, due to the poor yaw controllability of the vehicle (suffering from understeering and oversteering), the quality of control of such controllers deteriorates. The proposed predicted PD controller (PPD controller) overcomes the main drawback of PD controllers, namely, the reactiveness of their steering behavior. The latter implies that steering output is a direct result of the currently perceived lateral- and angular deviation of the vehicle from its intended, ideal trajectory, which is the center of the lane. This reactiveness, combined with the tardiness of the yaw control of the vehicle on slippery roads, results in a significant lag in the control loop that could not be compensated completely by the predictive (derivative) component of these controllers. In our approach, keeping the controller efforts at the same level as in PD controllers by avoiding (i) complex computations and (ii) adding additional variables, the PPD controller shows better quality of steering than that of the evolved (via genetic programming) models.", } @Article{Alemdag:2016:EG, author = "S. Alemdag and Z. Gurocak and A. Cevik and A. F. Cabalar and C. Gokceoglu", title = "Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming", journal = "Engineering Geology", volume = "203", pages = "70--82", year = "2016", note = "Special Issue on Probabilistic and Soft Computing Methods for Engineering Geology", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2015.12.002", URL = "http://www.sciencedirect.com/science/article/pii/S0013795215300971", abstract = "This paper investigates a series of experimental results and numerical simulations employed to estimate the deformation modulus of a stratified rock mass. The deformation modulus of rock mass has a significant importance for some applications in engineering geology and geotechnical projects including foundation, slope, and tunnel designs. Deformation modulus of a rock mass can be determined using large scale in-situ tests. This large scale sophisticated in-situ testing equipments are sometimes difficult to install, plus time consuming to be employed in the field. Therefore, this study aims to estimate indirectly the deformation modulus values via empirical methods such as the neural network, neuro fuzzy and genetic programming approaches. A series of analyses have been developed for correlating various relationships between the deformation modulus of rock mass, rock mass rating, rock quality designation, uniaxial compressive strength, and elasticity modulus of intact rock parameters. The performance capacities of proposed models are assessed and found as quite satisfactory. At the completion of a comparative study on the accuracy of models, in the results, it is seen that overall genetic programming models yielded more precise results than neural network and neuro fuzzy models.", keywords = "genetic algorithms, genetic programming, Deformation modulus, Rock mass, Neural network, Neuro fuzzy", notes = "Department of Geological Engineering, Gumushane University, Gumushane 29000, Turkey", } @InProceedings{aler:1998:5parity, author = "Ricardo Aler", title = "Immediate transference of global improvements to all individuals in a population in Genetic Programming compared to Automatically Defined Functions for the EVEN-5 PARITY problem", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "60--70", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055928", abstract = "Koza has shown how automatically defined functions (ADFs) can reduce computational effort in the GP paradigm. In Koza's ADF, as well as in standard GP, an improvement in a part of a program (an ADF or a main body) can only be transferred via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to the whole population. A system that implements this idea has been proposed and tested for the EVEN-5-PARITY and EVEN-6-PARITY problems. Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Finally, our work suggests further research where less extreme approaches to our idea could be tested.", notes = "EuroGP'98", affiliation = "Universidad Carlos III de Madrid Butarque 15 28911 Leganes Madrid Espana Butarque 15 28911 Leganes Madrid Espana", } @InProceedings{aler:1998:ehp, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Evolved Heuristics for Planning", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "745--754", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", DOI = "doi:10.1007/BFb0040753", notes = "EP-98 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7 EvoCK compared with PRODIGY. HAMLET. Blocksworld domain.", } @InProceedings{icml98-ricardo, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach", booktitle = "Proceedings of the Fifteenth International Conference on Machine Learning, ICML'98", year = "1998", editor = "Jude Shavlik", pages = "10--18", address = "Madison, Wisconsin, USA", month = jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, Learning in Planning, Multistrategy learning", ISBN = "1-55860-556-8", URL = "http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz", size = "9 pages", abstract = "Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multistrategy approach where an already multistrategy approach ({\sc hamlet} combines analytical and inductive learning) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone.", notes = "ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld many random problems generated in order to train system. No crossover, steady state population size = 2, tournament size = 2", } @PhdThesis{aler:thesis, author = "Ricardo Aler Mur", title = "Programacion Genetica de Heuristicas para Planificacion", school = "Facultad de Informatica de la Universidad Politecnica de Madrid", year = "1999", address = "Spain", month = jul, keywords = "genetic algorithms, genetic programming, Planning, Problem Solving, Rule Based System", URL = "http://oa.upm.es/1101/1/10199907.pdf", size = "167 pages", abstract = "The aim of this thesis is to use and extend the machine learning genetic programming (GP) paradigm to learn control knowledge for domain independent planning. GP will be used as a standalone technique and as part of a multi-strategy system. Planning is the problem of finding a sequence of steps to transform an initial state in a final state. Finding a correct plan is NP-hard. A solution proposed by Artificial Intelligence is to augment a domain independent planner with control knowledge, to improve its efficiency. Machine learning techniques are used for that purpose. However, although a lot has been achieved, the domain independent planning problem has not been solved completely, therefore there is still room for research. The reason for using GP to learn planning control knowledge is twofold. First, it is intended for exploring the control knowledge space in a less biased way than other techniques. Besides, learning search control knowledge with GP will consider the planning system, the domain theory, planning search and efficiency measures in a global manner, all at the same time. Second, GP flexibility will be used to add useful biases and characteristics to another learning method that lacks them (that is, a multi-strategy GP based system). In the present work, Prodigy will be used as the base planner and Hamlet will be used as the learning system to which useful characteristics will be added through GP. In other words, GP will be used to solve some of Hamlet limitations by adding new biases/characteristics to Hamlet. In addition to the main goal, this thesis will design and experiment with methods to add background knowledge to a GP system, without modifying its basic algorithm. The first method seeds the initial population with individuals obtained by another method (Hamlet). Actually, this is the multi-strategy system discussed in the later paragraph. The second method uses a new genetic operator (instance based crossover) that is able to use instances/examples to bias its search, like other machine learning techniques. To test the validity of the methods proposed, extensive empirical and statistical validation will be carried out.", notes = "In Spanish: Genetic Programming of Heuristics for Planning School of Computer Science at Polytechnic University of Madrid Author: Ricardo Aler Mur Supervisors: Daniel Borrajo Millan and Pedro Isasi Vinuela ", } @InProceedings{aler:2000:G, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "GP fitness functions to evolve heuristics for planning", booktitle = "Evolutionary Methods for AI Planning", year = "2000", editor = "Martin Middendorf", pages = "189--195", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://scalab.uc3m.es/~dborrajo/papers/gecco00.ps.gz", abstract = "There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literature. In this paper we emphasise the use of a new one, based on learning heuristics for planning. In particular, we focus on the design of fitness functions for this task. We explore two alternatives (black and white box fitness functions) and present some empirical results", size = "5 pages", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{oai:CiteSeerPSU:341634, title = "Knowledge Representation Issues in Control Knowledge Learning", author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", booktitle = "Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000)", year = "2000", editor = "Pat Langley", pages = "1--8", address = "Stanford University, Standord, CA, USA", month = jun # " 29 - " # jul # " 2", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, EBL, HAMLET, EVOCK", ISBN = "1-55860-707-2", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://scalab.uc3m.es/~dborrajo/papers/icml00.ps.gz", URL = "http://dl.acm.org/citation.cfm?id=645529.657964", acmid = "657964", URL = "http://citeseer.ist.psu.edu/341634.html", citeseer-isreferencedby = "oai:CiteSeerPSU:42967", citeseer-references = "oai:CiteSeerPSU:104987; oai:CiteSeerPSU:15322; oai:CiteSeerPSU:554819", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:341634", rights = "unrestricted", size = "8 pages", abstract = "Knowledge representation is a key issue for any machine learning task. There have already been many comparative studies about knowledge representation with respect to machine learning in classification tasks. However, apart from some work done on reinforcement learning techniques in relation to state representation, very few studies have concentrated on the effect of knowledge representation for machine learning applied to problem solving, and more specifically, to planning. In this paper, we present an experimental comparative study of the effect of changing the input representation of planning domain knowledge on control knowledge learning. We show results in two classical domains using three different machine learning systems, that have previously shown their effectiveness on learning planning control knowledge: a pure EBL mechanism, a combination of EBL and induction (HAMLET), and a Genetic Programming based system (EVOCK).", notes = "Also known as \cite{Aler:2000:KRI:645529.657964} http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2000.html ICML 2000", } @InProceedings{aler:2001:glckg, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Grammars for Learning Control Knowledge with GP", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "1220--1227", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computational linguistics, grammars, learning (artificial intelligence), search problems, AI planning system, EVOCK, Evolution of Control Knowledge, GP based system, PRODIGY, ad-hoc mechanisms, blocksworld domain, control knowledge learning, control rule language, control rule syntax, control rules, grammar approach flexibility, grammar specific, grammars, language restrictions, search space, standard GP, standard select type", ISBN = "0-7803-6658-1", URL = "http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz", DOI = "doi:10.1109/CEC.2001.934330", size = "8 pages", abstract = "In standard GP there are no constraints on the structure to evolve: any combination of functions and terminals is valid. However, sometimes GP is used to evolve structures that must respect some constraints. Instead of ad-hoc mechanisms, grammars can be used to guarantee that individuals comply with the language restrictions. In addition, grammars permit great flexibility to define the search space. EVOCK (Evolution of Control Knowledge) is a GP based system that learns control rules for PRODIGY, an AI planning system. EVOCK uses a grammar to constrain individuals to PRODIGY 4.0 control rule syntax. The authors describe the grammar specific details of EVOCK. Also, the grammar approach flexibility has been used to extend the control rule language used by EVOCK in earlier work. Using this flexibility, tests were performed to determine whether using combinations of several types of control rules for planning was better than using only the standard select type. Experiments have been carried out in the blocksworld domain that show that using the combination of types of control rules does not get better individuals, but it produces good individuals more frequently", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . EVOCK, PRODIGY 4.0, blocksworld", } @Article{aler:2001:ECJ, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Learning to Solve Planning Problems Efficiently by Means of Genetic Programming", journal = "Evolutionary Computation", year = "2001", volume = "9", number = "4", pages = "387--420", month = "Winter", keywords = "genetic algorithms, genetic programming, genetic planning, evolving heuristics, planning, search. EvoCK, STGP, blocks world, logistics, Prodigy4.0, STRIPS, PDL40.", ISSN = "1063-6560", URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841", DOI = "doi:10.1162/10636560152642841", size = "34 pages", abstract = "Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator Instance-Based Crossover that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.", } @Article{aler:2002:AI, author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi", title = "Using genetic programming to learn and improve control knowledge", journal = "Artificial Intelligence", year = "2002", volume = "141", number = "1-2", pages = "29--56", month = oct, keywords = "genetic algorithms, genetic programming, Speedup learning, Multi-strategy learning, Planning", URL = "http://scalab.uc3m.es/~dborrajo/papers/aij-evock.ps.gz", URL = "http://citeseer.ist.psu.edu/511810.html", DOI = "doi:10.1016/S0004-3702(02)00246-1", abstract = "The purpose of this article is to present a multi-strategy approach to learn heuristics for planning. This multi-strategy system, called HAMLET-EVOCK, combines a learning algorithm specialised in planning () and a genetic programming (GP) based system (: Evolution of Control Knowledge). Both systems are able to learn heuristics for planning on their own, but both of them have weaknesses. Based on previous experience and some experiments performed in this article, it is hypothesised that handicaps are due to its example-driven operators and not having a way to evaluate the usefulness of its control knowledge. It is also hypothesized that even if control knowledge is sometimes incorrect, it might be easily correctable. For this purpose, a GP-based stage is added, because of its complementary biases: GP genetic operators are not example-driven and it can use a fitness function to evaluate control knowledge. and are combined by seeding initial population with control knowledge. It is also useful for to start from a knowledge-rich population instead of a random one. By adding the GP stage to , the number of solved problems increases from 58% to 85% in the blocks world and from 50% to 87% in the logistics domain (0% to 38% and 0% to 42% for the hardest instances of problems considered).", notes = "Hamlet, EvoCK, PRODIGY 4.0", } @InProceedings{Aleshunas:2011:CAoUHiA, title = "Cost-benefit Analysis of Using Heuristics in {ACGP}", author = "John Aleshunas and Cezary Janikow", pages = "1177--1183", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2011.5949749", abstract = "Constrained Genetic Programming (CGP) is a method of searching the Genetic Programming search space non-uniformly, giving preferences to certain subspaces according to some heuristics. Adaptable CGP (ACGP) is a method for discovery of the heuristics. CGP and ACGP have previously demonstrated their capabilities using first-order heuristics: parent-child probabilities. Recently, the same advantage has been shown for second-order heuristics: parent- children probabilities. A natural question to ask is whether we can benefit from extending ACGP with deeper-order heuristics. This paper attempts to answer this question by performing cost-benefit analysis while simulating the higher- order heuristics environment. We show that this method cannot be extended beyond the current second or possibly third-order heuristics without a new method to deal with the sheer number of such deeper-order heuristics.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Alexander:2009:cec, author = "B. J. Alexander and M. J. Gratton", title = "Constructing an Optimisation Phase Using Grammatical Evolution", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1209--1216", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P395.pdf", DOI = "doi:10.1109/CEC.2009.4983083", size = "8 pages", abstract = "Optimising compilers present their authors with an intractable design space. A substantial body of work has used heuristic search techniques to search this space for the purposes of adapting optimisers to their environment. To date, most of this work has focused on sequencing, tuning and guiding the actions of atomic hand-written optimisation phases. In this paper we explore the adaption of optimisers at a deeper level by demonstrating that it is feasible to automatically build a non-trivial optimisation phase, for a simple functional language, using Grammatical Evolution. We show that the individuals evolved compare well in performance to a handwritten optimisation phase on a range of benchmarks. We conclude with proposals of how this work and its applications can be extended.", keywords = "genetic algorithms, genetic programming, grammatical evolution, SBSE, evolutionary computation, functional languages, grammars, optimising compilers, search problems, atomic hand-written optimisation phases, heuristic search techniques, intractable design space", notes = "Adl, DMO, C-MPI, FPGA, Semantics of program preserved. libGE, GAlib, effective crossover, Python, SWIG Python/C++. Canonical code. Five second time limit. Haskell. Training examples changed to 'provide traction for the evolutionary process' p1213. 'Evolution of fittest individuals highly discontinuous' p1214. Some examples where GE is competitive with hand written compiler optimisation, others less so. Evolved code _not_ like human. Compiler output _is_ correct. proof-of-concept. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite{4983083}", } @InProceedings{alexander2014boosting, author = "Brad Alexander and Brad Zacher", title = "Boosting Search for Recursive Functions Using Partial Call-Trees", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Brank and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "384--393", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming, grammatical evolution, Recursion, Call-Tree, Adaptive Grammar", DOI = "doi:10.1007/978-3-319-10762-2_38", size = "10 pages", abstract = "Recursive functions are a compact and expressive way to solve challenging problems in terms of local processing. These properties have made recursive functions a popular target for genetic programming. Unfortunately, the evolution of substantial recursive programs has proved difficult. One cause of this problem is the difficulty in evolving both correct base and recursive cases using just information derived from running test cases. In this work we describe a framework that exploits additional information in the form of partial call-trees. Such trees - a by-product of deriving input-output cases by hand - guides the search process by allowing the separate evolution of the recursive case. We show that the speed of evolution of recursive functions is significantly enhanced by the use of partial call-trees and demonstrate application of the technique in the derivation of functions for a suite of numerical functions.", notes = "CTGGP, GE, GElib 0.26, tiny-c-compiler tcc, factorial, oddevens, log2, fib2, fib3, lucas, factorings, summands1 PPSN-XIII Cites \cite{Moraglio:2012:CEC} and \cite{yu:1998:rlaGP98} (in both cases google scholar Feb 2015 data wrong)", } @InCollection{Alexander:2014:shonan, author = "Bradley Alexander", title = "Discussion on Automatic Fault Localisation and Repair", booktitle = "Computational Intelligence for Software Engineering", publisher = "National Institute of Informatics", year = "2014", editor = "Hong Mei and Leandro Minku and Frank Neumann and Xin Yao", pages = "16--19", address = "Japan", month = oct # " 20-23", note = "NII Shonan Meeting Report: No. 2014-13", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", ISSN = "2186-7437", URL = "http://shonan.nii.ac.jp/seminar/reports/wp-content/uploads/sites/56/2015/01/No.2014-13.pdf", size = "4 pages", notes = "Mention of GenProg \cite{DBLP:journals/tse/GouesNFW12} and \cite{Kim:2013:ICSE}, etc. Book also contains abstracts of talks, some on GP. National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan", } @InProceedings{Alexander:2016:PPSN, author = "Brad Alexander and Connie Pyromallis and George Lorenzetti and Brad Zacher", title = "Using Scaffolding with Partial Call-Trees to Improve Search", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "324--334", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Recursion", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_3", size = "11 page", abstract = "Recursive functions are an attractive target for genetic programming because they can express complex computation compactly. However, the need to simultaneously discover correct recursive and base cases in these functions is a major obstacle in the evolutionary search process. To overcome these obstacles two recent remedies have been proposed. The first is Scaffolding which permits the recursive case of a function to be evaluated independently of the base case. The second is Call- Tree-Guided Genetic Programming (CTGGP) which uses a partial call tree, supplied by the user, to separately evolve the parameter expressions for recursive calls. Used in isolation, both of these approaches have been shown to offer significant advantages in terms of search performance. In this work we investigate the impact of different combinations of these approaches. We find that, on our benchmarks, CTGGP significantly outperforms Scaffolding and that a combination CTGGP and Scaffolding appears to produce further improvements in worst-case performance.", notes = "factorial, odd-evens, log2, Fibonacci and Fibonacci-3, the nth Lucas number, the nth Pell number. p331 'We ran our experiments on an AMD Opteron 6348 machine with 48 processors running at 2.8 GHz' PPSN2016", } @Misc{Alexander:2018:arxiv, author = "Brad Alexander", title = "A Preliminary Exploration of Floating Point Grammatical Evolution", howpublished = "arXiv", year = "2018", month = "9 " # jun, keywords = "genetic algorithms, genetic programming, grammatical evolution", volume = "abs/1806.03455", bibdate = "2018-08-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1806.html#abs-1806-03455", URL = "http://arxiv.org/abs/1806.03455", size = "17 pages", abstract = "Current GP frameworks are highly effective on a range of real and simulated benchmarks. However, due to the high dimensionality of the genotypes for GP, the task of visualising the fitness landscape for GP search can be difficult. This paper describes a new framework: Floating Point Grammatical Evolution (FP-GE) which uses a single floating point genotype to encode an individual program. This encoding permits easier visualisation of the fitness landscape arbitrary problems by providing a way to map fitness against a single dimension. The new framework also makes it trivially easy to apply continuous search algorithms, such as Differential Evolution, to the search problem. In this work, the FP-GE framework is tested against several regression problems, visualising the search landscape for these and comparing different search meta-heuristics.", notes = "Also known as \cite{journals/corr/abs-1806-03455}", } @InProceedings{conf/eann/AlexandirisK13, author = "Antonios K. Alexandiris and Michael Kampouridis", title = "Temperature Forecasting in the Concept of Weather Derivatives: a Comparison between Wavelet Networks and Genetic Programming", editor = "Lazaros S. Iliadis and Harris Papadopoulos and Chrisina Jayne", booktitle = "Proceedings of 14th International Conference on Engineering Applications of Neural Networks (EANN 2013), Part {I}", year = "2013", volume = "383", series = "Communications in Computer and Information Science", pages = "12--21", address = "Halkidiki, Greece", month = sep # " 13-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming, weather derivatives, wavelet networks, temperature derivatives", isbn13 = "978-3-642-41012-3", bibdate = "2014-01-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eann/eann2013-1.html#AlexandirisK13", URL = "http://dx.doi.org/10.1007/978-3-642-41013-0", URL = "http://dx.doi.org/10.1007/978-3-642-41013-0_2", DOI = "doi:10.1007/978-3-642-41013-0_2", abstract = "The purpose of this study is to develop a model that accurately describes the dynamics of the daily average temperature in the context of weather derivatives pricing. More precisely we compare two state of the art algorithms, namely wavelet networks and genetic programming against the classic linear approaches widely using in the contexts of temperature derivative pricing. The accuracy of the valuation process depends on the accuracy of the temperature forecasts. Our proposed models were evaluated and compared in-sample and out-of-sample in various locations. Our findings suggest that the proposed nonlinear methods significantly outperform the alternative linear models and can be used for accurate weather derivative pricing.", } @Article{Alexandridis:2017:IJF, author = "Antonis K. Alexandridis and Michael Kampouridis and Sam Cramer", title = "A comparison of wavelet networks and genetic programming in the context of temperature derivatives", journal = "International Journal of Forecasting", volume = "33", number = "1", pages = "21--47", year = "2017", ISSN = "0169-2070", DOI = "doi:10.1016/j.ijforecast.2016.07.002", URL = "http://www.sciencedirect.com/science/article/pii/S0169207016300711", abstract = "The purpose of this study is to develop a model that describes the dynamics of the daily average temperature accurately in the context of weather derivatives pricing. More precisely, we compare two state-of-the-art machine learning algorithms, namely wavelet networks and genetic programming, with the classic linear approaches that are used widely in the pricing of temperature derivatives in the financial weather market, as well as with various machine learning benchmark models such as neural networks, radial basis functions and support vector regression. The accuracy of the valuation process depends on the accuracy of the temperature forecasts. Our proposed models are evaluated and compared, both in-sample and out-of-sample, in various locations where weather derivatives are traded. Furthermore, we expand our analysis by examining the stability of the forecasting models relative to the forecasting horizon. Our findings suggest that the proposed nonlinear methods outperform the alternative linear models significantly, with wavelet networks ranking first, and that they can be used for accurate weather derivative pricing in the weather market.", keywords = "genetic algorithms, genetic programming, Weather derivatives, Wavelet networks, Temperature derivatives, Modelling, Forecasting", } @PhdThesis{Alfaro-Cid:thesis, author = "Maria Eva {Alfaro Cid}", title = "Optimisation of Time Domain Controllers for Supply Ships Using Genetic Algorithms and Genetic Programming", school = "The University of Glasgow", year = "2003", address = "Glasgow, UK", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://casnew.iti.es/papers/ThesisEva.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=49&uin=uk.bl.ethos.398769", size = "348 pages", abstract = "The use of genetic methods for the optimisation of propulsion and heading controllers for marine vessels is presented in this thesis. The first part of this work is a study of the optimisation, using Genetic Algorithms, of controller designs based on a number of different time-domain control methodologies such as PID, Sliding Mode, H? and Pole Placement. These control methodologies are used to provide the structure for propulsion and navigation controllers for a ship. Given the variety in the number of parameters to optimise and the controller structures, the Genetic Algorithm is tested in different control optimisation problems with different search spaces. This study presents how the Genetic Algorithm solves this minimisation problem by evolving controller parameters solutions that satisfactorily perform control duties while keeping actuator usage to a minimum. A variety of genetic operators are introduced and a comparison study is conducted to find the Genetic Algorithm scheme best suited to the parameter controller optimisation problem. The performance of the four control methodologies is also compared. A variation of Genetic Algorithms, the Structured Genetic Algorithm, is also used for the optimisation of the H? controller. The H? controller optimisation presents the difficulty that the optimisation focus is not on parameters but on transfer functions. Structured Genetic Algorithm incorporates hierarchy in the representation of solutions making it very suitable for structural optimisation. The H? optimisation problem has been found to be very appropriate for comparing the performance of Genetic Algorithms versus Structured Genetic Algorithm. During the second part of this work, the use of Genetic Programming to optimise the controller structure is assessed. Genetic Programming is used to evolve control strategies that, given as inputs the current and desired state of the propulsion and heading dynamics, generate the commanded forces required to manoeuvre the ship. Two Genetic Programming algorithms are implemented. The only difference between them is how they generate the numerical constants needed for the solution of the problem. The first approach uses a random generation of constants while the second approach uses a combination of Genetic Programming with Genetic Algorithms. Finally, the controllers optimised using genetic methods are evaluated through computer simulations and real manoeuvrability tests in a laboratory water basin facility. The robustness of each controller is analysed through the simulation of environmental disturbances. Also, optimisations in presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessels used in this study are two scale models of a supply ship called CyberShip I and CyberShip II. The results obtained illustrate the benefits of using Genetic Algorithms and Genetic Programming to optimise propulsion and navigation controllers for surface ships.", notes = "uk.bl.ethos.398769", } @InProceedings{alfespshar05, title = "Clasificaci\'{o}n de Senales de Electroencefalograma Usando Programaci\'{o}n Gen\'{e}tica", author = "Eva Alfaro-Cid and Anna Esparcia-Alc\'{a}zar and Ken Sharman", booktitle = "Actas del IV Congreso Espanol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados ({MAEB}'05)", month = sep, address = "Granada, Spain", year = "2005", keywords = "genetic algorithms, genetic programming", URL = "http://www.iti.upv.es/cas/nade/data/maeb05vfinal.pdf", size = "8 pages", abstract = "En este articulo presentamos una nueva manera de aplicar programacion genetica al problema de clasificacion de series temporales. Eneste caso las series de datos usadas son senalesde electroencefalograma. Se han implementado dos tipos de algoritmos de programaciongenetica: uno de ellos usa programacion distribuida mientras que el otro aplica una tecnica de muestreo aleatorio para evitar el problema de la sobreadaptacion. Los arboles resultantes obtienen porcentajes de aciertos en la clasificacion equivalentes a los que se obtienen usando metodos de clasifficacion tradicionales", notes = "in Spanish", } @InProceedings{eurogp:Alfaro-CidMM05, author = "Eva Alfaro-Cid and Euan William McGookin and David James Murray-Smith", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Evolution of a Strategy for Ship Guidance Using Two Implementations of Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-25436-6", pages = "250--260", DOI = "doi:10.1007/978-3-540-31989-4_22", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "In this paper the implementation of Genetic Programming (GP) to optimise a controller structure for a supply ship is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to manoeuvre the ship. The optimised controllers are evaluated through computer simulations and real manoeuvrability tests in a water basin laboratory. In order to deal with the issue of the generation of numerical constants, two kinds of GP algorithms are implemented. The first one chooses the constants necessary to create the controller structure by random generation . The second algorithm includes a Genetic Algorithms (GAs) technique for the optimisation of such constants. The results obtained illustrate the benefits of using GP to optimise propulsion and navigation controllers for ships.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{conf/esann/Alfaro-CidES06, title = "Using distributed genetic programming to evolve classifiers for a brain computer interface", author = "Eva Alfaro-Cid and Anna Esparcia-Alc{\'a}zar and Ken Sharman", year = "2006", booktitle = "ESANN'2006 proceedings - European Symposium on Artificial Neural Networks", editor = "Michel Verleysen", pages = "59--66", address = "Bruges, Belgium", month = "26-28 " # apr, bibdate = "2006-08-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/esann/esann2006.html#Alfaro-CidES06", keywords = "genetic algorithms, genetic programming", ISBN = "2-930307-06-4", URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-44.pdf", abstract = "The objective of this paper is to illustrate the application of genetic programming to evolve classifiers for multi-channel time series data. The paper shows how high performance distributed genetic programming (GP) has been implemented for evolving classifiers. The particular application discussed herein is the classification of human electroencephalographic (EEG) signals for a brain-computer interface (BCI). The resulting classifying structures provide classification rates comparable to those obtained using traditional, human-designed, classification", notes = "http://www.dice.ucl.ac.be/Proceedings/esann/", } @InProceedings{Alfaro-Cid:2006:CEC, author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alcazar", title = "Evolving a Learning Machine by Genetic Programming", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "958--962", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, simulated annealing, function set, learning machine, learning node, optimization algorithm, simulated annealing", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688316", size = "5 pages", abstract = "We describe a novel technique for evolving a machine that can learn. The machine is evolved using a Genetic Programming (GP) algorithm that incorporates in its function set what we have called a learning node. Such a node is tuned by a second optimisation algorithm (in this case Simulated Annealing), mimicking a natural learning process and providing the GP tree with added flexibility and adaptability. The result of the evolution is a system with a fixed structure but with some variable parameters. The system can then learn new tasks in new environments without undergoing further evolution.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 254--258", } @InProceedings{alshaes2007a, title = "Predicci\'{o}n de quiebra empresarial usando programaci\'{o}n gen\'{e}tica", author = "Eva {Alfaro Cid} and Ken Sharman and Anna I. {Esparcia Alc\'{a}zar}", booktitle = "Actas del V Congreso Espa{\~n}ol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB'07)", editor = "Francisco Almeida Rodriguez and Maria Belen Melian Batista and Jose Andres Moreno Perez and Jose Marcos Moreno Vega", publisher = "La Laguna", month = "Febrero", year = "2007", pages = "703--710", address = "Tenerife, Spain", publisher_address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming", isbn13 = "978-84-690-3470-5", URL = "https://dialnet.unirioja.es/servlet/articulo?codigo=4142085", notes = "Prediccion de quiebra empresarial usando programacion genetica in Spanish http://www.redheur.org/files/MAEBs/MAEB07.pdf", } @InProceedings{alshaescu2007a, title = "Aprendizaje autom\'{a}tico con programaci\'{o}n gen\'{e}tica", author = "Eva {Alfaro Cid} and Ken Sharman and Anna I. {Esparcia Alc\'{a}zar} and Alberto {Cuesta Ca{\~n}ada}", booktitle = "Actas del V Congreso Espa{\~n}ol sobre Metaheur\'{i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB'07)", publisher = "La Laguna", month = "Febrero", year = "2007", pages = "819--826", address = "Tenerife, Spain", publisher_address = "Tenerife, Spain", keywords = "genetic algorithms, genetic programming", isbn13 = "978-84-690-3470-5", URL = "https://dialnet.unirioja.es/servlet/articulo?codigo=4148339", notes = "Aprendizaje automatico con programacion genetica in Spanish http://www.redheur.org/files/MAEBs/MAEB07.pdf", } @InProceedings{alfaro-cid:evows07, author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alc\`azar", title = "A genetic programming approach for bankruptcy prediction using a highly unbalanced database", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "169--178", keywords = "genetic algorithms, genetic programming, SVM", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_19", abstract = "in this paper we present the application of a genetic programming algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database of Spanish companies. The database has two important drawbacks: the number of bankrupt companies is very small when compared with the number of healthy ones (unbalanced data) and a considerable number of companies have missing data. For comparison purposes we have solved the same problem using a support vector machine. Genetic programming has achieved very satisfactory results, improving those obtained with the support vector machine.", notes = "EvoWorkshops2007", } @InProceedings{conf/evoW/Alfaro-CidMGES08, title = "A {SOM} and {GP} Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem", author = "Eva Alfaro-Cid and Antonio Miguel Mora and Juan Juli{\'a}n Merelo Guerv{\'o}s and Anna Esparcia-Alc{\'a}zar and Ken Sharman", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#Alfaro-CidMGES08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "123--132", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_13", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming", } @InProceedings{Alfaro-Cid:2008:cec, author = "E. Alfaro-Cid and P. A. Castillo and A. Esparcia and K. Sharman and J. J. Merelo and A. Prieto and J. L. J. Laredo", title = "Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2902--2908", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0649.pdf", DOI = "doi:10.1109/CEC.2008.4631188", abstract = "In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimise one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimises the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimise both error types in classification: artificial neural networks and function trees ensembles created through multiobjective Optimization. Since the multiobjective Optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{Alfaro-Cid:2008:ieeeITS, author = "Eva Alfaro-Cid and Euan W. McGookin and David J. Murray-Smith and Thor I. Fossen", title = "Genetic Programming for the Automatic Design of Controllers for a Surface Ship", journal = "IEEE Transactions on Intelligent Transportation Systems", year = "2008", month = jun, volume = "9", number = "2", pages = "311--321", keywords = "genetic algorithms, genetic programming, control system synthesis, navigation, propulsion, ships CyberShip II, automatic design, controller structure, navigation controllers, propulsion controllers, supply ship, surface ship", ISSN = "1524-9050", DOI = "doi:10.1109/TITS.2008.922932", URL = "http://results.ref.ac.uk/Submissions/Output/2145080", abstract = "In this paper, the implementation of genetic programming (GP) to design a controller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships.", notes = "Also known as \cite{4517335}", uk_research_excellence_2014 = "This paper represents the outcome of a Marie Curie funded collaborative project with the Norwegian University of Science and Technology (NUST), and is one of the first studies to use Genetic Programming in the design of marine vehicle control systems. The research involved the optimisation of the structure and associated gains of a control system for guiding a surface vessel. The optimised controller was used on the test vehicle in tank trials and evaluated in the Marine Cybernetics Laboratory at NUST (Contact: Thor Fossen, ), to demonstrate the power of this optimisation method for controller design.", } @InProceedings{Alfaro-Cid:2008:HIS, author = "Eva Alfaro-Cid and Anna Esparcia-Alcazar and Ken Sharman and Francisco {Fernandez de Vega} and J. J. Merelo", title = "Prune and Plant: A New Bloat Control Method for Genetic Programming", booktitle = "Eighth International Conference on Hybrid Intelligent Systems, HIS '08", year = "2008", month = sep, pages = "31--35", keywords = "genetic algorithms, genetic programming, bloat control method, genetic operator, prune and plant, time consumption, tree size reduction, mathematical operators, trees (mathematics)", DOI = "doi:10.1109/HIS.2008.127", abstract = "This paper reports a comparison of several bloat control methods and also evaluates a new proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to prove the adequacy of this new method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains prune and plant has demonstrated to be better in terms of fitness, size reduction and time consumption than any of the other bloat control techniques under comparison.", notes = "Also known as \cite{4626601}", } @InCollection{series/sci/Alfaro-CidCSE08, title = "Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming", author = "Eva Alfaro-Cid and Alberto Cuesta-Canada and Ken Sharman and Anna Esparcia-Alcazar", bibdate = "2008-08-26", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci100.html#Alfaro-CidCSE08", booktitle = "Natural Computing in Computational Finance", publisher = "Springer", year = "2008", volume = "100", editor = "Anthony Brabazon and Michael O'Neill", isbn13 = "978-3-540-77476-1", pages = "161--185", series = "Studies in Computational Intelligence", DOI = "doi:10.1007/978-3-540-77477-8_9", chapter = "9", keywords = "genetic algorithms, genetic programming, STGP, SVM", size = "29 pages", abstract = "In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.", } @InProceedings{Alfaro-Cid:2009:evonum, title = "Modeling Pheromone Dispensers Using Genetic Programming", author = "Eva Alfaro-Cid and Anna I. Esparcia-Alc\'{a}zar and Pilar Moya and Beatriu Femenia-Ferrer and Ken Sharman and J. J. Merelo", booktitle = "Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Penousal Machado and Jon McCormack and Michael O'Neill and Ferrante Neri and Mike Preuss and Franz Rothlauf and Ernesto Tarantino and Shengxiang Yang", volume = "5484", series = "Lecture Notes in Computer Science", address = "Tubingen, Germany", year = "2009", pages = "635--644", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01128-3", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1007/978-3-642-01129-0_73", size = "10 pages", abstract = "Mating disruption is an agricultural technique that intends to substitute the use of insecticides for pest control. This technique consists of the diffusion of large amounts of sexual pheromone, so that the males are confused and mating is disrupted. Pheromones are released using devices called dispensers. The speed of release is, generally, a function of time and atmospheric conditions such as temperature and humidity. One of the objectives in the design of the dispensers is to minimise the effect of atmospheric conditions in the performance of the dispenser. With this objective, the Centro de Ecologia Quimica Agricola (CEQA) has designed an experimental dispenser that aims to compete with the dispensers already in the market. The hypothesis we want to validate (and which is based on experimental results) is that the performance of the CEQA dispenser is independent of the atmospheric conditions, as opposed to the most widely used commercial dispenser, Isomate CPlus. This was done using a genetic programming (GP) algorithm. GP evolved functions able to describe the performance of both dispensers and that support the initial hypothesis.", notes = "ECJ. EvoWorkshops2009 held in conjunction with EuroGP2009, EvoCOP2009, EvoBIO2009", } @InProceedings{DBLP:conf/gecco/Alfaro-CidEMMFSP09, author = "Eva Alfaro-Cid and Anna Esparcia-Alcazar and Pilar Moya and J. J. Merelo and Beatriu Femenia-Ferrer and Ken Sharman and Jaime Primo", title = "Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenser", booktitle = "GECCO-2009 Symbolic regression and modeling workshop (SRM)", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2225--2230", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570309", abstract = "The accurate modeling of the release kinetics of pheromone dispensers is a matter or great importance for ensuring that the dispenser field-life covers the flight period of the pest and for optimizing the layout of dispensers in the treated area. A new experimental dispenser has been recently designed by researchers at the Instituto Agroforestal del Mediterraneo - Centro de Ecologia Quimica Agricola (CEQA) of the Universidad Politecnica de Valencia (Spain). The most challenging problem for the modeling of the release kinetics of this dispensers is the difficulty in obtaining experimental measurements for building the model. The procedure for obtaining these data is very costly, both time and money wise, therefore the available data across the whole season are scarce. In prior work we demonstrated the utility of using Genetic Programming (GP) for this particular problem. However, the models evolved by the GP algorithm tend to have discontinuities in those time ranges where there are not available measurements. In this work we propose the use of a multiobjective Genetic Programming for modeling the performance of the CEQA dispenser. We take two approaches, involving two and nine objectives respectively. In the first one, one of the objectives of the GP algorithm deals with how well the model fits the experimental data, while the second objective measures how {"}smooth{"} the model behaviour is. In the second approach we have as many objectives as data points and the aim is to predict each point separately using the remaining ones. The results obtained endorse the utility of this approach for those modeling problems characterized by the lack of experimental data.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @Article{Alfaro-Cid:2010:EC, author = "Eva Alfaro-Cid and J. J. Merelo and Francisco {Fernandez de Vega} and Anna I. Esparcia-Alcazar and Ken Sharman", title = "Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study", journal = "Evolutionary Computation", year = "2010", volume = "18", number = "2", pages = "305--332", month = "Summer", keywords = "genetic algorithms, genetic programming", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2010.18.2.18206", abstract = "This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not derive from its influence on the genetic pool diversity.", } @Article{Alfaro-Cid:2014:EC, author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alcazar", title = "Genetic programming and serial processing for time series classification", journal = "Evolutionary Computation", year = "2014", volume = "22", number = "2", pages = "265--285", month = "Summer", keywords = "genetic algorithms, genetic programming, Classification, time series, serial data processing, real world applications", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00110", size = "20 pages", abstract = "This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for on-line or conference competitions. As there are published results of these two problems this gives us the chance of comparing the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large data sets.", notes = "ECJ. EEG BCI competition II. Ford Classification Challenge", } @Article{alfonseca:2004:GPEM, author = "Manuel Alfonseca and Alfonso Ortega", title = "Book Review: {Grammatical Evolution}: {Evolutionary} Automatic Programming in an Arbitrary Language", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "4", pages = "393", month = dec, keywords = "genetic algorithms, genetic programming, grammatical evolution", ISSN = "1389-2576", URL = "https://rdcu.be/dR8co", DOI = "doi:10.1023/B:GENP.0000036057.27304.5b", size = "1 page", notes = "review of \cite{oneill:book}. Article ID: 5272973", } @Article{journals/biosystems/AlfonsecaG13, title = "Evolving an ecology of mathematical expressions with grammatical evolution", author = "Manuel Alfonseca and Francisco Jose Soler Gil", journal = "Biosystems", year = "2013", number = "2", volume = "111", keywords = "genetic algorithms, genetic programming, grammatical evolution", bibdate = "2014-12-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/biosystems/biosystems111.html#AlfonsecaG13", pages = "111--119", URL = "http://dx.doi.org/10.1016/j.biosystems.2012.12.004", } @Article{journals/complexity/AlfonsecaG15, author = "Manuel Alfonseca and Francisco Jose Soler Gil", title = "Evolving a predator-prey ecosystem of mathematical expressions with grammatical evolution", journal = "Complexity", year = "2015", number = "3", volume = "20", bibdate = "2015-03-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/complexity/complexity20.html#AlfonsecaG15", pages = "66--83", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://dx.doi.org/10.1002/cplx.21507", } @InProceedings{Alghamdi:2019:GI7, author = "Mahfouth Alghamdi and Christoph Treude and Markus Wagner", title = "Toward Human-Like Summaries Generated from Heterogeneous Software Artefacts", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Prague, Czech Republic", pages = "1701--1702", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Heterogeneous software artefacts, extractive summarisation, human-like summaries", isbn13 = "978-1-4503-6748", URL = "https://arxiv.org/abs/1905.02258", URL = "https://ctreude.files.wordpress.com/2019/05/gi19.pdf", DOI = "doi:10.1145/3319619.3326814", size = "2 pages", abstract = "Automatic text summarisation has drawn considerable interest in the field of software engineering. It can improve the efficiency of software developers, enhance the quality of products, and ensure timely delivery. In this paper, we present our initial work towards automatically generating human-like multi-document summaries from heterogeneous software artefacts. Our analysis of the text properties of 545 human-written summaries from 15 software engineering projects will ultimately guide heuristics searches in the automatic generation of human-like summaries.", } @InProceedings{Alghieth:2015:INISTA, author = "Manal Alghieth and Yingjie Yang and Francisco Chiclana", booktitle = "2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)", title = "Development of {2D} curve-fitting genetic/gene-expression programming technique for efficient time-series financial forecasting", year = "2015", abstract = "Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. Therefore, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The research presented in this paper aims at the modelling and prediction of short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technique uses hierarchically defined GP and GEP techniques to tune algebraic functions representing the fittest equation for stock market activities. The proposed methodology is evaluated against five well-known stock market companies with each having its own trading circumstances during the past 20+ years. The proposed GEP/GP methodologies were evaluated based on variable window/population sizes, selection methods, and Elitism, Rank and Roulette selection methods. The Elitism-based approach showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 93.46percent for short-term 5-day and 92.105 for medium-term 56-day trading periods.", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1109/INISTA.2015.7276734", month = sep, notes = "Fac. of Technol., De Montfort Univ., Leicester, UK Also known as \cite{7276734}", } @InProceedings{Alghieth:2016:CEC, author = "Manal Alghieth and Yingjie Yang and Francisco Chiclana", title = "Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2381--2388", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, gene expressing programming, Stock market, Time series financial forecasting", isbn13 = "978-1-5090-0623-6", URL = "https://www.dora.dmu.ac.uk/handle/2086/11896", DOI = "doi:10.1109/CEC.2016.7744083", size = "8 pages", abstract = "This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The aim of this research is to model and predict short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technology proposes a fractional adaptive mutation rate Elitism (GEPFAMR) technique to initiate a balance between varied mutation rates and between varied-fitness chromosomes, thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against different dataset and selection methods and showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96percent for short-term 5-day and 95.35percent for medium-term 56-day trading periods.", notes = "WCCI2016 Centre for Computational Intelligence, Faculty of Technology, De Montfort University, Leicester, UK", } @Article{ALHAMED:2022:energy, author = "Khaled H. M. Al-Hamed and Ibrahim Dincer", title = "Exergoeconomic analysis and optimization of a solar energy-based integrated system with oxy-combustion for combined power cycle and carbon capturing", journal = "Energy", volume = "250", pages = "123814", year = "2022", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2022.123814", URL = "https://www.sciencedirect.com/science/article/pii/S0360544222007174", keywords = "genetic algorithms, genetic programming, Ammonia, Carbon capture, Energy, Exergoeconomic analysis, Gas turbine, Optimization", abstract = "This work presents a newly developed integrated system that produces multiple useful products, namely electricity, space cooling, freshwater, and ammonium bicarbonate. The two sources of energy for this integrated system are solar energy and natural gas. The natural gas is consumed in an oxy-combustion Brayton cycle to produce electricity, while the solar energy provides electric power to the carbon capturing unit to produce ammonium bicarbonate as a valuable chemical product to compensate for the operation costs of carbon capture. This integrated system is studied using the exergoeconomic analysis and the multi-objective optimization method of genetic programming and genetic algorithm to enhance the thermodynamic and economic aspects of this system. Applying such an analysis to this integrated system adds more understanding and knowledge on how effectively and efficiently this carbon capture system operates and whether or not it is financially viable to pursue this integrated system for further prototyping and concept demonstration. The results of this exergoeconomic analysis show that the production cost of ammonium bicarbonate per 1 kg in this integrated system is 0.0687 $ kg-1, and this is much lower than the market price. This means that producing ammonium bicarbonate as a way to capture carbon dioxide is feasible financially. Furthermore, the optimization results show that the overall exergy destruction rate and the overall unit cost of products are 86,000 kW and 5.19 times 10-3 $ kJ-1, respectively, when operated under optimum conditions", } @InProceedings{alharthi:2025:EuroGP, author = "Khulud Alharthi and S Zahraa Abdallah and Sabine Hauert", title = "Ghost Swarms: Learning Swarm Rules from Environmental Changes Alone", booktitle = "European Conference on Genetic Programming, EuroGP 2025", year = "2025", editor = "Bing Xue and Luca Manzoni and Illya Bakurov", series = "LNCS", address = "Trieste", month = "23-25 " # apr, publisher = "Springer Nature", note = "Forthcoming", keywords = "genetic algorithms, genetic programming, Swarm Behaviour, Imitation Learning, Environmental Imprints", abstract = "Swarm behaviours emerge from agents interacting with their local environment following simple rules. While directly observing each agent can be challenging, their collective behaviour leaves detectable environmental imprints that could offer insights into the underlying swarm dynamics. However, this task is complex due to the hidden and interconnected relationships between the rules governing agent interactions, the emergent swarm behaviour, and the environmental changes generated by this behaviour. In this work, we propose a method for extracting human-readable controllers from demonstrations showing only observable environmental imprints caused by the swarm. This approach explores whether these environmental imprints can reveal the swarm's actions, even when the individual agents are challenging to track. Our approach eliminates the need for prior knowledge about the controller or its structure, enabling the successful learning of controllers from a single demonstration. We provide a novel method for understanding and managing both natural and engineered swarms by using the environmental imprints left by swarm behaviours, even when direct observation of the swarm's actions is not feasible.", notes = "also known as \cite{paper_221_environmental_changes_alone} Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025", } @InProceedings{Alhejali:2010:UKCI, author = "Atif M. Alhejali and Simon M. Lucas", title = "Evolving diverse {Ms. Pac-Man} playing agents using genetic programming", booktitle = "UK Workshop on Computational Intelligence (UKCI 2010)", year = "2010", month = "8-10 " # sep, pages = "1--6", abstract = "This paper uses genetic programming (GP) to evolve a variety of reactive agents for a simulated version of the classic arcade game Ms. Pac-Man. A diverse set of behaviours were evolved using the same GP setup in three different versions of the game. The results show that GP is able to evolve controllers that are well-matched to the game used for evolution and, in some cases, also generalise well to previously unseen mazes. For comparison purposes, we also designed a controller manually using the same function set as GP. GP was able to significantly outperform this hand-designed controller. The best evolved controllers are competitive with the best reactive controllers reported for this problem.", keywords = "genetic algorithms, genetic programming, Ms PacMan game, reactive agents, computer games, learning (artificial intelligence), software agents", DOI = "doi:10.1109/UKCI.2010.5625586", notes = "Also known as \cite{5625586}", } @InProceedings{Alhejali:2011:CIG, author = "Atif M. Alhejali and Simon M. Lucas", title = "Using a Training Camp with Genetic Programming to Evolve {Ms Pac-Man} Agents", booktitle = "Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games", year = "2011", pages = "118--125", address = "Seoul, South Korea", month = "31 " # aug # " - 3 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Pac-Man, Evolving Controllers, Decomposition learning, Training camp", isbn13 = "978-1-4577-0010-1", URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper31.pdf", DOI = "doi:10.1109/CIG.2011.6031997", size = "8 pages", abstract = "This paper investigates using a training camp in conjunction with Genetic Programming in the evolution of Ms Pac-Man playing agents. We measure the amount of effort, time and resources required to run the training camp successfully. The approach is compared with standard GP. The results indicate that better and more stable performance can be achieved using the training camp method at the expense of greater manual effort in the design of the training scenarios. However, in addition to the better results, the training camp also provides more detailed insight into the strengths and weaknesses of each controller.", notes = "Also known as \cite{6031997}", } @InProceedings{Alhejali:2013:CIG, author = "Atif M. Alhejali and Simon M. Lucas", title = "Using genetic programming to evolve heuristics for a Monte Carlo Tree Search {Ms Pac-Man} agent", booktitle = "IEEE Conference on Computational Intelligence in Games (CIG 2013)", year = "2013", month = "11-13 " # aug, address = "Niagara Falls, Canada", keywords = "genetic algorithms, genetic programming, Monte Carlo methods, artificial intelligence, computer games, tree searching, Al, MCTS, Monte Carlo tree search Ms Pac-Man agent, evolved default policy, game artificial intelligence, random agent, random default policy, Equations, Games, Mathematical model, Monte Carlo methods, Sociology, Monte Carlo Tree Search, Pac-Man", ISSN = "2325-4270", isbn13 = "978-1-4673-5311-3", DOI = "doi:10.1109/CIG.2013.6633639", abstract = "Ms Pac-Man is one of the most challenging test beds in game artificial intelligence (AI). Genetic programming and Monte Carlo Tree Search (MCTS) have already been successful applied to several games including Pac-Man. In this paper, we use Monte Carlo Tree Search to create a Ms Pac-Man playing agent before using genetic programming to enhance its performance by evolving a new default policy to replace the random agent used in the simulations. The new agent with the evolved default policy was able to achieve an 18percent increase on its average score over the agent with random default policy.", notes = "Also known as \cite{6633639}", } @PhdThesis{Alhejali:thesis, author = "Atif Mansour Alhejali", title = "Genetic Programming and the Evolution of Games Playing Agents", school = "Computing and Electronic Systems, University of Essex", year = "2013", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=5796", notes = "Supervisor Simon M. Lucas", } @Article{ali:2004:GPEM, author = "B. Ali and A. E. A. Almaini and T. Kalganova", title = "Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", month = mar, keywords = "genetic algorithms, evolvable hardware, sequential circuits, state assignment", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017009.11392.e2", abstract = "design synchronous sequential logic circuits with minimum number of logic gates is suggested. The proposed method consists of four main stages. The first stage is concerned with the use of genetic algorithms (GA) for the state assignment problem to compute optimal binary codes for each symbolic state and construct the state transition table of finite state machine (FSM). The second stage defines the subcircuits required to achieve the desired functionality. The third stage evaluates the subcircuits using extrinsic Evolvable Hardware (EHW). During the fourth stage, the final circuit is assembled. The obtained results compare favourably against those produced by manual methods and other methods based on heuristic techniques.", notes = "Article ID: 5264733", } @InCollection{Ali:2008:GPTP, author = "Mostafa Z. Ali and Robert G. Reynolds and Xiangdong Che", title = "Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "16", pages = "249--269", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", DOI = "doi:10.1007/978-0-387-87623-8_16", size = "20 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming", } @Article{DBLP:journals/evi/AliM20, author = "Muhammad Quamber Ali and Hammad Majeed", title = "Difficult first strategy {GP:} an inexpensive sampling technique to improve the performance of genetic programming", journal = "Evol. Intell.", volume = "13", number = "4", pages = "537--549", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s12065-020-00355-2", DOI = "doi:10.1007/s12065-020-00355-2", timestamp = "Tue, 20 Oct 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/evi/AliM20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/nca/AliJAAM22, author = "Mir Masoud Ale Ali and Ali Jamali and A. Asgharnia and R. Ansari and Rammohan Mallipeddi", title = "Multi-objective Lyapunov-based controller design for nonlinear systems via genetic programming", journal = "Neural Computing and Applications", year = "2022", volume = "34", number = "2", pages = "1345--1357", month = jan, keywords = "genetic algorithms, genetic programming, Lyapunov function, Stability, Region of attraction, Pareto", ISSN = "0941-0643", URL = "https://rdcu.be/dl3Cd", URL = "https://doi.org/10.1007/s00521-021-06453-1", DOI = "doi:10.1007/s00521-021-06453-1", timestamp = "Tue, 08 Feb 2022 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/nca/AliJAAM22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "13 pages", abstract = "In system control, stability is considered the most important factor as unstable system is impractical or dangerous to use. Lyapunov direct method, one of the most useful tools in the stability analysis of nonlinear systems, enables the design of a controller by determining the region of attraction (ROA). However, the two main challenges posed are (1) it is hard to determine the scalar function referred to as Lyapunov function, and (2) the optimality of the designed controller is generally questionable. In this paper, multi-objective genetic programming (MOGP)-based framework is proposed to obtain both optimal Lyapunov and control functions at the same time. In other words, MOGP framework is employed to minimize several time-domain performances as well as the ROA radius to find the optimal Lyapunov and control functions. The proposed framework is tested in several nonlinear benchmark systems, and the control performance is compared with state-of-the-art algorithms.", notes = "Duffing oscillator Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran", } @Article{ALI:2018:AFM, author = "Mumtaz Ali and Ravinesh C. Deo and Nathan J. Downs and Tek Maraseni", title = "Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach", journal = "Agricultural and Forest Meteorology", volume = "263", pages = "428--448", year = "2018", keywords = "genetic algorithms, genetic programming, Crop yield prediction, Cotton yield, Climate data, Markov Chain Monte Carlo based copula model", ISSN = "0168-1923", DOI = "doi:10.1016/j.agrformet.2018.09.002", URL = "http://www.sciencedirect.com/science/article/pii/S0168192318302971", abstract = "Reliable data-driven models designed to accurately estimate cotton yield, an important agricultural commodity, can be adopted by farmers, agricultural system modelling experts and agricultural policy-makers in strategic decision-making processes. In this paper a hybrid genetic programing model integrated with the Markov Chain Monte Carlo (MCMC) based Copula technique is developed to incorporate climate-based inputs as the predictors of cotton yield, for selected study regions: Faisalabad", } @InCollection{ALI:2020:HPM, author = "Mumtaz Ali and Ravinesh C. Deo", title = "Chapter 2 - Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression", editor = "Pijush Samui and Dieu {Tien Bui} and Subrata Chakraborty and Ravinesh C. Deo", booktitle = "Handbook of Probabilistic Models", publisher = "Butterworth-Heinemann", pages = "37--87", year = "2020", isbn13 = "978-0-12-816514-0", DOI = "doi:10.1016/B978-0-12-816514-0.00002-3", URL = "http://www.sciencedirect.com/science/article/pii/B9780128165140000023", keywords = "genetic algorithms, genetic programming, Agricultural precision, Artificial neural network, Minimax probability machine regression, Wheat yield model", abstract = "In precision agriculture, data-intelligent algorithms applied for predicting wheat yield can generate crucial information about enhancing crop production and strategic decision-making. In this chapter, artificial neural network (ANN) model is trained with three neighboring station-based wheat yields to predict the yield for two nearby objective stations that share a common geographic boundary in the agricultural belt of Pakistan. A total of 2700 ANN models (with a combination of hidden neurons, training algorithm, and hidden transfer/output functions) are developed by trial-and-error method, attaining the lowest mean square error, in which the 90 best-ranked models for 3-layered neuronal network are used for wheat prediction. Models such as learning algorithms comprised of pure linear, tangent, and logarithmic sigmoid equations in hidden transfer/output functions, executed by Levenberg-Marquardt, scaled conjugate gradient, conjugate gradient with Powell-Beale restarts, Broyden-Fletcher-Goldfarb-Shanno quasi-Newton, Fletcher-Reeves update, one-step secant, conjugate gradient with Polak-Ribiere updates, gradient descent with adaptive learning, gradient descent with momentum, and gradient descent with momentum adaptive learning method are trained. For the predicted wheat yield at objective station 1 (i.e., Toba Taik Singh), the optimal architecture was 3-14-1 (input-hidden-output neurons) trained with the Levenberg-Marquardt algorithm and logarithmic sigmoid as activation and tangent sigmoid as output function, while at objective station 2 (i.e., Bakkar), the Levenberg-Marquardt algorithm provided the best architecture (3-20-1) with pure liner as activation and tangent sigmoid as output function. The results are benchmarked with those from minimax probability machine regression (MPMR) and genetic programming (GP) in accordance with statistical analysis of predicted yield based on correlations (r), Willmott's index (WI), Nash-Sutcliffe coefficient (EV), root mean-squared error (RMSE), and mean absolute error (MAE). For objective station 1, the ANN model attained the r value of approximately 0.983, with WIapprox0.984 and EVapprox0.962, while the MPMR model attained rapprox0.957, WIapprox0.544, and EVapprox0.527, with the results attained by GP model, rapprox0.982, WIapprox0.980, and EVapprox0.955. For optimal ANN model, a relatively low value of RMSE approx 192.02kg/ha and MAE approx 162.75kg/ha was registered compared with the MPMR (RMSE approx 614.46kg/ha; MAE approx 431.29kg/ha) and GP model (RMSE approx 209.25kg/ha; MAE approx 182.84kg/ha). For both objective stations, ANN was found to be superior, as confirmed by a larger Legates-McCabe's (LM) index used in conjunction with relative RMSE and MAE. Accordingly, it is averred that ANN is considered as a useful data-intelligent contrivance for predicting wheat yield by using nearest neighbor yield", } @Article{Ali:2015:JBI, author = "Safdar Ali and Abdul Majid", title = "{Can-Evo-Ens}: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences", journal = "Journal of Biomedical Informatics", volume = "54", pages = "256--269", year = "2015", month = apr, keywords = "genetic algorithms, genetic programming, Breast cancer, Amino acids, Physicochemical properties, Stacking ensemble", ISSN = "1532-0464", URL = "http://www.sciencedirect.com/science/article/pii/S1532046415000064", DOI = "doi:10.1016/j.jbi.2015.01.004", abstract = "The diagnostic of human breast cancer is an intricate process and specific indicators may produce negative results. In order to avoid misleading results, accurate and reliable diagnostic system for breast cancer is indispensable. Recently, several interesting machine-learning (ML) approaches are proposed for prediction of breast cancer. To this end, we developed a novel classifier stacking based evolutionary ensemble system Can-Evo-Ens for predicting amino acid sequences associated with breast cancer. In this paper, first, we selected four diverse-type of ML algorithms of Naive Bayes, K-Nearest Neighbour, Support Vector Machines, and Random Forest as base-level classifiers. These classifiers are trained individually in different feature spaces using physicochemical properties of amino acids. In order to exploit the decision spaces, the preliminary predictions of base-level classifiers are stacked. Genetic programming (GP) is then employed to develop a meta-classifier that optimal combine the predictions of the base classifiers. The most suitable threshold value of the best-evolved predictor is computed using Particle Swarm Optimisation technique. Our experiments have demonstrated the robustness of Can-Evo-Ens system for independent validation dataset. The proposed system has achieved the highest value of Area Under Curve (AUC) of ROC Curve of 99.95percent for cancer prediction. The comparative results revealed that proposed approach is better than individual ML approaches and conventional ensemble approaches of AdaBoostM1, Bagging, GentleBoost, and Random Subspace. It is expected that the proposed novel system would have a major impact on the fields of Biomedical, Genomics, Proteomics, Bioinformatics, and Drug Development.", } @PhdThesis{Ali:thesis, author = "Safdar Ali", title = "Intelligent Decision Making Ensemble Classification System for Breast Cancer Prediction", school = "Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences", year = "2015", address = "Nilore, Islamabad, Pakistan", month = "27 " # jul, keywords = "genetic algorithms, genetic programming, Can-Evo-Ens", URL = "http://faculty.pieas.edu.pk/abdulmajid/", URL = "http://prr.hec.gov.pk/jspui/handle/123456789//7613", URL = "https://prr.hec.gov.pk/jspui/bitstream/123456789/7613/1/Safdar-Ali_Computer_and_Information_Sciences_2015_PIEAS_ISD%20PDF.pdf", size = "159 pages", abstract = "Breast cancer is a complex and heterogeneous disease which seriously impacts women's health. The diagnostic of breast cancer is an intricate process. Therefore, an accurate and reliable prediction system for breast cancer is indispensable to avoid misleading results.In this regard, improved decision support systems are essential for breast cancer prediction. Consequently, this thesis focuses on the development of intelligent decision making systems using ensemble classification for the early prediction of breast cancer. Proteins of a breast tissue generally reflect the initial changes caused by successive genetic mutations, which may lead to cancer. In this research, such changes in protein sequences are exploited for the early diagnosis of breast cancer. It is found that substantial variation of Proline, Serine, Tyrosine, Cysteine, Arginine, and Asparagine amino acid molecules in cancerous proteins offer high discrimination for cancer diagnostic. Molecular descriptors derived from physicochemical properties of amino acids are used to transform primary protein sequences into feature spaces of amino acid composition (AAC), split amino acid composition (SAAC), pseudo amino acid composition-series (PseAAC-S), and pseudo amino acid composition-parallel (PseAAC-P). The research work in this thesis is divided in two phases. In the first phase, the basic framework is established to handle imbalanced dataset in order to enhance true prediction performance. In this phase, conventional individual learning algorithms are employed to develop different prediction systems. Firstly, in conjunction with oversampling based Mega-Trend-Diffusion (MTD) technique, individual prediction systems are developed. Secondly, homogeneous ensemble systems CanPro-IDSS and Can-CSCGnB are developed using MTD and cost-sensitive classifier (CSC) techniques, respectively. It is found that assimilation of MTD technique for the CanPro-IDSS system is superior than CSC based technique to handle imbalanced dataset of protein sequences. In this connection, a web based CanPro-IDSS cancer prediction system is also developed. Lastly, a novel heterogeneous ensemble system called IDMS-HBC is developed for breast cancer detection. The second phase of this research focuses on the exploitation of variation of amino acid molecules in cancerous protein sequences using physicochemical properties. In this phase, unlike traditional ensemble prediction approaches, the proposed IDM-PhyChm-Ens ensemble system is developed by combining the decision spaces of a specific classifier trained on different feature spaces. This intelligent ensemble system is constructed using diverse learning algorithms of Random Forest(RF), Support Vector Machines, K-Nearest Neighbor, and Naive Bayes (NB). It is observed that the combined spaces of SAAC+PseAAC-S and AAC+SAAC possess the best discrimination using ensemble-RF and ensemble-NB. Lastly, a novel classifier stacking based evolutionary ensemble system Can-Evo-Ens is also developed, whereby Genetic programming is used as the ensemble method. This study revealed that PseAAC-S feature space carries better discrimination power compared to AAC, SAAC, and PseAAC-P based feature extraction strategies. Intensive experiments are performed to evaluate the performance of the proposed intelligent decision making systems for cancer/non-cancer and breast/non-breast cancer datasets. The proposed approaches have demonstrated improvement over previous state-of-the-art approaches. The proposed systems maybe useful for academia, practitioners, and clinicians for the early diagnosis of breast cancer using protein sequences. Finally, it is expected that the findings of this research would have positive impact on diagnosis, prevention, treatment, and management of cancer", notes = "In English. PIEAS, Islamabad Supervisor: Abdul Majid Co-Supervisor: Asifullah Khan", } @Article{Ali:2010:ieeeTSE, author = "Shaukat Ali and Lionel C. Briand and Hadi Hemmati and Rajwinder K. Panesar-Walawege", title = "A Systematic Review of the Application and Empirical Investigation of Search-Based Test-Case Generation", journal = "IEEE Transactions on Software Engineering", year = "2010", volume = "36", number = "6", pages = "742--762", month = nov # "-" # dec, keywords = "genetic algorithms, genetic programming, SBSE", ISSN = "0098-5589", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463", DOI = "doi:10.1109/TSE.2009.52", size = "22 pages", abstract = "Metaheuristic search techniques have been extensively used to automate the process of generating test cases and thus providing solutions for a more cost-effective testing process. This approach to test automation, often coined as Search-based Software Testing (SBST), has been used for a wide variety of test case generation purposes. Since SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents the results of a systematic, comprehensive review that aims at characterising how empirical studies have been designed to investigate SBST cost-effectiveness and what empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well designed empirical studies.", notes = "cites one GP paper: \cite{Wappler:2007:ASE}. TSESI-2008-09-0283", } @InProceedings{Ali:2011:ICCNIT, author = "Zulfiqar Ali and Waseem Shahzad", title = "Critical analysis of swarm intelligence based routing protocols in adhoc and sensor wireless networks", booktitle = "International Conference on Computer Networks and Information Technology (ICCNIT 2011)", year = "2011", month = "11-13 " # jul, pages = "287--292", address = "Abbottabad", size = "6 pages", abstract = "There are various bio inspired and evolutionary approaches including genetic programming (GP), Neural Network, Evolutionary programming (EP), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) used for the routing protocols in ad hoc and sensor wireless networks. There are constraints involved in these protocols due to the mobility and non infrastructure nature of an ad hoc and sensor networks. We study in this research work a probabilistic performance evaluation frameworks and Swarm Intelligence approaches (PSO, ACO) for routing protocols. The performance evaluation metrics employed for wireless and ad hoc routing algorithms is routing overhead, route optimality, and energy consumption. This survey gives critical analysis of PSO and ACO based algorithms with other approaches applied for the optimisation of an ad hoc and wireless sensor network routing protocols.", keywords = "genetic algorithms, ACO, EP, PSO, adhoc network, ant colony optimisation, bioinspired approach, critical analysis, energy consumption, evolutionary approach, evolutionary programming, mobility nature, neural network, particle swarm optimisation, probabilistic performance evaluation framework, route optimality, routing overhead, routing protocol, swarm intelligence, wireless sensor network, evolutionary computation, mobile ad hoc networks, mobility management (mobile radio), particle swarm optimisation, performance evaluation, routing protocols, wireless sensor networks", DOI = "doi:10.1109/ICCNIT.2011.6020945", ISSN = "2223-6317", notes = "not on GP Also known as \cite{6020945}", } @InProceedings{Ali:2012:SETIT, author = "Samaher Hussein Ali", booktitle = "6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2012)", title = "Miner for {OACCR}: Case of medical data analysis in knowledge discovery", year = "2012", pages = "962--975", month = "21-24 " # mar, address = "Sousse, Tunisia", keywords = "genetic algorithms, genetic programming, data mining, medical administrative data processing, OACCR, TreeNet classifier, astroinformatics, bioinformatics, data mining algorithm, datasets, genetic programming data construction method, geoinformatics, hybrid techniques, knowledge discovery, medical data analysis, obtaining accurate and comprehensible classification rules, principle component analysis, scientific World Wide Web, Algorithm design and analysis, Classification algorithms, Clustering algorithms, Data mining, Databases, Training, Vegetation, Adboosting, FP-Growth, GPDCM, PCA, Random Forest", isbn13 = "978-1-4673-1657-6", DOI = "doi:10.1109/SETIT.2012.6482043", size = "14 pages", abstract = "Modern scientific data consist of huge datasets which gathered by a very large number of techniques and stored in much diversified and often incompatible data repositories as data of bioinformatics, geoinformatics, astroinformatics and Scientific World Wide Web. At the other hand, lack of reference data is very often responsible for poor performance of learning where one of the key problems in supervised learning is due to the insufficient size of the training dataset. Therefore, we try to suggest a new development a theoretically and practically valid tool for analysing small of sample data remains a critical and challenging issue for researches. This paper presents a methodology for Obtaining Accurate and Comprehensible Classification Rules (OACCR) of both small and huge datasets with the use of hybrid techniques represented by knowledge discovering. In this article the searching capability of a Genetic Programming Data Construction Method (GPDCM) has been exploited for automatically creating more visual samples from the original small dataset. Add to that, this paper attempts to developing Random Forest data mining algorithm to handle missing value problem. Then database which describes depending on their components were built by Principle Component Analysis (PCA), after that, association rule algorithm to the FP-Growth algorithm (FP-Tree) was used. At the last, TreeNet classifier determines the class under which each association rules belongs to was used. The proposed methodology provides fast, Accurate and comprehensible classification rules. Also, this methodology can be use to compression dataset in two dimensions (number of features, number of records).", notes = "Also known as \cite{6482043}", } @InProceedings{conf/icaart/Ali0NR21, author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "{AutoGE}: A Tool for Estimation of Grammatical Evolution Models", booktitle = "Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021", year = "2021", editor = "Ana Paula Rocha and Luc Steels and H. Jaap {van den Herik}", volume = "2", pages = "1274--1281", address = "Online", month = feb # " 4-6", publisher = "SCITEPRESS", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-989-758-484-8", bibdate = "2021-03-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaart/icaart2021-2.html#Ali0NR21", DOI = "doi:10.5220/0010393012741281", } @InProceedings{conf/ijcci/Ali0NR21, author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "Towards Automatic Grammatical Evolution for Real-world Symbolic Regression", booktitle = "Proceedings of the 13th International Joint Conference on Computational Intelligence, IJCCI", year = "2021", editor = "Thomas Baeck and Christian Wagner and Jonathan M. Garibaldi and H. K. Lam and Marie Cottrell and Juan Julian Merelo and Kevin Warwick", pages = "68--78", address = "Online", month = oct # " 25-27", organization = "INSTICC", publisher = "SCITEPRESS", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar pruning, effective genome length", isbn13 = "978-989-758-534-0", bibdate = "2021-11-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2021.html#Ali0NR21", DOI = "doi:10.5220/0010691500003063", size = "11 pages", abstract = "AutoGE (Automatic Grammatical Evolution) is a tool designed to aid users of GE for the automatic estimation of Grammatical Evolution (GE) parameters, a key one being the grammar. The tool comprises of a rich suite of algorithms to assist in fine tuning a BNF (Backus-Naur Form) grammar to make it adaptable across a wide range of problems. It primarily facilitates the identification of better grammar structures and the choice of function sets to enhance existing fitness scores at a lower computational overhead. we discuss and report experimental results for our Production Rule Pruning algorithm from AutoGE which employs a simple frequency-based approach for eliminating less useful productions. It captures the relationship between production rules and function sets involved in the problem domain to identify better grammar. The experimental study incorporates an extended function set and common grammar structures for grammar definition. Preliminary results based on ten popular real-world regression datasets demonstrate that the proposed algorithm not only identifies suitable grammar structures, but also prunes the grammar which results in shorter genome length for every problem, thus optimising memory usage. Despite using a fraction of budget in pruning, AutoGE was able to significantly enhance test scores for 3 problems.", notes = "Biocomputing and Developmental Systems Lab, University of Limerick, Ireland", } @InProceedings{ali:2022:GECCO, author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan", title = "Automated Grammar-based Feature Selection in Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "902--910", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature selection, symbolic regression, production ranking, grammatical evolution, grammar pruning", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528852", abstract = "With the growing popularity of machine learning (ML), regression problems in many domains are becoming increasingly high-dimensional. Identifying relevant features from a high-dimensional dataset still remains a significant challenge for building highly accurate machine learning models.Evolutionary feature selection has been used for high-dimensional symbolic regression using Genetic Programming (GP). While grammar based GP, especially Grammatical Evolution (GE), has been extensively used for symbolic regression, no systematic grammar-based feature selection approach exists. This work presents a grammar-based feature selection method, Production Ranking based Feature Selection (PRFS), and reports on the results of its application in symbolic regression.The main contribution of our work is to demonstrate that the proposed method can not only consistently select the most relevant features, but also significantly improves the generalization performance of GE when compared with several state-of-the-art ML-based feature selection methods. Experimental results on benchmark symbolic regression problems show that the generalization performance of GE using PRFS was significantly better than that of a state-of-the-art Random Forest based feature selection in three out of four problems, while in fourth problem the performance was the same.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @PhdThesis{Muhammad_Sarmad_Ali:thesis, author = "Muhammad Sarmad Ali", title = "Automatic Production Selection in Grammatical Evolution", school = "Lero, Faculty of Science and Engineering, Department of Computer Science \& Information Systems, University of Limerick", year = "2023", address = "Ireland", month = "23 " # mar, keywords = "genetic algorithms, genetic programming, grammatical evolution, AutoGE", URL = "https://doi.org/10.34961/researchrepository-ul.24083970.v1", size = "152 pages", abstract = "By the very nature of its representation, symbolic regression through Grammatical Evolution (GE) stands a chance of being interpretable. However, while GE builds solutions using available building blocks (grammar productions), striving to achieve better approximation can sometimes compromise program size and hence interpretability. On the other hand, increased data dimensionality in regression problems poses a significant challenge when attempting to achieve better performance. This research addresses both issues and demonstrates that choosing the right set of grammar productions through production selection for a given problem not only lets GE perform dimensionality reduction but also reduces program sizes while maintaining the most important performance criterion, generalisation. Grammar design, especially the choice of productions, has largely been a subject of expert judgement or trial and error. We hypothesise that evolution convergence carries information which can be exploited to distinguish between worthy and less useful productions. To test this hypothesis, we devise a production ranking scheme to rank grammar productions used in solution derivations based on structural analysis. The ranking profile of productions provides rich information for production selection, and further development affirmed the effectiveness of the ranking approach. Grammar is not a static artifact in this research but rather adapts to a given problem. At different stages during evolution, productions which appear not to improve evolvability are pruned from the grammar. We develop two grammar pruning approaches: static pruning and dynamic pruning. While static pruning removes productions across subexperiments, dynamic pruning prunes the grammar across generations. The developed approaches of production ranking and grammar pruning are shown to achieve significantly smaller solutions while maintaining accuracy on a variety of synthetic as well as real-world regression problems. Algorithms developed in this research, with an extensive set of experimentation, analysis, and comparison, are integrated into an automated tool, AutoGE, which not only aids in primitive set selection but also in feature selection. Feature selection has been a challenging task, especially in high-dimensional symbolic regression. Using linear scaling to build the ranking profile of features, it is demonstrated that feature selection with AutoGE helps improve generalisation performance in high-dimensional problems compared to state-of-the-art machine learning approaches.", notes = "ir@ul.ie Supervisor: Conor Ryan and Meghana Kshirsagar", } @InProceedings{Ali:2022:ICAIoT, author = "Mohammed Sadeq Ali Ali and Mesut Cevik", booktitle = "2022 International Conference on Artificial Intelligence of Things (ICAIoT)", title = "Optimization of the Number and Placement of Routers in Wireless Mesh Networks", year = "2022", month = "29-30 " # dec, address = "Istanbul, Turkey", keywords = "genetic algorithms, genetic programming, Wireless networks, Wireless mesh networks, Evolutionary computation, Software, Reliability, Problem-solving, Internet of Things, IOT, routers, wireless network, WMNs", isbn13 = "979-8-3503-9677-5", DOI = "doi:10.1109/ICAIoT57170.2022.10121861", abstract = "Wireless Mesh Networks (WMNs) are a new type of wireless network that has been growing in popularity. These networks consist of routers and clients. The routers are called mesh routers (MRs) and the clients are called mesh clients. WMNs have several advantages over traditional wireless networks, such as more reliable coverage and faster speeds. Many different types of algorithms can be used to determine the best placement for these routers, with some algorithms being better than others depending on the environment or situation. One algorithm is called a Genetic Algorithm (GA), which uses genetic programming to find an optimal solution for router placement. GA is used to find the best placement of the router so that it can provide the most coverage possible for a specific area GA or evolutionary algorithms are based on a biological theory known as Darwin's theory. In evolutionary algorithms, it is since the information of the problem becomes chromosomes, and then the problem is solved by special problem-solving techniques in the evolutionary algorithm. The suggested method was implemented using the C++ programming environment and the NS2 software suite. Using a benchmark of produced instances, the experimental outcomes have been analysed. Variable sets of produced instances ranging in size from small to big have been explored. Consequently, several properties of WMNs, including the topological placement of mesh clients, have been recorded.", notes = "Also known as \cite{10121861}", } @Article{ALI:2023:istruc, author = "Mujahid Ali and Sai {Hin Lai}", title = "Artificial intelligent techniques for prediction of rock strength and deformation properties - A review", journal = "Structures", volume = "55", pages = "1542--1555", year = "2023", ISSN = "2352-0124", DOI = "doi:10.1016/j.istruc.2023.06.131", URL = "https://www.sciencedirect.com/science/article/pii/S2352012423008901", keywords = "genetic algorithms, genetic programming, Deformation, Unconfined Compressive Strength (UCS), Intelligent techniques, ANN, Statistical analysis", abstract = "In rock design projects, a number of mechanical properties are frequently employed, particularly unconfined compressive strength (UCS) and deformation (E). The researchers attempt to conduct an indirect investigation since direct measurement of UCS and E is time-consuming, expensive, and requires more expertise and methodologies. Recent and past studies investigate the UCS and E from rock index tests mainly P-wave velocity (Vp), slake durability index, Density, Shore hardness, Schmidt hammer Rebound number (Rn), unit weight, porosity (e) point load strength (Is(50)), and block punch strength index test as its economical and easy to use. The evaluation of these properties is the essential input into modern design methods that routinely adopt some form of numerical modeling, such as machine learning (ML), Artificial Neural Networking (ANN), finite element modeling (FEM), and finite difference methods. Besides, several researchers evaluate the correlation between the input parameters using statistical analysis tools before using them for intelligent techniques. The current study compared the results of laboratory tests, statistical analysis, and intelligent techniques for UCS and E estimation including ANN and adaptive neuro-fuzzy inference system (ANFIS), Genetic Programming (GP), Genetic Expression Programming (GEP), and hybrid models. Following the execution of the relevant models, numerous performance indicators, such as root mean squared error, coefficient of determination (R2), variance account for, and overall ranking, are reviewed to choose the best model and compare the acquired results. Based on the current review, it is concluded that the same rock types from different countries show different mechanical properties due to weathering, size, texture, mineral composition, and temperature. For instance, in the UCS of strong rock (granite) in Spain, ranges from 24 MPa to 278 MPa, whereas in Malaysian rocks, it shows 39 MPa to 212 MPa. On the other side, the coefficient of determination (R2) correlation for the UCS also varies from country to country; while using different modern techniques, the R2 values improved. Finally, recommendations on material properties and modern techniques have been suggested", } @InProceedings{Alibekov:2016:CDC, author = "Eduard Alibekov and Jiri Kubalik and Robert Babuska", booktitle = "2016 IEEE 55th Conference on Decision and Control (CDC)", title = "Symbolic method for deriving policy in reinforcement learning", year = "2016", pages = "2789--2795", abstract = "This paper addresses the problem of deriving a policy from the value function in the context of reinforcement learning in continuous state and input spaces. We propose a novel method based on genetic programming to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived. The symbolic proxy function is constructed such that it maximizes the number of correct choices of the control input for a set of selected states. Maximization methods can then be used to derive a control policy that performs better than the policy derived from the original approximate value function. The method was experimentally evaluated on two control problems with continuous spaces, pendulum swing-up and magnetic manipulation, and compared to a standard policy derivation method using the value function approximation. The results show that the proposed method and its variants outperform the standard method.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CDC.2016.7798684", month = dec, notes = "Also known as \cite{7798684}", } @PhdThesis{Alibekov:thesis, author = "Eduard Alibekov", title = "Symbolic Regression for Reinforcement Learning in Continuous Spaces", school = "F3 Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague", year = "2021", address = "Czech Republic", month = aug, keywords = "genetic algorithms, genetic programming, Single Node Genetic Programming, reinforcement learning, optimal control, function approximation,evolutionary optimization, symbolic regression, robotics, autonomous systems", URL = "https://cyber.felk.cvut.cz/news/eduard-alibekov-defended-his-ph-d-thesis/", URL = "http://hdl.handle.net/10467/98283", URL = "https://dspace.cvut.cz/handle/10467/98283", URL = "https://dspace.cvut.cz/bitstream/handle/10467/98283/F3-D-2021-Alibekov-Eduard-phd_ready.pdf", size = "134 pages", abstract = "Reinforcement Learning (RL) algorithms can optimally solve dynamic decision and control problems in engineering, economics, medicine, artificial intelligence, and other disciplines.However, state-of-the-art RL methods still have not solved the transition from a small set of discrete states to fully continuous spaces. They have to rely on numerical function approximators, such as radial basis functions or neural networks, to represent the value function or policy mappings. While these numerical approximators are well-developed, the choice of a suitable architecture is a difficult step that requires significant trial-and-error tuning. Moreover, numerical approximators frequently exhibit uncontrollable surface artifacts that damage the overall performance of the controlled system. Symbolic Regression (SR) is an evolutionary optimization technique that automatically, without human intervention, generates analytical expressions to fit numerical data. The method has gained attention in the scientific community not only for its ability to recover known physical laws, but also for suggesting yet unknown but physically plausible and interpretable relationships. Additionally, the analytical nature of the result approximators allows to unleash the full power of mathematical apparatus. This thesis aims to develop methods to integrate SR into RL in a fully continuous case. To accomplish this goal, the following original contributions to the field have been developed. (i) Introduction of policy derivation methods. Their main goal is to exploit the full potential of using continuous action spaces, contrary to the state-of-the-art discretised set of actions. (ii) Quasi-symbolic policy derivation (QSPD) algorithm, specifically designed to be used with a symbolic approximation of the value function. The goal of the proposed algorithm is to efficiently derive continuous policy out of symbolic approximator. The experimental evaluation indicated the superiority of QSPD over state-of-the-art methods. (iii) Design of a symbolic proxy-function concept. Such a function is successfully used to alleviate the negative impacts of approximation artifacts on policy derivation. (iv) Study on fitness criterion in the context of SR for RL. The analysis indicated a fundamental flaw with any other symmetric error functions, including commonly used mean squared error. Instead, a new error function procedure has been proposed alongside with a novel fitting procedure. The experimental evaluation indicated dramatic improvement of the approximation quality for both numerical and symbolic approximators. (v) Robust symbolic policy derivation (RSPD) algorithm, which adds an extra level of robustness against imperfections in symbolic approximators. The experimental evaluation demonstrated significant improvements in the reachability of the goal state. All these contributions are then combined into a single,efficient SR for RL (ESRL) framework. Such a framework is able to tackle high-dimensional, fully-continuous RL problems out-of-the-box. The proposed framework has been tested on three bench-marks: pendulum swing-up, magnetic manipulation, and high-dimensional drone strike benchmark.", notes = "https://starfos.tacr.cz/en/project/GA15-22731S Supervisor: Olga Stepankova Supervisor-specialist: Robert Babuska", } @Article{ALIDOUST:2021:JCP, author = "Pourya Alidoust and Mohsen Keramati and Pouria Hamidian and Amir Tavana Amlashi and Mahsa Modiri Gharehveran and Ali Behnood", title = "Prediction of the shear modulus of municipal solid waste ({MSW):} An application of machine learning techniques", journal = "Journal of Cleaner Production", volume = "303", pages = "127053", year = "2021", ISSN = "0959-6526", DOI = "doi:10.1016/j.jclepro.2021.127053", URL = "https://www.sciencedirect.com/science/article/pii/S0959652621012725", keywords = "genetic algorithms, genetic programming, Municipal solid waste, Cyclic triaxial test, Shear modulus, Artificial neural network (ANN), Multivariate adaptive regression splines (MARS), Multi-gene genetic programming (MGGP), M5 model tree (M5Tree)", abstract = "The dynamic properties of Municipal Solid Waste (MSW) are site-specific and need to be evaluated separately in different regions. The laboratory-based evaluation of MSW has difficulties such as an unpleasant aroma or degradability of MSW, making the testing procedure unfavorable. Moreover, these evaluations are time- and cost-intensive, which may also require trained personnel to conduct the tests. To address this concern, alternatively, the shear modulus of MSW can be estimated through some predictive models. In this study, the shear modulus was evaluated using 153 cyclic triaxial tests. For this purpose, the effects of various factors, including the shear strain (ShS), age of the MSW (Age), percentage of plastic (POP), confining pressure (CP), unit weight (UW), and loading frequency (F) on the shear modulus of MSW were evaluated. The data obtained through laboratory experiments was then employed to model the dynamic response of MSW using four different machine learning techniques including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Multi-Gene Genetic Programming (MGGP), and M5 model Tree (M5Tree). A comparison of the performance of developed models indicated that the ANN model outperformed the other models. More specifically, for ANN, MARS, MGGP, and M5Tree models, the corresponding values of R-squared equal to 0.9897, 0.9640, 0.9617, and 0.8482 for the training dataset, while the values for the testing dataset for ANN, MARS, MGGP, and M5Tree are 0.9812, 0.9551, 0.9574, and 0.8745. Furthermore, although the developed models using MARS and MGGP techniques resulted in more errors compared to the ANN technique, they were found to produce reliable predictions. To further compare the performance and efficiency of the developed models and study the effects of each input variable on the output variable (i.e., shear modulus), model validity, parametric study, and sensitivity analysis were performed", } @InProceedings{Aliehyaei:2014:SKIMA, author = "R. Aliehyaei and S. Khan", booktitle = "8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)", title = "Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study", year = "2014", abstract = "Credit scoring is a commonly used method for evaluating the risk involved in granting credits. Both Genetic Programming (GP) and Ant Colony Optimisation (ACO) have been investigated in the past as possible tools for credit scoring. This paper reports an investigation into the relative performances of GP, ACO and a new hybrid GP-ACO approach, which relies on the ACO technique to produce the initial populations for the GP technique. Performance of the hybrid approach has been compared with both the GP and ACO approaches using two well-known benchmark data sets. Experimental results demonstrate the dependence of GP and ACO classification accuracies on the input data set. For any given data set, the hybrid approach performs better than the worse of the other two methods. Results also show that use of ACO in the hybrid approach has only a limited impact in improving GP performance.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SKIMA.2014.7083391", month = dec, notes = "Also known as \cite{7083391}", } @Article{AliGhorbani2010620, author = "Mohammad Ali Ghorbani and Rahman Khatibi and Ali Aytek and Oleg Makarynskyy and Jalal Shiri", title = "Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks", journal = "Computer \& Geosciences", volume = "36", number = "5", pages = "620--627", year = "2010", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2009.09.014", URL = "http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9", keywords = "genetic algorithms, genetic programming, Sea-level variations, Forecasting, Artificial Neural Networks, Comparative studies", abstract = "Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years, emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12 h, 24 h, 5 day and 10 day time intervals using observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, were used to train and validate the employed GP for the period from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis.", } @InCollection{AliGhorbani:2012:GPnew, author = "M. A. Ghorbani and R. Khatibi and H. Asadi and P. Yousefi", title = "Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "12", pages = "255--284", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GEP, ANN, MLR, Chaos", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/47801", size = "30 pages", notes = "Modelling Mississippi mud transport. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{ALISHAH:2024:fuel, author = "Zubair {Ali Shah} and G. Marseglia and M. G. {De Giorgi}", title = "Predictive models of laminar flame speed in {NH3/H2/O3/air} mixtures using multi-gene genetic programming under varied fuelling conditions", journal = "Fuel", volume = "368", pages = "131652", year = "2024", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2024.131652", URL = "https://www.sciencedirect.com/science/article/pii/S0016236124008007", keywords = "genetic algorithms, genetic programming, NH, H, Laminar Flame Speed (LFS), Ignition Delay Time (IDT), Ozone (O), Multi-gene genetic programming", abstract = "The primary aim of this study is to develop and validate a novel multi-gene genetic programming approach for accurately predicting Laminar Flame Speed (LFS) in ammonia (NH3)/hydrogen (H2)/air mixtures, a key aspect in the advancement of carbon-free fuel technologies. Ammonia, particularly when blended with hydrogen, presents significant potential as a carbon-free fuel due to its enhanced reactivity. This research not only investigates the effects of hydrogen concentration, initial temperature, and pressure on LFS and Ignition Delay Time (IDT) but also explores the impact of oxidizing agents like ozone (O3) in augmenting NH3 combustion. A modified reaction mechanism was implemented and validated through parametric analysis. Main findings demonstrate that IDT decreases with higher hydrogen concentrations, increased initial temperature, and initial pressure, although the influence of pressure decreases above 10 atm. Conversely, at lower temperatures (below 1200 K) and higher hydrogen concentrations (30 percent and 50 percent), the dominance of H2 chemistry can negatively impact initial pressure. LFS increases with higher temperature and hydrogen concentration, but decreases under elevated pressure, with its effect becoming negligible above 5 atm. An optimized equivalence ratio (?) range of 1.10 - 1.15 is identified for efficient combustion. Introducing ozone into the oxidizer notably improves LFS in NH3/H2/air mixtures, with the addition of 0.01 ozone mirroring the effect of a 10 percent hydrogen addition under normal conditions. The study's fundamental contribution is the development of a multi-gene genetic algorithm, showcasing the correlation between predicted LFS values and actual values derived from chemkin simulations. The successful validation of this methodology across various case studies underscores its potential as a robust tool in zero-carbon combustion applications, marking a significant stride in the field", } @InProceedings{Alissa:2020:GECCO, author = "Mohamad Alissa and Kevin Sim and Emma Hart", title = "A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390224", DOI = "doi:10.1145/3377930.3390224", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "157--165", size = "9 pages", keywords = "genetic algorithms, deep learning, algorithm selection problem, bin-packing", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict the best algorithm based on features extracted from the data, which is well known to be a difficult task and even more challenging with streaming data. We propose a radical approach that bypasses algorithm-selection altogether by training a Deep-Learning model using solutions obtained from a set of heuristic algorithms to directly predict a solution from the instance-data. To validate the concept, we conduct experiments using a packing problem in which items arrive in batches. Experiments conducted on six large datasets using batches of varying size show the model is able to accurately predict solutions, particularly with small batch sizes, and surprisingly in a small number of cases produces better solutions than any of the algorithms used to train the model.", notes = "Also known as \cite{10.1145/3377930.3390224} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Alissa:2021:CEC, author = "Mohamad Alissa and Kevin Sim and Emma Hart", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "A Neural Approach to Generation of Constructive Heuristics", year = "2021", editor = "Yew-Soon Ong", pages = "1147--1154", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, keywords = "genetic algorithms, genetic programming, Measurement, Navigation, Heuristic algorithms, Neural networks, ANN, Evolutionary computation, Dynamic scheduling, Automatic Heuristics Generation, Hyper-Heuristics, Encoder-Decoder LSTM, Streaming Bin-packing", isbn13 = "978-1-7281-8393-0", DOI = "doi:10.1109/CEC45853.2021.9504989", abstract = "Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic programming have proved successful in automating this process in the past. Typically, these make use of problem state-information and existing heuristics as components. Here we propose a novel neural approach for generating constructive heuristics, in which a neural network acts as a heuristic by generating decisions. We evaluate two architectures, an Encoder-Decoder LSTM and a Feed-Forward Neural Network. Both are trained using the decisions output from existing heuristics on a large set of instances. We consider streaming instances of bin-packing problems in a continual stream that must be packed immediately in strict order and using a limited number of resources. We show that the new heuristics generated are capable of solving a subset of instances better than the well-known heuristics forming the original pool, and hence the overall value of the pool is improved w.r.t. both Falkenauers performance metric and the number of bins used.", notes = "Also known as \cite{9504989}", } @InProceedings{Aliwi:2020:SIU, author = "Mohamed Aliwi and Selcuk Aslan and Sercan Demirci", booktitle = "2020 28th Signal Processing and Communications Applications Conference (SIU)", title = "Firefly Programming For Symbolic Regression Problems", year = "2020", abstract = "Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly Programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods.", keywords = "genetic algorithms, genetic programming, Optimization, Statistics, Sociology, Linear programming, Brightness, firefly algorithm, symbolic regression, automatic programming", DOI = "doi:10.1109/SIU49456.2020.9302201", ISSN = "2165-0608", month = oct, notes = "Also known as \cite{9302201}", } @InProceedings{Alizadeh:2011:EAIS, author = "Mehrdad Alizadeh and Mohammad Mehdi Ebadzadeh", title = "Kernel evolution for support vector classification", booktitle = "IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS 2011)", year = "2011", month = "11-15 " # apr, pages = "93--99", address = "Paris", size = "7 pages", abstract = "Support vector machines (SVMs) have been used in a variety of classification tasks. SVMs undoubtedly are one of the most effective classifiers in several data mining applications. Determination of a kernel function and related parameters has been a bottleneck for this group of classifiers. In this paper a novel approach is proposed to use genetic programming (GP) to design domain-specific and optimal kernel functions for support vector classification (SVC) which automatically adjusts the parameters. Complex low dimensional mapping function is evolved using GP to construct an optimal linear and Gaussian kernel functions in new feature space. By using the principled kernel closure properties, these basic kernels are then used to evolve more optimal kernels. To evaluate the proposed method, benchmark datasets from UCI are applied. The result indicates that for some cases the proposed methods can find a more optimal solution than evolving known kernels.", keywords = "genetic algorithms, genetic programming, Gaussian kernel functions, automatic parameter adjustment, classification task, data mining application, domain-specific kernel functions, feature space, kernel evolution, low dimensional mapping function, optimal kernel functions, optimal linear functions, principled kernel closure properties, support vector classification, support vector machines, Gaussian processes, data mining, pattern classification, support vector machines", DOI = "doi:10.1109/EAIS.2011.5945924", notes = "Also known as \cite{5945924}", } @InProceedings{Aljahdali:2010:AICCSA, author = "Sultan Aljahdali and Alaa F. Sheta", title = "Software effort estimation by tuning COOCMO model parameters using differential evolution", booktitle = "2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)", year = "2010", month = "16-19 " # may, address = "Hammamet, Tunisia", abstract = "Accurate estimation of software projects costs represents a challenge for many government organisations such as the Department of Defense (DOD) and NASA. Statistical models considerably used to assist in such a computation. There is still an urgent need on finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimise such a relationship via tuning mathematical model parameters is urgently needed. In two new model structures to estimate the effort required for software projects using Genetic Algorithms (GAs) were proposed as a modification to the famous Constructive Cost Model (COCOMO). In this paper, we follow up on our previous work and present Differential Evolution (DE) as an alternative technique to estimate the COCOMO model parameters. The performance of the developed models were tested on NASA software project dataset provided in. The developed COCOMO-DE model was able to provide good estimation capabilities.", keywords = "genetic algorithms, genetic programming, sbse, COOCMO model parameter tuning, NASA software project dataset, constructive cost model, differential evolution, mathematical model, optimisation algorithm, software effort estimation, software projects cost estimation, statistical model, optimisation, software cost estimation", DOI = "doi:10.1109/AICCSA.2010.5586985", notes = "'We suggest the use of Genetic Programming (GP) technique to build suitable model structure for the software effort estimation.' Also known as \cite{5586985}", } @Article{Aljahdali:2011:Jcomputerscience, author = "Sultan Aljahdali", title = "Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic", journal = "Journal of Computer Science", year = "2011", volume = "7", number = "10", pages = "1574--1580", publisher = "Science Publications", keywords = "software reliability growth models (SRGM), takagi-sugeno technique, fuzzy logic (FL), artificial neural net-works (ANN), model structure, linear regression model, NASA space", ISSN = "15493636", URL = "http://www.thescipub.com/pdf/10.3844/jcssp.2011.1574.1580", DOI = "doi:10.3844/jcssp.2011.1574.1580", size = "7 pages", abstract = "Problem statement: The use of Software Reliability Growth Models (SRGM) plays a major role in monitoring progress, accurately predicting the number of faults in the software during both development and testing processes; define the release date of a software product, helps in allocating resources and estimating the cost for software maintenance. This leads to achieving the required reliability level of a software product. Approach: We investigated the use of fuzzy logic on building SRGM to estimate the expected software faults during testing process. Results: The proposed fuzzy model consists of a collection of linear sub-models, based on the Takagi-Sugeno technique and attached efficiently using fuzzy membership functions to represent the expected software faults as a function of historical measured faults. A data set provided by John Musa of bell telephone laboratories (i.e., real time control, military and operating system applications) was used to show the potential of using fuzzy logic in solving the software reliability modelling problem. Conclusion: The developed models provided high performance modelling capabilities.", notes = "mentions GP papers but not on GP?", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:84b6d807e851efb50b17f965f70c97d8", } @Article{Aljahdali:2013:IJARAI, author = "Sultan Aljahdali and Alaa Sheta", title = "Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming", journal = "International Journal of Advanced Research in Artificial Intelligence", year = "2013", number = "12", volume = "2", pages = "52--57", keywords = "genetic algorithms, genetic programming, SBSE", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJARAI.2013.021207", URL = "http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf", URL = "http://dx.doi.org/10.14569/IJARAI.2013.021207", size = "6 pages", abstract = "Software has played an essential role in engineering, economic development, stock market growth and military applications. Mature software industry count on highly predictive software effort estimation models. Correct estimation of software effort lead to correct estimation of budget and development time. It also allows companies to develop appropriate time plan for marketing campaign. Now a day it became a great challenge to get these estimates due to the increasing number of attributes which affect the software development life cycle. Software cost estimation models should be able to provide sufficient confidence on its prediction capabilities. Recently, Computational Intelligence (CI) paradigms were explored to handle the software effort estimation problem with promising results. In this paper we evolve two new models for software effort estimation using Multigene Symbolic Regression Genetic Programming (GP). One model uses the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model uses the Inputs, Outputs, Files, and User Enquiries to estimate the Function Point (FP). The proposed GP models show better estimation capabilities compared to other reported models in the literature. The validation results are accepted based Albrecht data set.", } @InProceedings{Aljero:2020:ICOASE, author = "Mona Khalifa A. Aljero and Nazife Dimililer", title = "Hate Speech Detection Using Genetic Programming", booktitle = "2020 International Conference on Advanced Science and Engineering (ICOASE)", year = "2020", abstract = "There has been a steep increase in the use of social media in our everyday lives in recent years. Along with this, there has been an increase in hate speech disseminated on these platforms, due to the anonymity of the users as well as the ease of use. Social media platforms need to filter and prevent the spread of hate speech to protect their users and society. Due to the high traffic, automatic detection of hate speech is necessary. Hate speech detection is one of the most difficult classification challenges in text mining. Research in this domain focuses on the use of supervised machine learning approaches, such as support vector machine, logistic regression, convolutional neural network, and random forest. Ensemble techniques have also been employed. However, the performance of these approaches has not yet reached an acceptable level. In this paper, we propose the use of the Genetic Programming (GP) approach for binary classification of hate speech on social media platforms. Each individual in the GP framework represents a classifier that is evolved to optimize Fl-score. Experimental results show the effectiveness of our GP approach; the proposed approach outperforms the state-of-the-art using the same dataset HatEval.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICOASE51841.2020.9436621", month = dec, notes = "Also known as \cite{9436621}", } @Article{Aljero:2021:A, author = "Mona Khalifa A. Aljero and Nazife Dimililer", title = "Genetic Programming Approach to Detect Hate Speech in Social Media", journal = "IEEE Access", year = "2021", volume = "9", pages = "115115--115125", abstract = "Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide. If there is no control, these platforms may be used to propagate hate speech. In fact, in recent years, hate speech has increased on social media. Therefore, there is a need to monitor and prevent hate speech on these platforms. However, manual control is not feasible due to the high traffic of content production on social media sites. Moreover, the language used and the length of the messages provide a challenge when using classical machine learning approaches as prediction methods. This paper presents a genetic programming (GP) model for detecting hate speech where each chromosome represents a classifier employing a universal sentence encoder as a feature. A novel mutation technique that affects only the feature values in combination with the standard one-point mutation technique improved the performance of the GP model by enriching the offspring pool with alternative solutions. The proposed GP model outperformed all state-of-the-art systems for the four publicly available hate speech datasets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ACCESS.2021.3104535", ISSN = "2169-3536", notes = "Also known as \cite{9513275}", } @Article{DBLP:journals/elektrik/AljeroD23, author = "Mona Khalifa A. Aljero and Nazife Dimililer", title = "Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets", journal = "Turkish J. Electr. Eng. Comput. Sci.", volume = "31", number = "1", pages = "180--192", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.55730/1300-0632.3978", DOI = "doi:10.55730/1300-0632.3978", timestamp = "Thu, 23 Feb 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/elektrik/AljeroD23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Alkhaldi:2022:IEEEAccess, author = "Eid Alkhaldi and Ezzatollah Salari", journal = "IEEE Access", title = "Ensemble Optimization for Invasive Ductal Carcinoma ({IDC)} Classification Using Differential Cartesian Genetic Programming", year = "2022", volume = "10", pages = "128790--128799", abstract = "The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers' diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we use the differential Cartesian Genetic Programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using Cartesian Genetic Programming transcended formerly published algorithms on slim datasets.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/ACCESS.2022.3228176", ISSN = "2169-3536", notes = "Also known as \cite{9978635}", } @PhdThesis{Alkroosh:thesis, title = "Modelling pile capacity and load-settlement behaviour of piles embedded in sand \& mixed soils using artificial intelligence", author = "Iyad Salim Jabor Alkroosh", year = "2011", school = "Curtin University, Faculty of Engineering and Computing, Department of Civil Engineering", address = "Australia", month = may, keywords = "genetic algorithms, genetic programming, gene expression programming, modelling pile capacity, load-settlement behaviour of piles, artificial intelligence, (GEP) and the artificial neural networks (ANNs), numerical modelling techniques", URL = "http://espace.library.curtin.edu.au/Modelling.pdf", URL = "http://hdl.handle.net/20.500.11937/351", URL = "https://espace.curtin.edu.au/handle/20.500.11937/351", size = "338 pages", abstract = "This thesis presents the development of numerical models which are intended to be used to predict the bearing capacity and the load-settlement behaviour of pile foundations embedded in sand and mixed soils. Two artificial intelligence techniques, the gene expression programming (GEP) and the artificial neural networks (ANNs), are used to develop the models. The GEP is a developed version of genetic programming (GP). Initially, the GEP is used to model the bearing capacity of the bored piles, concrete driven piles and steel driven piles. The use of the GEP is extended to model the load-settlement behaviour of the piles but achieved limited success. Alternatively, the ANNs have been employed to model the load-settlement behaviour of the piles. The GEP and the ANNs are numerical modelling techniques that depend on input data to determine the structure of the model and its unknown parameters. The GEP tries to mimic the natural evolution of organisms and the ANNs tries to imitate the functions of human brain and nerve system. The two techniques have been applied in the field of geotechnical engineering and found successful in solving many problems. The data used for developing the GEP and ANN models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. The bored piles have different sizes and round shapes, with diameters ranging from 320 to 1800 mm and lengths from 6 to 27 m. The driven piles also have different sizes and shapes (i.e. circular, square and hexagonal), with diameters ranging from 250 to 660 mm and lengths from 8 to 36 m. All the information of case records in the data source is reviewed to ensure the reliability of used data. The variables that are believed to have significant effect on the bearing capacity of pile foundations are considered. They include pile diameter, embedded length, weighted average cone point resistance within tip influence zone and weighted average cone point resistance and weighted average sleeve friction along shaft. The sleeve friction values are not available in the bored piles data, so the weighted average sleeve friction along shaft is excluded from bored piles models. The models output is the pile capacity (interpreted failure load). Additional input variables are included for modelling the load-settlement behaviour of piles. They include settlement, settlement increment and current state of load settlement. The output is the next state of load-settlement. The data are randomly divided into two statistically consistent sets, training set for model calibration and an independent validation set for model performance verification. The predictive ability of the developed GEP model is examined via comparing the performance of the model in training and validation sets. Two performance measures are used: the mean and the coefficient of correlation. The performance of the model was also verified through conducting sensitivity analysis which aimed to determine the response of the model to the variations in the values of each input variables providing the other input variables are constant. The accuracy of the GEP model was evaluated further by comparing its performance with number of currently adopted traditional CPT-based methods. For this purpose, several ranking criteria are used and whichever method scores best is given rank 1. The GEP models, for bored and driven piles, have shown good performance in training and validation sets with high coefficient of correlation between measured and predicted values and low mean values. The results of sensitivity analysis have revealed an incremental relationship between each of the input variables and the output, pile capacity. This agrees with what is available in the geotechnical knowledge and experimental data. The results of comparison with CPT-based methods have shown that the GEP models perform well.", abstract = "The GEP technique is also used to simulate the load-settlement behaviour of the piles. Several attempts have been carried out using different input settings. The results of the favoured attempt have shown that the GEP have achieved limited success in predicting the load-settlement behaviour of the piles. Alternatively, the ANN is considered and the sequential neural network is used for modelling the load-settlement behaviour of the piles. This type of network can account for the load-settlement interdependency and has the option to feedback internally the predicted output of the current state of load settlement to be used as input for the next state of load-settlement. Three ANN models are developed: a model for bored piles and two models for driven piles (a model for steel and a model for concrete piles). The predictive ability of the models is verified by comparing their predictions in training and validation sets with experimental data. Statistical measures including the coefficient of correlation and the mean are used to assess the performance of the ANN models in training and validation sets. The results have revealed that the predicted load-settlement curves by ANN models are in agreement with experimental data for both of training and validation sets. The results also indicate that the ANN models have achieved high coefficient of correlation and low mean values. This indicates that the ANN models can predict the load-settlement of the piles accurately. To examine the performance of the developed ANN models further, the prediction of the models in the validation set are compared with number of load-transfer methods. The comparison is carried out first visually by comparing the load-settlement curve obtained by the ANN models and the load transfer methods with experimental curves. Secondly, is numerically by calculating the coefficient of correlation and the mean absolute percentage error between the experimental data and the compared methods for each case record. The visual comparison has shown that the ANN models are in better agreement with the experimental data than the load transfer methods. The numerical comparison also has shown that the ANN models scored the highest coefficient of correlation and lowest mean absolute percentage error for all compared case records. The developed ANN models are coded into a simple and easily executable computer program. The output of this study is very useful for designers and also for researchers who wish to apply this methodology on other problems in Geotechnical Engineering. Moreover, the result of this study can be considered applicable worldwide because its input data is collected from different regions.", notes = "See also \cite{Alkroosh:book} Supervisors: Hamid Nikraze and Ian Misich", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", description = "The sleeve friction values are not available in the bored piles data, so the weighted average sleeve friction along shaft is excluded from bored piles models. The models output is the pile capacity (interpreted failure load). Additional input variables are included for modelling the load-settlement behaviour of piles. They include settlement, settlement increment and current state of load settlement. The output is the next state of load-settlement. The data are randomly divided into two statistically consistent sets, training set for model calibration and an independent validation set for model performance verification. This type of network can account for the load-settlement interdependency and has the option to feedback internally the predicted output of the current state of loadsettlement to be used as input for the next state of load-settlement. The developed ANN models are coded into a simple and easily executable computer program.", language = "en", oai = "oai:espace.library.curtin.edu.au:166155", rights = "unrestricted", } @Book{Alkroosh:book, author = "Iyad Alkroosh", title = "Modelling pile capacity \& load-settlement behaviour from {CPT} data: For piles in sand and mixed soils using artificial intelligence", publisher = "Lambert Academic Publishing", year = "2012", month = "23 " # may, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, ANN", isbn13 = "3848436906", URL = "https://www.amazon.co.uk/Modelling-pile-capacity-load-settlement-behaviour/dp/3848436906", size = "340 pages", abstract = "This work involves the presentation of new approach attempted to predict the axial capacity and load-settlement behaviour of piles embedded in sand and mixed soils. Two artificial intelligence techniques including Gene Expression Programming (GEP) and Artificial Neural Networks (ANNs) have been used in the approach. The work begins with the definitions of the two techniques and explanation of their terminology and the theories which each of them is based on. The work also comprises extensive literature review of the proposed procedures for evaluating pile capacity and load settlement behaviour. The application of the artificial intelligence in the work begins with the use of the GEP for modelling the pile capacity. The modelling involves data collection, selection of input variables, data division, determination of setting parameters and GEP model selection and model formulation and validation. Two models are developed, a model for bored piles and two others for driven piles. In the second phase of this work, the artificial neural network used for modelling the load-settlement behaviour of the piles.", notes = "See also \cite{Alkroosh:thesis}", } @Article{Alkroosh:2014:SF, author = "I. Alkroosh and H. Nikraz", title = "Predicting pile dynamic capacity via application of an evolutionary algorithm", journal = "Soils and Foundations", volume = "54", number = "2", pages = "233--242", year = "2014", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0038-0806", DOI = "doi:10.1016/j.sandf.2014.02.013", URL = "http://www.sciencedirect.com/science/article/pii/S0038080614000213", size = "10 pages", abstract = "This study presents the development of a new model obtained from the correlation of dynamic input and SPT data with pile capacity. An evolutionary algorithm, gene expression programming (GEP), was used for modelling the correlation. The data used for model development comprised 24 cases obtained from existing literature. The modelling was carried out by dividing the data into two sets: a training set for model calibration and a validation set for verifying the generalisation capability of the model. The performance of the model was evaluated by comparing its predictions of pile capacity with experimental data and with predictions of pile capacity by two commonly used traditional methods and the artificial neural networks (ANNs) model. It was found that the model performs well with a coefficient of determination, mean, standard deviation and probability density at 50percent equivalent to 0.94, 1.08, 0.14, and 1.05, respectively, for the training set, and 0.96, 0.95, 0.13, and 0.93, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the model is accurate in predicting pile capacity. The results of comparison also showed that the model predicted pile capacity more accurately than traditional methods including the ANNs model.", notes = "The Japanese Geotechnical Society also known as \cite{Alkroosh2014233} Department of Civil Engineering", bibsource = "OAI-PMH server at espace.library.curtin.edu.au", oai = "oai:espace.library.curtin.edu.au:237657", } @Article{Allen:2003:NB, author = "Jess Allen and Hazel M. Davey and David Broadhurst and Jim K. Heald and Jem J. Rowland and Stephen G. Oliver and Douglas B. Kell", title = "High-throughput classification of yeast mutants for functional genomics using metabolic footprinting", journal = "Nature Biotechnology", year = "2003", volume = "21", number = "6", pages = "692--696", month = jun, email = "dbk@umist.ac.uk", keywords = "genetic algorithms, genetic programming", URL = "http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf", DOI = "doi:10.1038/nbt823", abstract = "Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes1. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming2-8, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.", } @Article{Allen:2004:AEM, author = "Jess Allen and Hazel M. Davey and David Broadhurst and Jem J. Rowland and Stephen G. Oliver and Douglas B. Kell", title = "Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting", journal = "Applied and Environmental Microbiology", year = "2004", volume = "70", number = "10", pages = "6157--6165", month = oct, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1128/AEM.70.10.6157-6165.2004", abstract = "Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their metabolic footprints by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.", notes = "PMID:", } @InProceedings{DBLP:conf/gecco/AllenBHK09, author = "Sam Allen and Edmund K. Burke and Matthew R. Hyde and Graham Kendall", title = "Evolving reusable {3D} packing heuristics with genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "931--938", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570029", abstract = "This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human analysts to design the very effective heuristic methods that currently exist. Hyper-heuristics search a space of heuristics, rather than directly searching a solution space. GP operates as a hyper-heuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP can design simple, yet effective, stand-alone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed by humans.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @PhdThesis{Allen:thesis, author = "Sam D. Allen", title = "Algorithms and data structures for three-dimensional packing", school = "School of Computer Science, University of Nottingham", year = "2011", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming, packing, shipment, business, operations research", URL = "http://etheses.nottingham.ac.uk/2779/1/thesis_nicer.pdf", size = "123 pages", abstract = "Cutting and packing problems are increasingly prevalent in industry. A well used freight vehicle will save a business money when delivering goods, as well as reducing the environmental impact, when compared to sending out two lesser-used freight vehicles. A cutting machine that generates less wasted material will have a similar effect. Industry reliance on automating these processes and improving productivity is increasing year-on-year. This thesis presents a number of methods for generating high quality solutions for these cutting and packing challenges. It does so in a number of ways. A fast, efficient framework for heuristically generating solutions to large problems is presented, and a method of incrementally improving these solutions over time is implemented and shown to produce even higher packing. The results from these findings provide the best known results for 28 out of 35 problems from the literature. This framework is analysed and its effectiveness shown over a number of datasets, along with a discussion of its theoretical suitability for higher-dimensional packing problems. A way of automatically generating new heuristics for this framework that can be problem specific, and therefore highly tuned to a given dataset, is then demonstrated and shown to perform well when compared to the expert-designed packing heuristics. Finally some mathematical models which can guarantee the optimality of packings for small datasets are given, and the (in)effectiveness of these techniques discussed. The models are then strengthened and a novel model presented which can handle much larger problems under certain conditions. The thesis finishes with a discussion about the applicability of the different approaches taken to the real-world problems that motivate them.", notes = "Supervisors: Edmund K. Burke and Graham Kendall ID Code: 2779", } @InCollection{Almal:2005:GPTP, author = "A. Almal and W. P. Worzel and E. A. Wollesen and C. D. MacLean", title = "Content Diversity in Genetic Programming and its Correlation with Fitness", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "12", pages = "177--190", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, diversity, chaos game, fitness correlation, visualisation", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_12", size = "14 pages", abstract = "A technique used to visualise DNA sequences is adapted to visualize large numbers of individuals in a genetic programming population. This is used to examine how the content diversity of a population changes during evolution and how this correlates with changes in fitness.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{1144040, author = "Arpit A. Almal and Anirban P. Mitra and Ram H. Datar and Peter F. Lenehan and David W. Fry and Richard J. Cote and William P. Worzel", title = "Using genetic programming to classify node positive patients in bladder cancer", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "239--246", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p239.pdf", DOI = "doi:10.1145/1143997.1144040", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Biological Applications, algorithms and similarity measures, bladder cancer, classification rules, classifier design and evaluation, concept learning and induction, feature design and evaluation, feature selection, machine learning, Nodal staging, pattern analysis, program synthesis, synthesis", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InCollection{Almal:2007:GPTP, author = "A. A. Almal and C. D. MacLean and W. P. Worzel", title = "Program Structure-Fitness Disconnect and Its Impact On Evolution In GP", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "9", pages = "143--158", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, phenotype, genotype, evolutionary dynamics, GP structure, GP content, speciation, population, fitness", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_9", size = "15 pages", abstract = "Simple Genetic Programming (GP) is generally considered to lack the strong separation between genotype and phenotype found in natural evolution. In many cases, the genotype and the phenotype are considered identical in GP since the program representation does not undergo any modification prior to its encounter with 'environment' in the form of inputs and a fitness function. However, this view overlooks a key fact: fitness in GP is determined without reference to the makeup of the individual programs but evolutionary changes occur in the structure and content of the individual without reference to its fitness. This creates a disconnect between 'genetic recombination' and fitness similar to that in nature that can create unexpected effects during the evolution of a population and suggests an important dynamic that has not been thoroughly considered by the GP community. This paper describes some of the observed effects of this disconnect and studies some approaches for the estimating diversity of a population which could lead to a new way of modelling the dynamics of GP. We also speculate on the similarity of these effects and some recently studied aspects of natural evolution.", notes = "part of \cite{Riolo:2007:GPTP} Published 2008", } @InCollection{Almal:2008:GPTP, author = "A. A. Almal and C. D. MacLean and W. P. Worzel", title = "A Population Based Study of Evolutionary Dynamics in Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "2", pages = "19--29", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", DOI = "doi:10.1007/978-0-387-87623-8_2", size = "10 pages", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", keywords = "genetic algorithms, genetic programming", } @InProceedings{almarimi2020community, author = "Nuri Almarimi and Ali Ouni and Moataz Chouchen and Islem Saidani and Mohamed Wiem Mkaouer", title = "On the Detection of Community Smells using Genetic Programming-based Ensemble Classifier Chain", booktitle = "15th IEEE/ACM International Conference on Global Software Engineering (ICGSE)", year = "2020", pages = "43--54", address = "internet", month = "26 " # jun, keywords = "genetic algorithms, genetic programming, SBSE, community smells, social debt, socio-technical factors, search-based software engineering, multi-label learning", isbn13 = "9781450370936", URL = "https://conf.researchr.org/details/icgse-2020/icgse-2020-research-papers/6/On-the-Detection-of-Community-Smells-using-Genetic-Programming-based-Ensemble-Classif", DOI = "doi:10.1145/3372787.3390439", dataset_url = "https://github.com/GP-ECC/community-smells", abstract = "Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as suboptimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89percent achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.", notes = "Ecole de technologie superieure (ETS), Montreal, Canada. ICGSE 2020 co-located with ICSE 2020", } @InProceedings{Almarimi:2020:ICGSE, author = "Nuri Almarimi and Ali Ouni and Moataz Chouchen and Islem Saidani and Mohamed Wiem Mkaouer", booktitle = "2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE)", title = "On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain", year = "2020", pages = "43--54", abstract = "Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterise the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89percent achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterise community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.", keywords = "genetic algorithms, genetic programming, Search-based software engineering, SBSE, Costs, Social networking (online), Standards organizations, Collaboration, Transforms, Software quality, Community smells, Social debt, Socio-technical factors, Multi-label learning", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=10148849", month = may, notes = "Also known as \cite{10148849}", } @InProceedings{alMasalma:2022:GECCOcomp, author = "Mihyar {Al Masalma} and Malcolm Heywood", title = "Genetic Programming with External Memory in Sequence Recall Tasks", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "518--521", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, modularity, partially observable, external memory", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528883", video_url = "https://vimeo.com/723511528", abstract = "Partially observable tasks imply that a learning agent has to recall previous state in order to make a decision in the present. Recent research with neural networks have investigated both internal and external memory mechanisms for this purpose, as well as proposing benchmarks to measure their effectiveness. These developments motivate our investigation using genetic programming and an external linked list memory model. A thorough empirical evaluation using a scalable sequence recall benchmark establishes the underlying strength of the approach. In addition, we assess the impact of decisions made regarding the instruction set and characterize the sensitivity to noise / obfuscation in the definition of the benchmarks. Compared to neural solutions to these benchmarks, GP extends the state-of-the-art to greater task depths than previously possible.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{alMasalma:2023:GPEM, author = "Mihyar {Al Masalma} and Malcolm Heywood", title = "Benchmarking ensemble genetic programming with a linked list external memory on scalable partially observable tasks", journal = "Genetic Programming and Evolvable Machines", year = "2022", volume = "23", number = "Suppl 1", pages = "s1--s29", month = "30 " # nov, keywords = "genetic algorithms, genetic programming, External memory, Partial observability, Internal state, Ensembles, noop, pop_head, pop_tail, DIV", ISSN = "1389-2576", URL = "https://rdcu.be/daFLX", DOI = "doi:10.1007/s10710-022-09446-8", size = "29 pages", abstract = "Reactive learning agents cannot solve partially observable sequential decision-making tasks as they are limited to defining outcomes purely in terms of the observable state. However, augmenting reactive agents with external memory might provide a path for addressing this limitation. In this work, external memory takes the form of a linked list data structure that programs have to learn how to use. We identify conditions under which additional recurrent connectivity from program output to input is necessary for state disambiguation. Benchmarking against recent results from the neural network literature on three scalable partially observable sequential decision-making tasks demonstrates that the proposed approach scales much more effectively. Indeed, solutions are shown to generalize to far more difficult sequences than those experienced under training conditions. Moreover, recommendations are made regarding the instruction set and additional benchmarking is performed with input state values designed to explicitly disrupt the identification of useful states for later recall. The protected division operator appears to be particularly useful in developing simple solutions to all three tasks.", notes = "See also MSc https://dalspace.library.dal.ca/handle/10222/81503 http://hdl.handle.net/10222/81503 Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada", } @Article{Almeida:2017:ieeeGRSL, author = "Alexandre E. Almeida and Ricardo {da S. Torres}", journal = "IEEE Geoscience and Remote Sensing Letters", title = "Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions", year = "2017", volume = "14", number = "9", pages = "1499--1503", abstract = "In several applications, the automatic identification of regions of interest in remote sensing images is based on the assessment of the similarity of associated time series, i.e., two regions are considered as belonging to the same class if the patterns found in their spectral information observed over time are somewhat similar. In this letter, we investigate the use of a genetic programming (GP) framework to discover an effective combination of time series similarity functions to be used in remote sensing classification tasks. Performed experiments in a Forest-Savanna classification scenario demonstrated that the GP framework yields effective results when compared with the use of traditional widely used similarity functions in isolation.", keywords = "genetic algorithms, genetic programming, remote sensing, time series similarity", DOI = "doi:10.1109/LGRS.2017.2719033", ISSN = "1545-598X", month = sep, notes = "Also known as \cite{7981314}", } @Article{Almeida:2015:EI, author = "Jurandy Almeida and Jefersson A. {dos Santos} and Waner O. Miranda and Bruna Alberton and Leonor Patricia C. Morellato and Ricardo {da S. Torres}", title = "Deriving vegetation indices for phenology analysis using genetic programming", journal = "Ecological Informatics", volume = "26, Part 3", pages = "61--69", year = "2015", keywords = "genetic algorithms, genetic programming, Remote phenology, Digital cameras, Image analysis, Vegetation indices", ISSN = "1574-9541", DOI = "doi:10.1016/j.ecoinf.2015.01.003", URL = "http://www.sciencedirect.com/science/article/pii/S1574954115000114", size = "9 pages", abstract = "Plant phenology studies recurrent plant life cycle events and is a key component for understanding the impact of climate change. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful strategies relies on the use of digital cameras, which are used as multi-channel imaging sensors to estimate colour changes that are related to phenological events. We monitor leaf-changing patterns of a cerrado-savanna vegetation by taking daily digital images. We extract individual plant color information and correlate with leaf phenological changes. For that, several vegetation indices associated with plant species are exploited for both pattern analysis and knowledge extraction. In this paper, we present a novel approach for deriving appropriate vegetation indices from vegetation digital images. The proposed method is based on learning phenological patterns from plant species through a genetic programming framework. A comparative analysis of different vegetation indices is conducted and discussed. Experimental results show that our approach presents higher accuracy on characterising plant species phenology.", } @InProceedings{Almeida:2016:SIBGRAPI, author = "M. A. Almeida and E. C. Pedrino and M. C. Nicoletti", booktitle = "2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)", title = "A Genetically Programmable Hybrid Virtual Reconfigurable Architecture for Image Filtering Applications", year = "2016", pages = "152--157", abstract = "A new and efficient automatic hybrid method, called Hy-EH, based on Virtual Reconfigurable Architectures (VRAs) and implemented in Field Programmable Gate Arrays (FPGAs) is proposed, for a hardware-embedded construction of image filters. The method also encompass an evolutionary software system, which represents the chromosome as a bi-dimensional grid of function elements (FEs), entirely parametrised using the Verilog-HDL (Verilog Hardware Description Language), which is reconfigured using the MATLAB toolbox GPLAB, before its download into the FPGA. In the so-called intrinsic proposals, evolutionary processes take place internally to the hardware, in a pre-defined fixed way, in extrinsic proposals evolutionary processes happen externally to the hardware. The hybrid Hy-EH method, described in this paper allows for the intrinsic creation of a flexible-sized hardware, in an extrinsic way i.e., by means of an evolutionary process that happens externally to the hardware. Hy-EH is also a convenient choice as far as extrinsic methods are considered, since it does not depend on a proprietary solution for its implementation. A comparative analysis of using the Hy-EH versus an existing intrinsic proposal, in two well-known problems, has been conducted. Results show that by using Hy-EH there was little hardware complexity due to the optimised and more flexible use of shorter chromosomes.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SIBGRAPI.2016.029", month = oct, notes = "Also known as \cite{7813028}", } @Article{Almeida:2018:ICAE, author = "M. A. Almeida and E. C. Pedrino", title = "Hybrid Evolvable Hardware for automatic generation of image filters", journal = "Integrated Computer-Aided Engineering", year = "2018", volume = "25", number = "3", pages = "289--303", keywords = "genetic algorithms, genetic programming, Evolvable Hardware, FPGA, virtual reconfigurable architecture", ISSN = "1069-2509", DOI = "doi:10.3233/ICA-180561", size = "15 pages", abstract = "In this article, a new framework is proposed and implemented for automatic generation of image filters in reconfigurable hardware (FPGA), called H-EHW (Hybrid-Evolvable Hardware). This consists basically of two modules. The first (training module) is responsible for the automatic generation of solutions (filters). The second (fusion module) converts such solutions into hardware, thus creating a virtual and reconfigurable architecture for fast image processing. Monochromatic pairs of images are used for the system training and testing. Extensive tests show that there are several benefits of the proposed system when compared to other similar systems described in the literature, such as: reduced phenotype length (generated circuit), reduced reconfiguration time, greater hardware reconfiguration flexibility and no more need for the manipulation of the bitstream of the FPGA for circuit evolution (a problem often encountered in practice by designers).", } @InCollection{almgren:2000:CADGP, author = "Magnus Almgren", title = "Communicating Agents Developed with Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "25--32", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @PhdThesis{AlMosawe:thesis, author = "Alaa Al-Mosawe", title = "Investigation of the performance of cracked steel members strengthened with carbon fibre reinforced polymers under impact loads", school = "Faculty of Science, Engineering and Technology, Swinburne University of Technology", year = "2016", address = "Melbourne, Australia", keywords = "genetic algorithms, genetic programming, CFRP, Fe", URL = "http://hdl.handle.net/1959.3/414765", URL = "https://researchbank.swinburne.edu.au/file/0cca0b0c-0219-4cb0-b0d3-b33bcdb159a8/1/Alaa%20Al-Mosawe%20Thesis.pdf", size = "224 pages", abstract = "Generally, steel structures are subjected to different types of loadings during their life-time. Over time these structures sustain fewer loads than those for which they were designed. The reduction in structural capacity might occur as a result of various parameters, including aging, changes in use, increases in applied loads, and as a result of environmental effects causing corrosion. These structures need to be strengthened or repaired in order to be able to carry the different applied loads. Carbon fibre reinforced polymers (CFRPs) are a new method of strengthening, the use of which has grown in the last few decades. This method of strengthening has attracted structural engineers due to its ease of application, light weight and very high tensile strength. The bond between CFRP and steel members is the main issue in understanding the bond behaviour. This thesis presents the effect of impact loading on the bond behaviour of CFRP-steel double strap joints. The results of comprehensive experimental tests are presented in this project on the basis of testing large numbers of CFRP-steel double strap joints under both static and dynamic loadings. Another series of tests was conducted to investigate the mechanical properties of the composite material itself. The mechanical properties were investigated under different loading rates, starting from quasi-static loading at 2mm/min, to impact loadings of 201000mm/minute, 258000mm/minute and 300000mm/minute. The experimental results showed that loading rate has a significant effect on the material properties, and a significant increase was shown in tensile strength and modulus of elasticity. The results of another series of tests are presented in this thesis. A number of CFRP-steel double strap joints were prepared and tested under quasi-static loads. Three different types of CFRP modulus (low modulus 165 GPa, normal modulus 205GPa and ultra-high CFRP modulus 460 GPa) were used, to study the effect of CFRP modulus on the bond behaviour between steel and CFRP laminates. In order to investigate the effect of CFRP geometry on the bond properties, two different CFRP sections were used (20 by 1.4mm and 10 by 1.4mm). The results showed a significant influence on the bond strength, strain distribution along the bond, effective bond length and failure mode for specimens with different CFRP modulus. The results also showed that a small CFRP section is sensitive to any little movement. Further tests were also conducted on CFRP-steel double strap specimens with different CFRP moduli under high impact loading rates. The load rates used in this project were 201m per minute, 258m per minute and 300m/min. The aim of this test was to find the degree of joint enhancement under dynamic loadings compared to quasi-static loads. The results showed a significant increase in load-carrying capacity, and strain distribution along the bond. However, a significant decrease in the effective bond length under impact loads was observed compared to quasi-static testing. Different failure modes were shown compared to specimens tested under quasi-static loadings. Finite element analysis was conducted in this research to model the CFRP-steel double strap joint under both quasi-static and dynamic loads. The individual components of the joint (CFRP laminate, Araldite 420 adhesive and steel plates) were first modeled and analysed under the four loading rates. The CFRP-steel double strap joints were modelled using non-linear finite element analysis using the commercial software ABAQUS 6.13. The results showed good prediction of material properties and joint behaviour using non-linear finite element analysis, and the results of tensile joint strength, strain distribution along the bond, effective bond length and failure modes were close to those tested experimentally. This thesis also shows a new formulation of CFRP-steel double strap joints using genetic programming; the data from the experimental and numerical analysis were analysed using genetic programming software. Three different parameters were used: bond length, loading rate and the CFRP modulus. The outcomes of this analysis are showing an expression tree and a new equation to express the bond strength of these types of joints. The results are assumed to be used for the range of parameters used as input data in the programming. Finally, some suggestions on future work to continue the investigation of the bond behaviour between CFRP and steel in the double strap joints are provided.", notes = "supervisor: Riadh Al-Mahaidi", } @Article{AlMosawe:2017:CS, author = "Alaa Al-Mosawe and Robin Kalfat and Riadh Al-Mahaidi", title = "Strength of Cfrp-steel double strap joints under impact loads using genetic programming", journal = "Composite Structures", volume = "160", pages = "1205--1211", year = "2017", keywords = "genetic algorithms, genetic programming, Carbon fibre, Genetic programing, Impact behaviour, Joint strength, CFRP-steel joint", ISSN = "0263-8223", URL = "http://www.sciencedirect.com/science/article/pii/S0263822316317767", DOI = "doi:10.1016/j.compstruct.2016.11.016", abstract = "Carbon fibre reinforced polymers (CFRPs) are widely used by structural engineers to increase the strength of existing structures subjected to different loading actions. Existing steel structures are subjected to impact loadings due to the presence of new types of loads, and these structures need to be strengthened to sustain the new applied loads. Design guidelines for FRP-strengthened steel structures are not yet available, due to the lack of understanding of bond properties and bond strength. This paper presents the application of genetic programming (GP) to predict the bond strength of CFRP-steel double strap joints subjected to direct tension load. Extensive data from experimental tests and finite element modelling were used to develop a new joint strength formulation. The selected parameters which have a direct impact on the joint strength were: bond length, CFRP modulus and the loading rate. A wide range of loading rates and four CFRP moduli with different bond lengths were used. The prediction of the GP model was compared with the experimental values. The model has a high value of R squared, which indicates good accuracy of results.", } @InProceedings{Al-Mulla:2009:EMBC, author = "M. R. Al-Mulla and F. Sepulveda and M. Colley and A. Kattan", title = "Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction", booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009", year = "2009", month = "2-6 " # sep, address = "Minneapolis, Minnesota, USA", pages = "2633--2638", keywords = "genetic algorithms, genetic programming, GP training phase, K-means clustering, fuzzy classifier, isometric contraction, isometric sEMG signal filtering, localized muscle fatigue classification, nonfatigue classifier, rectified surface electromyography, statistical feature extraction, transition-to-fatigue classifier, two-dimensional Euclidean space, biomechanics, electromyography, fatigue, feature extraction, filtering theory, fuzzy logic, medical signal processing, neurophysiology, pattern clustering, signal classification, statistical analysis", DOI = "doi:10.1109/IEMBS.2009.5335368", ISSN = "1557-170X", abstract = "Genetic programming is used to generate a solution that can classify localized muscle fatigue from filtered and rectified surface electromyography (sEMG). The GP has two classification phases, the GP training phase and a GP testing phase. In the training phase, the program evolved with multiple components. One component analyzes statistical features extracted from sEMG to chop the signal into blocks and label them using a fuzzy classifier into three classes: non-fatigue, transition-to-fatigue and fatigue. The blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is then applied to group similar data blocks. Each cluster is then labeled according to its dominant members. The programs that achieve good classification are evolved. In the testing phase, it tests the signal using the evolved components, however without the use of a fuzzy classifier. As the results show the evolved program achieves good classification and it can be used on any unseen isometric sEMG signals to classify fatigue without requiring any further evolution. The GP was able to classify the signal into a meaningful sequence of non-fatigue -> transition-to-fatiguer -> fatigue. By identifying a transition-to fatigue state the GP can give a prediction of an oncoming fatigue. The genetic classifier gave promising results 83.17percent correct classification on average of all signals in the test set, especially considering that the GP is classifying muscle fatigue for ten different individuals.", notes = "Also known as \cite{5335368}", } @Article{Al-Mulla:2011:MEP, author = "Mohamed R. Al-Mulla and Francisco Sepulveda and M. Colley", title = "Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue", journal = "Medical Engineering and Physics", year = "2011", volume = "33", number = "4", pages = "411--417", month = may, keywords = "genetic algorithms, Localized muscle fatigue, sEMG, Wavelet analysis, matlab", DOI = "doi:10.1016/j.medengphy.2010.11.008", abstract = "The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps muscle was recorded from ten subjects performing semi-isometric (i.e., attempted isometric) contraction until fatigue. For training and testing purposes, the signals were labelled in two classes (Non-Fatigue and Fatigue), with the labelling being determined by a fuzzy classifier using elbow angle and its standard deviation as inputs. A genetic algorithm was used for evolving a pseudo-wavelet function for optimising the detection of muscle fatigue on any unseen sEMG signals. Tuning of the generalised evolved pseudo-wavelet function was based on the decomposition of twenty sEMG trials. After completing twenty independent pseudo-wavelet evolution runs, the best run was selected and then tested on ten previously unseen sEMG trials to measure the classification performance. Results show that an evolved pseudo-wavelet improved the classification of muscle fatigue between 7.31percent and 13.15percent when compared to other wavelet functions, giving an average correct classification of 88.41percent", } @InProceedings{AlNajar:2022:GI, author = "Mahmoud {Al Najar} and Rafael Almar and Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson", title = "Genetic Improvement of Shoreline Evolution Forecasting Models", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1916--1923", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, RCGP, symbolic regression, forecasting, shoreline evolution, earth observation salellite, CGP-ShoreFor, ShorefFor, physical sciences, geography, coastal erosion, Tairua New Zealand, Mielke correlation coefficient", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/AlNajar_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534041", slides_url = "https://sourcesup.renater.fr/wiki/atelieromp/_media/presentation_envia_mahmoud_al_ajar_omp_v2.pdf", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/najar-genetic-improvement-of-shoreline-evolution-forecasting-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=66UiDk9lsnc&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=6", size = "8+1 pages", abstract = "Coastal development and climate change are changing the geography of our coasts, while more and more people are moving towards the coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Cartesian Genetic Programming (CGP) is a form of Genetic Programming (GP) that has been successfully used in a large variety of tasks including data-driven symbolic regression. We formulate the problem of shoreline evolution forecasting as a Genetic Improvement (GI) problem using CGP to encode and improve upon ShoreFor, an equilibrium shoreline prediction model, to study the effectiveness of CGP in GI in forecasting tasks. This work presents an empirical study of the sensitivity of CGP to a number of evolutionary configurations and constraints and compares the performances of the evolved models to the base ShoreFor model.", notes = "Supplementary Material: Figure 8: Example evolved CGP-ShoreFor model graphs 15 years (dauly sampling). Goldman mutation \cite{goldman:2013:EuroGP} http://geneticimprovementofsoftware.com/events/gecco2022 GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{alnajar2023improving, author = "Mahmoud {Al Najar} and Rafael Almar and Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson", title = "Improving a Shoreline Forecasting Model with Symbolic Regression", booktitle = "ICLR 2023 Workshop on Tackling Climate Change with Machine Learning", year = "2023", address = "Kigali Rwanda", month = "4 " # may, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Machine Learning, Interpretable ML, XAI, Symbolic Computation, Earth Observation & Monitoring, Extreme Weather, Ocean, Atmosphere, Hybrid Physical Models, Time-series Analysis", URL = "https://www.climatechange.ai/papers/iclr2023/21", URL = "https://www.climatechange.ai/papers/iclr2023/21/paper.pdf", URL = "https://hal.science/hal-04281530", size = "12 pages", abstract = "Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones.", notes = "Published as a workshop paper at Tackling Climate Change with Machine Learning, ICLR 2023", } @PhdThesis{AlNajar:thesis, author = "Mahmoud {Al Najar}", title = "Estimating Coastal Evolution with Machine Learning", title_fr = "Estimation de l'evolution du littoral par l'apprentissage automatique", school = "University of Toulouse", year = "2023", address = "France", month = "30 " # nov, note = "forthcoming", keywords = "genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, Deep Learning, Earth Observation, Shoreline forecasting, Bathymetry inversion", URL = "https://www.isae-supaero.fr/IMG/pdf/annonce_soutenance_these_m_al_najar.pdf", URL = "https://zoom.us/my/dennisgwilson", abstract = "Forecasting coastal evolution is a prerequisite for effective coastal management and has been a fundamental goal of coastal research for decades. However, coastal evolution is a complex process, and predicting its development through time remains challenging. The absence of representative datasets which accurately track the state and evolution of coastal systems greatly limits our ability to study these processes given different natural and anthropological scenarios. While traditional field surveys have been used extensively in the literature and have served as important assets in advancing our knowledge of these systems, the high operational costs of traditional field surveys limit their use to local and sparse spatio-temporal scales. Satellite-based Remote Sensing (RS) provides the opportunity for frequently monitoring the Earth at high temporal resolutions and scales, but requires the development of novel data processing methodologies for large streams of Earth Observation data. Machine Learning (ML) is a subfield of Artificial Intelligence which aims at constructing algorithms able to leverage large amounts of example data in order to automatically construct predictive models, and has been a critical component of many scientific advancements in recent years. This thesis examines the potential and capability of modern ML in two important problems in Coastal Science where ML remains unexplored. Deep Learning and Interpretable Machine Learning are applied to the problems of satellite-derived bathymetry and shoreline evolution modelling. The work demonstrates that ML is competitive with current physics-based baselines on both tasks, and shows the potential of ML in automating many of our large-scale coastal data analysis towards gaining a global understanding of coastal evolution.", resume = "a prvision de l'volution du littoral est une condition pralable a une gestion efficace des cotes et constitue un objectif fondamental de la recherche cotiere depuis des dcennies. Cependant, l'volution des cotes est un processus complexe et la prvision de son dveloppement dans le temps reste un dfi. L'absence d'ensembles de donnes reprsentatives permettant de suivre avec prcision l'tat et l'volution des systemes cotiers limite considrablement notre capacit a tudier ces processus dans le cadre de diffrents scnarios naturels et anthropologiques. Bien que les enquetes traditionnelles sur le terrain aient t largement dans la littrature et qu'elles aient permis de faire progresser notre connaissance de ces systemes, les couts oprationnels levs des enquetes sur le terrain limitent leur utilisation a des chelles spatio-temporelles locales et parses. La tldtection par satellite permet de surveiller frquemment la Terre a des rsolutions et des chelles temporelles leves, mais ncessite le dveloppement de nouvelles mthodologies de traitement des donnes pour les grands flux de donnes d'observation de la Terre. L'apprentissage automatique est un sous-domaine de l'intelligence artificielle qui vise a construire des algorithmes capables d'exploiter de grandes quantits de donnes d'entrainement afin de construire automatiquement des modeles prdictifs, et a t un lment essentiel de nombreuses avances scientifiques au cours des dernieres annes. Cette these examine le potentiel et la capacit de l'apprentissage automatique moderne dans deux problemes importants de la science cotiere o l'apprentissage automatique reste inexplor. L'apprentissage profond et l'apprentissage automatique interprtable sont appliqus aux problemes de la bathymtrie drive des satellites et de la modlisation de l'volution du trait de cote. Le travail dmontre que l'apprentissage automatique est comptitif par rapport aux bases actuelles bases sur la physique pour les deux taches, et montre le potentiel de l'apprentissage automatique dans l'automatisation d'un grand nombre de nos analyses de donnes cotieres a grande chelle afin d'obtenir une comprhension globale de l'volution du littoral.", notes = "in english? Supervisor Rafael Almar", } @Article{ALOISIO:2023:ymssp, author = "Angelo Aloisio and Alessandro Contento and Rocco Alaggio and Giuseppe Quaranta", title = "Physics-based models, surrogate models and experimental assessment of the vehicle-bridge interaction in braking conditions", journal = "Mechanical Systems and Signal Processing", volume = "194", pages = "110276", year = "2023", ISSN = "0888-3270", DOI = "doi:10.1016/j.ymssp.2023.110276", URL = "https://www.sciencedirect.com/science/article/pii/S0888327023001838", keywords = "genetic algorithms, genetic programming, Bouncing, Braking, Bridge, Fragility curve, Machine learning, Moving load, Neural network, ANN, Pitching, Roughness, Surrogate model, Vehicle-bridge interaction", abstract = "The dynamics of roadway bridges crossed by vehicles moving at variable speed has attracted far less attention than that generated by vehicles travelling at constant velocity. Consequently, the role of some parameters and the combination thereof, as well as influence and accuracy of the modelling strategies, are not fully understood yet. Therefore, a large statistical analysis is performed in the present study to provide novel insights into the dynamic vehicle-bridge interaction (VBI) in braking conditions. To this end, an existing mid-span prestressed concrete bridge is selected as case study. First, several numerical simulations are performed considering alternative vehicle models (i.e., single and two degrees-of-freedom models) and different braking scenarios (i.e., soft and hard braking conditions, with both stationary and nonstationary road roughness models in case of soft braking). The statistical appraisal of the obtained results unfolds some effects of the dynamic VBI modelling in braking conditions that have not been reported in previous studies. Additionally, the use of machine learning techniques is explored for the first time to develop surrogate models able to predict the effect of the dynamic VBI in braking conditions efficiently. These surrogate models are then employed to obtain the fragility curve for the selected prestressed concrete bridge, where the attainment of the decompression moment is considered as relevant limit state. Whilst the derivation of the fragility curve using numerical simulations turned out to be almost unpractical using standard computational resources, the proposed approach that exploits surrogate models carried out via machine learning techniques was demonstrated accurate despite the dramatic reduction of the total elaboration time. Finally, the accuracy of the numerical (physics-based and surrogate) models is evaluated on a statistical basis through comparisons with experimental data", } @InProceedings{Alonso:2008:ieeeICTAI, author = "Cesar L. Alonso and Jorge Puente and Jose Luis Montana", title = "Straight Line Programs: A New Linear Genetic Programming Approach", booktitle = "20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '08", year = "2008", month = nov, volume = "2", pages = "517--524", keywords = "genetic algorithms, genetic programming, computer programs, data structure, linear genetic programming approach, program tree encoding, straight line programs, symbolic regression problems, linear programming, regression analysis, tree data structures", DOI = "doi:10.1109/ICTAI.2008.14", ISSN = "1082-3409", abstract = "Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described and new recombination operators for GP related to slp's are introduced. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.", notes = "Also known as \cite{4669818}", } @Article{Alonso:2009:IJAIT, author = "Cesar L. Alonso and Jose Luis Montana and Jorge Puente and Cruz Enrique Borges", title = "A new Linear Genetic Programming approach based on straight line programs: some Theoretical and Experimental Aspects", journal = "International Journal on Artificial Intelligence Tools", year = "2009", volume = "18", number = "5", pages = "757--781", keywords = "genetic algorithms, genetic programming, slp, Vapnik-Chervonenkis dimension, VC", oai = "oai:CiteSeerX.psu:10.1.1.301.3133", DOI = "doi:10.1142/S0218213009000391", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.3133", URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/08IJAIT.pdf", abstract = "Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.", notes = "IJAIT", } @InProceedings{Alonso:2009:ICTAI, author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique Borges", title = "Evolution Strategies for Constants Optimization in Genetic Programming", booktitle = "21st International Conference on Tools with Artificial Intelligence, ICTAI '09", year = "2009", month = nov, pages = "703--707", keywords = "genetic algorithms, genetic programming, computer program, constants optimization, evolutionary computation methods, learning problems, linear genetic programming approach, symbolic regression problem, regression analysis", DOI = "doi:10.1109/ICTAI.2009.35", ISSN = "1082-3409", abstract = "Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods to constants optimization in genetic programming. A general evolution strategy technique is proposed for approximating the optimal constants in a computer program representing the solution of a symbolic regression problem. The new algorithm has been compared with a recent linear genetic programming approach based on straight-line programs. The experimental results show that the proposed algorithm improves such technique.", notes = "Also known as \cite{5366517}", } @InProceedings{conf/ijcci/AlonsoMB13, author = "Cesar Luis Alonso and Jose Luis Montana and Cruz Enrique Borges", title = "Model Complexity Control in Straight Line Program Genetic Programming", booktitle = "Proceedings of the 5th International Joint Conference on Computational Intelligence, IJCCI 2013", year = "2013", editor = "Agostinho C. Rosa and Antonio Dourado and Kurosh Madani Correia and Joaquim Filipe and Janusz Kacprzyk", pages = "25--36", address = "Vilamoura, Algarve, Portugal", month = "20-22 " # sep, publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-8565-77-8", bibdate = "2014-05-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2013.html#AlonsoMB13", URL = "https://ijcci.scitevents.org/Abstract.aspx?idEvent=0fEvcjBHBM8=", URL = "https://www.scitepress.org/Link.aspx?doi=10.5220/0004554100250036", DOI = "doi:10.5220/0004554100250036", abstract = "In this paper we propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials) and real problems, show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalisation error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.", notes = "See also \cite{Alonso:2016:CI}", } @InCollection{Alonso:2016:CI, author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique Borges", title = "Genetic Programming Model Regularization", booktitle = "Computational Intelligence", publisher = "Springer", year = "2016", editor = "Kurosh Madani and Antonio Dourado and Agostinho Rosa and Joaquim Filipe and Janusz Kacprzyk", series = "Springer Professional Technik", pages = "105--120", note = "Selected extended papers from the fifth International Joint Conference on Computational Intelligence (IJCCI 2013), held in Vilamoura, Algarve, Portugal, from 20 to 22 September 2013", keywords = "genetic algorithms, genetic programming, VC dimension", isbn13 = "978-3-319-23391-8", URL = "https://www.springerprofessional.de/en/genetic-programming-model-regularization/6856568", DOI = "doi:10.1007/978-3-319-23392-5_6", abstract = "We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.", notes = "See also \cite{conf/ijcci/AlonsoMB13} Using incollection rather than inproceedings as the book seems more edited than a usual conference proceedings, see DBLP http://dblp.uni-trier.de/db/conf/ijcci/ijcci2013.html https://www.springerprofessional.de/en/computational-intelligence/6652954 Centro de Inteligencia Artificial, Campus de Gijon, Universidad de Oviedo, 33271, Gijon, Spain", } @InProceedings{conf/incdm/AlonsoMPSV08, title = "Modelling Medical Time Series Using Grammar-Guided Genetic Programming", author = "Fernando Alonso and Loic Martinez and Aurora Perez-Perez and Agustin Santamaria and Juan Pedro Valente", bibdate = "2010-02-01", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/incdm/incdm2008.html#AlonsoMPSV08", booktitle = "8th Industrial Conference in Data Mining, Medical Applications, E-Commerce, Marketing and Theoretical Aspects, ICDM 2008", publisher = "Springer", year = "2008", volume = "5077", editor = "Petra Perner", isbn13 = "978-3-540-70717-2", pages = "32--46", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-70720-2_3", address = "Leipzig, Germany", month = jul # " 16-18", keywords = "genetic algorithms, genetic programming, Time series characterization, isokinetics, symbolic distance, information extraction, reference model, text mining", size = "15 pages", abstract = "The analysis of time series is extremely important in the field of medicine, because this is the format of many medical data types. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. This paper describes the definition of the symbolic domain, the process of converting numerical into symbolic time series and a distance for comparing symbolic temporal sequences. Then, the paper focuses on a method to create the symbolic reference model for a certain population using grammar-guided genetic programming. The work is applied to the isokinetics domain within an application called I4.", notes = "Context Free Grammar", } @InProceedings{Alonso:2010:gecco, author = "Fernando Alonso and Loic Martinez and Agustin Santamaria and Aurora Perez and Juan Pedro Valente", title = "GGGP-based method for modeling time series: operator selection, parameter optimization and expert evaluation", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "989--990", keywords = "genetic algorithms, genetic programming, grammar-guided genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830664", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper describes the theoretical and experimental analysis conducted to define the best values for the various operators and parameters of a grammar-guided genetic programming process for creating isokinetic reference models for top competition athletes. Isokinetics is a medical domain that studies the strength exerted by the patient joints (knee, ankle, etc.). We also present an evaluation of the resulting reference models comparing our results with the reference models output using other methods.", notes = "Also known as \cite{1830664} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{alotaibi:2023:Buildings, author = "Khalid Saqer Alotaibi and A. B. M. Saiful Islam", title = "Symbolic Regression Model for Predicting Compression Strength of Prismatic Masonry Columns Confined by {FRP}", journal = "Buildings", year = "2023", volume = "13", number = "2", pages = "Article No. 509", keywords = "genetic algorithms, genetic programming", ISSN = "2075-5309", URL = "https://www.mdpi.com/2075-5309/13/2/509", DOI = "doi:10.3390/buildings13020509", abstract = "The use of Fiber Reinforced Polymer (FRP) materials for the external confinement of existing concrete or masonry members is now an established technical solution. Several studies in the scientific literature show how FRP wrapping can improve the mechanical properties of members. Though there are numerous methods for determining the compressive strength of FRP confined concrete, no generalised formulae are available because of the greater complexity and heterogeneity of FRP-confined masonry. There are two main objectives in this analytical study: (a) proposing an entirely new mathematical expression to estimate the compressive strength of FRP confined masonry columns using symbolic regression model approach which can outperform traditional regression models, and (b) evaluating existing formulas. Over 198 tests of FRP wrapped masonry were compiled in a database and used to train the model. Several formulations from the published literature and international guidelines have been compared against experimental data. It is observed that the proposed symbolic regression model shows excellent performance compared to the existing models. The model is easier, has no restriction and thereby it can be feasibly employed to foresee the behaviour of FRP confined masonry elements. The coefficient of determination for the proposed symbolic regression model is determined as 0.91.", notes = "also known as \cite{buildings13020509}", } @PhdThesis{MoniraAloud-Ph.D.Thesis, author = "Monira Essa Aloud", title = "Modelling the High-Frequency {FX} Market: An Agent-Based Approach", school = "Department of Computing and Electronic Systems, University of Essex", year = "2013", address = "United Kingdom", month = apr, keywords = "genetic algorithms, genetic programming", URL = "http://fac.ksu.edu.sa/sites/default/files/MoniraAloud-Ph.D.Thesis.pdf", size = "183 pages", abstract = "In this thesis, we use an agent-based modelling (ABM) approach to model the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We first establish the statistical properties (stylised facts) of the trading activity in the FX market using a unique high-frequency dataset of anonymised individual traders' historical transactions on an account level, spanning 2.25 years. To the best of our knowledge, this dataset is the biggest available high-frequency dataset of individual FX market traders' historical transactions. We then construct an agentbased FX market (ABFXM) which features a number of distinguishing elements including zero-intelligence directional-change event (ZI-DCT0) trading agents and asynchronous trading-time windows. The individual agents are characterised by different levels of wealth, trading time windows, different profit objectives and risk appetites and initial activation conditions. Using the identified stylized facts as a benchmark, we evaluate the trading activity reproduced from the ABFXM and we establish that this resembles to a satisfactory level the trading activity of the real FX market. In the course of this thesis, we study in depth the constructed ABFXM. We focus on performing a systematic exploration of the constituent elements of the ABFXM and their impact on the dynamics of the FX market behaviour. In particular, our study explores and identifies the essential elements under which the stylised facts of the FX market trading activity are exhibited in the ABFXM. Our study suggests that the key elements are the ZI-DCT0 agents, heterogeneity which has been embedded in our model in different ways, asynchronous trading time windows, initial activation conditions and the generation of limit orders. We also show that the dynamics of the market trading activity depend on the number of agents one considers. We explore the emergence of the stylised facts in the trading activity when the ABFXM is populated with agents with three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the ZI-DCT0 strategy; and a genetic programming-based (GP) strategy. Our results show that the ZI-DCT0 agents best reproduce and explain the stylised facts observed in the FX market transactions data. Our study suggests that some the observed stylised facts could be the result of introducing a threshold which triggers the agents to respond to fixed periodic patterns in the price time series.", notes = "Supervisor: Prof. Maria Fasli, Prof. Edward Tsang and Prof. Richard Olsen", } @Article{aloud:2017:coin, author = "Monira Aloud and Maria Fasli and Edward Tsang and Alexander Dupuis and Richard Olsen", title = "Modeling the High-Frequency {FX} Market: An Agent-Based Approach", journal = "Computational Intelligence", year = "2017", volume = "33", number = "4", pages = "771--825", month = nov, keywords = "genetic algorithms, genetic programming, agent-based modeling, agent-based simulation, electronic markets, FX markets, stylized facts.", ISSN = "1467-8640", URL = "http://repository.essex.ac.uk/18823/", DOI = "doi:10.1111/coin.12114", size = "51 pages", abstract = "The development of computational intelligence-based strategies for electronic markets has been the focus of intense research. To be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use, and their interactions as well as the patterns emerging as a result of these interactions. In this article, we develop an agent-based model of the foreign exchange (FX) market, which is the market for the buying and selling of currencies. Our agent-based model of the FX market comprises heterogeneous trading agents that employ a strategy that identifies and responds to periodic patterns in the price time series. We use the agent-based model of the FX market to undertake a systematic exploration of its constituent elements and their impact on the stylised facts (statistical patterns) of transactions data. This enables us to identify a set of sufficient conditions that result in the emergence of the stylized facts similarly to the real market data, and formulate a model that closely approximates the stylized facts. We use a unique high-frequency data set of historical transactions data that enables us to run multiple simulation runs and validate our approach and draw comparisons and conclusions for each market setting.", } @Article{Al-Rabadi:2006:EPB, author = "Anas N. Al-Rabadi", title = "Book Review: {Lee Spector $\bullet$ Automatic Quantum Computer Programming: A Genetic Programming Approach. Kluwer Academic Publishers (2004). ISBN 1-4020-7894-3. 100. 153 pp.}", journal = "The Computer Journal", volume = "49", number = "1", pages = "129--130", month = jan, year = "2006", CODEN = "CMPJA6", ISSN = "0010-4620", bibdate = "Wed Dec 21 17:38:55 MST 2005", bibsource = "http://comjnl.oxfordjournals.org/content/vol49/issue1/index.dtl", URL = "http://comjnl.oxfordjournals.org/cgi/content/full/49/1/129", URL = "http://comjnl.oxfordjournals.org/cgi/reprint/49/1/129", acknowledgement = "ack-nhfb", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1093/comjnl/bxh134", notes = "review of \cite{spector:book}", } @InProceedings{Alrefaie:2013:CIES, author = "Mohamed Taher Alrefaie and Alaa-Aldine Hamouda and Rabie Ramadan", booktitle = "IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES 2013)", title = "A smart agent to trade and predict foreign exchange market", year = "2013", month = apr, pages = "141--148", keywords = "genetic algorithms, genetic programming, foreign exchange trading, probability, US dollars daily turnover, adaptive neuro-fuzzy inference system, foreign exchange market, genetic programming approach, probability, smart agent, Companies, Fluctuations, Market research, Prediction algorithms, Predictive models, Profitability, ANFI, Agent, Forex, NSGA-II, Prediction", DOI = "doi:10.1109/CIES.2013.6611741", size = "8 pages", abstract = "Foreign Exchange market is a worldwide market to exchange currencies with 3.98 trillion US dollars daily turnover. With such a massive turnover, probability of profit is very high; however, trading in such massive market needs high knowledge, skills and full commitment in order to achieve high profit. The purpose of this work is to design a smart agent that 1) acquire Foreign Exchange market prices, 2) pre-processes it, 3) predicts future trend using Genetic Programming approach and Adaptive Neuro-fuzzy Inference System and 4) makes a buy/sell decision to maximise profitability with no human supervision.", notes = "Also known as \cite{6611741}", } @Article{ALSAFY:2019:CBM, author = "Rawaa Al-Safy and Alaa Al-Mosawe and Riadh Al-Mahaidi", title = "Utilization of magnetic water in cementitious adhesive for near-surface mounted {CFRP} strengthening system", journal = "Construction and Building Materials", volume = "197", pages = "474--488", year = "2019", keywords = "genetic algorithms, genetic programming, Magnetic water, Cement-based adhesive, NSM, CFRP, Concrete, GP modelling", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2018.11.219", URL = "http://www.sciencedirect.com/science/article/pii/S0950061818329143", abstract = "Cement-based adhesive (CBA) is used as a bonding agent in Carbon Fibre Reinforced Polymer (CFRP) applications as an alternative to epoxy-based adhesive due to the drawbacks of the epoxy system under severe service conditions which negatively affect the bond between the CFRP and strengthened elements. This paper reports the results of, an investigation carried out to develop two types of CBA using magnetized water (MW) for mixing and curing. Two magnetic devices (MD-I and MD-II), with different magnetic field strengths (9000 and 6000 Gauss) respectively, were employed for water magnetization. Different water flows with different water circulation times in the magnetizer were used for each device. Compressive and splitting tensile strength tests of the magnetized CBA (MCBA) were conducted for different curing periods (3. 7, 14, 21 and 28a days) using MW. It was found that MW treatment increases the strength of CBA. The highest strength was obtained for MCBA samples when MD-I was used at a low flow rate (Fa =a 0.1a m3/hr) for 15 mins of circulation time (T). The latter was found to positively affect MCBA properties when T was increased from 15a min to 60a mins. Prediction of the compressive and tensile strength values are also studied in this paper using genetic programming, the models showed good correlation with the experimental data", } @InProceedings{Al-Sahaf:2011:ICARA, author = "Harith Al-Sahaf and Kourosh Neshatian and Mengjie Zhang", title = "Automatic feature extraction and image classification using genetic programming", booktitle = "5th International Conference on Automation, Robotics and Applications (ICARA 2011)", year = "2011", month = "6-8 " # dec, pages = "157--162", address = "Wellington, New Zealand", size = "6 pages", abstract = "In this paper, we propose a multilayer domain-independent GP-based approach to feature extraction and image classification. We propose two different structures for the system and compare the results with a baseline approach in which domain-specific pre-extracted features are used for classification. In the baseline approach, human/domain expert intervention is required to perform the task of feature extraction. The proposed approach, however, extracts (evolves) features and generates classifiers all automatically in one loop. The experiments are conducted on four image data sets. The results show that the proposed approach can achieve better performance compared to the baseline while removing the human from the loop.", keywords = "genetic algorithms, genetic programming, feature extraction, human-domain expert intervention, image classification, multilayer domain-independent GP-based approach, feature extraction, image classification", DOI = "doi:10.1109/ICARA.2011.6144874", notes = "Also known as \cite{6144874}", } @InProceedings{Al-Sahaf:2012:CEC, title = "Extracting Image Features for Classification By Two-Tier Genetic Programming", author = "Harith Al-Sahaf and Andy Song and Kourosh Neshatian and Mengjie Zhang", pages = "1630--1637", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256412", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Evolutionary Computer Vision", abstract = "Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{AlSahaf2012, author = "Harith Al-Sahaf and Andy Song and Kourosh Neshatian and Mengjie Zhang", title = "Two-Tier genetic programming: towards raw pixel-based image classification", journal = "Expert Systems with Applications", volume = "39", number = "16", pages = "12291--12301", year = "2012", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2012.02.123", URL = "http://www.sciencedirect.com/science/article/pii/S0957417412003867", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Feature extraction, Feature selection, Image classification", abstract = "Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately.", } @InProceedings{Al-Sahaf:2013:CEC, article_id = "1692", author = "Harith Al-Sahaf and Andy Song and Mengjie Zhang", title = "Hybridisation of Genetic Programming and Nearest Neighbour for Classification", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "2650--2657", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557889", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Al-Sahaf:2013:IVCNZ, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Binary image classification using genetic programming based on local binary patterns", booktitle = "28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)", year = "2013", pages = "220--225", address = "Wellington", month = nov, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, computer vision, image classification, learning (artificial intelligence), statistical analysis, ANOVA, GP based methods, LBP, SVM, binary image classification, computer vision, image descriptor, learning instances, local binary patterns, machine learning, nonGP methods, one-way analysis of variance, support vector machine, wrapped classifiers, Accuracy, Analysis of variance, Feature extraction, Histograms, Support vector machines, Training, Vectors", DOI = "doi:10.1109/IVCNZ.2013.6727019", size = "6 pages", abstract = "Image classification represents an important task in machine learning and computer vision. To capture features covering a diversity of different objects, it has been observed that a sufficient number of learning instances are required to efficiently estimate the models' parameter values. In this paper, we propose a genetic programming (GP) based method for the problem of binary image classification that uses a single instance per class to evolve a classifier. The method uses local binary patterns (LBP) as an image descriptor, support vector machine (SVM) as a classifier, and a one-way analysis of variance (ANOVA) as an analyser. Furthermore, a multi-objective fitness function is designed to detect distinct and informative regions of the images, and measure the goodness of the wrapped classifiers. The performance of the proposed method has been evaluated on six data sets and compared to the performances of both GP based (Two-tier GP and conventional GP) and non-GP (Naive Bayes, Support Vector Machines and hybrid Naive Bayes/Decision Trees) methods. The results show that a comparable or significantly better performance has been achieved by the proposed method over all methods on all of the data sets considered.", notes = "also known as \cite{6727019}", } @InProceedings{Al-Sahaf:2013:AI, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "A One-Shot Learning Approach to Image Classification Using Genetic Programming", booktitle = "Proceedings of the 26th Australasian Joint Conference on Artificial Intelligence (AI2013)", year = "2013", editor = "Stephen Cranefield and Abhaya Nayak", volume = "8272", series = "LNAI", pages = "110--122", address = "Dunedin, New Zealand", month = "1-6 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Local Binary Patterns, Image Classification, One-shot Learning", isbn13 = "978-3-319-03679-3", URL = "http://dx.doi.org/10.1007/978-3-319-03680-9_13", DOI = "doi:10.1007/978-3-319-03680-9_13", size = "13 pages", abstract = "In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naive Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features.", } @InProceedings{conf/ivcnz/Al-SahafZJ14, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification", booktitle = "Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, {IVCNZ} 2014", publisher = "ACM", year = "2014", editor = "Michael J. Cree and Lee V. Streeter and John Perrone and Michael Mayo and Anthony M. Blake", pages = "84--89", address = "Hamilton, New Zealand", month = nov # " 19-21", keywords = "genetic algorithms, genetic programming, Multiclass classification, Textures", isbn13 = "978-1-4503-3184-5", bibdate = "2015-01-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ivcnz/ivcnz2014.html#Al-SahafZJ14", DOI = "doi:10.1145/2683405.2683418", acmid = "2683418", abstract = "Texture classification is an essential task in pattern recognition and computer vision. In this paper, a novel genetic programming (GP) based method is proposed for the task of multiclass texture classification. The proposed method evolves a set of filters using only two instances per class. Moreover, the evolved program operates directly on the raw pixel values and does not require human intervention to perform feature selection and extraction. Two well-known and widely used data sets are used in this study to evaluate the performance of the proposed method. The performance of the new method is compared to that of two GP-based methods using the raw pixel values, and six non-GP methods using three different sets of domain-specific features. The results show that the proposed method has significantly outperformed the other methods on both data sets.", URL = "http://dl.acm.org/citation.cfm?id=2683405", } @InProceedings{conf/seal/Al-SahafZJ14, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances", bibdate = "2014-11-12", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2014.html#Al-SahafZJ14", booktitle = "Simulated Evolution and Learning - 10th International Conference, {SEAL} 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings", publisher = "Springer", year = "2014", volume = "8886", editor = "Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang", isbn13 = "978-3-319-13562-5", pages = "335--346", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-13563-2", } @InProceedings{Al-Sahaf:2015:CEC, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston and Brijesh Verma", title = "Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2460--2467", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257190", size = "8 pages", abstract = "Texture classification is an essential task in computer vision that aims at grouping instances that have a similar repetitive pattern into one group. Detecting texture primitives can be used to discriminate between materials of different types. The process of detecting prominent features from the texture instances represents a cornerstone step in texture classification. Moreover, building a good model using a few training instances is difficult. In this study, a genetic programming (GP) descriptor is proposed for the task of multiclass texture classification. The proposed method synthesises a set of mathematical formulas relying on the raw pixel values and a sliding window of a predetermined size. Furthermore, only two instances per class are used to automatically evolve a descriptor that has the potential to effectively discriminate between instances of different textures using a simple instance-based classifier to perform the classification task. The performance of the proposed approach is examined using two widely-used data sets, and compared with two GP-based and nine well-known non-GP methods. Furthermore, three hand-crafted domain-expert designed feature extraction methods have been used with the non-GP methods to examine the effectiveness of the proposed method. The results show that the proposed method has significantly outperformed all these other methods on both data sets, and the new method evolves a descriptor that is capable of achieving significantly better performance compared to hand-crafted features.", notes = "1340 hrs 15390 CEC2015", } @InProceedings{Al-Sahaf:2015:GECCO, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "975--982", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754661", DOI = "doi:10.1145/2739480.2754661", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Texture classification aims at categorising instances that have a similar repetitive pattern. In computer vision, texture classification represents a fundamental element in a wide variety of applications, which can be performed by detecting texture primitives of the different classes. Using image descriptors to detect prominent features has been widely adopted in computer vision. Building an effective descriptor becomes more challenging when there are only a few labelled instances. This paper proposes a new Genetic Programming (GP) representation for evolving an image descriptor that operates directly on the raw pixel values and uses only two instances per class. The new method synthesises a set of mathematical formulas that are used to generate the feature vector, and the classification is then performed using a simple instance-based classifier. Determining the length of the feature vector is automatically handled by the new method. Two GP and nine well-known non-GP methods are compared on two texture image data sets for texture classification in order to test the effectiveness of the proposed method. The proposed method is also compared to three hand-crafted descriptors namely domain-independent features, local binary patterns, and Haralick texture features. The results show that the proposed method has superior performance over the competitive methods.", notes = "Also known as \cite{2754661} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Al-Sahaf:2015:EC, author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston", title = "Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances", journal = "Evolutionary Computation", year = "2016", volume = "24", number = "1", pages = "143--182", month = "Spring", keywords = "genetic algorithms, genetic programming, Local Binary Patterns, One-shot Learning, Image Classification", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00146", size = "37 pages", abstract = "In the Computer Vision and Pattern Recognition fields, image classification represents an important, yet difficult, task to perform. The remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class, is a challenge to build effective computer models to replicate this ability. Recently, we have proposed two Genetic Programming (GP) based methods, One-shot GP and Compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. Ten data sets that vary in difficulty have been used to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that One-shot GP and Compound-GP outperform or achieve comparable results to other competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases", } @Article{Al-Sahaf:2016:ieeeTEC, author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mark Johnston and Mengjie Zhang", title = "Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "1", pages = "83--101", month = feb, DOI = "doi:10.1109/TEVC.2016.2577548", notes = "May 2018 opps duplicate of \cite{Al-Sahaf:2017a:ieeeTEC}", } @Article{Al-Sahaf:2017a:ieeeTEC, author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mark Johnston and Mengjie Zhang", title = "Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "1", pages = "83--101", month = feb, keywords = "genetic algorithms, genetic programming, Classification, feature extraction, image descriptor, keypoint detection", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2016.2577548", size = "19 pages", abstract = "In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.", notes = "also known as \cite{7486119}", } @InProceedings{Al-Sahaf:2017:GECCO, author = "Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Evolving Texture Image Descriptors Using a Multitree Genetic Programming Representation", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "219--220", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076039", DOI = "doi:10.1145/3067695.3076039", acmid = "3076039", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multiclass classification, multitree, textures", month = "15-19 " # jul, abstract = "Image descriptors play very important roles in a wide range of applications in computer vision and pattern recognition. In this paper, a multitree genetic programming method to automatically evolve image descriptors for multiclass texture image classification task is proposed. Instead of using domain knowledge, the proposed method uses only a few instances of each class to automatically identify a set of features that are distinctive between the instances of different classes. The results on seven texture classification datasets show significant, or comparable, performance has been achieved by the proposed method compared with the baseline method and six state-of-the-art methods.", notes = "Also known as \cite{Al-Sahaf:2017:ETI:3067695.3076039} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{conf/seal/Al-SahafXZ17, author = "Harith Al-Sahaf and Bing Xue and Mengjie Zhang", title = "A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors", booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017", year = "2017", editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin", volume = "10593", series = "Lecture Notes in Computer Science", pages = "499--511", address = "Shenzhen, China", month = nov # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multitree, Image classification, Feature extraction", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/seal/seal2017.html#Al-SahafXZ17", isbn13 = "978-3-319-68758-2", DOI = "doi:10.1007/978-3-319-68759-9_41", abstract = "Image descriptors are very important components in computer vision and pattern recognition that play critical roles in a wide range of applications. The main task of an image descriptor is to automatically detect micro-patterns in an image and generate a feature vector. A domain expert is often needed to undertake the process of developing an image descriptor. However, such an expert, in many cases, is difficult to find or expensive to employ. In this paper, a multitree genetic programming representation is adopted to automatically evolve image descriptors. Unlike existing hand-crafted image descriptors, the proposed method does not rely on predetermined features, instead, it automatically identifies a set of features using a few instances of each class. The performance of the proposed method is assessed using seven benchmark texture classification datasets and compared to seven state-of-the-art methods. The results show that the new method has significantly outperformed its counterpart methods in most cases.", } @Article{Al-Sahaf:2017:ieeeTEC, author = "Harith Al-Sahaf and Mengjie Zhang and Ausama Al-Sahaf and Mark Johnston", journal = "IEEE Transactions on Evolutionary Computation", title = "Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors", year = "2017", volume = "21", number = "6", pages = "825--844", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7885048", DOI = "doi:10.1109/TEVC.2017.2685639", abstract = "The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to use Genetic Programming to automatically construct a rotation-invariant image descriptor by synthesising a set of formulae using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.", notes = "Also known as \cite{7885048}", } @PhdThesis{Al-Sahaf:thesis, author = "Harith Al-Sahaf", title = "Genetic Programming for Automatically Synthesising Robust Image Descriptors with A Small Number of Instances", school = "School of Engineering and Computer Science, Victoria University of Wellington", year = "2017", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/6177", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/6177/thesis.pdf", size = "321 pages", abstract = "Image classification is a core task in many applications of computer vision, including object detection and recognition. It aims at analysing the visual content and automatically categorising a set of images into different groups. Performing image classification can largely be affected by the features used to perform this task. Extracting features from images is a challenging task due to the large search space size and practical requirements such as domain knowledge and human intervention. Human intervention is usually needed to identify a good set of keypoints (regions of interest), design a set of features to be extracted from those keypoints such as lines and corners, and develop a way to extract those features. Automating these tasks has great potential to dramatically decrease the time and cost, and may potentially improve the performance of the classification task. There are two well-recognised approaches in the literature to automate the processes of identifying keypoints and extracting image features. Designing a set of domain-independent features is the first approach, where the focus is on dividing the image into a number of predefined regions and extracting features from those regions. The second approach is synthesising a function or a set of functions to form an image descriptor that aims at automatically detecting a set of keypoints such as lines and corners, and performing feature extraction. Although employing image descriptors is more effective and very popular in the literature, designing those descriptors is a difficult task that in most cases requires domain-expert intervention. The overall goal of this thesis is to develop a new domain independent Genetic Programming (GP) approach to image classification by using GP to evolve programs that are capable of automatically detecting diverse and informative keypoints, designing a set of features, and performing feature extraction using only a small number of training instances to facilitate image classification, and are robust to different image changes such as illumination and rotation. This thesis focuses on incorporating a variety of simple arithmetic operators and first-order statistics (mid-level features) into the evolutionary process and on representation of GP to evolve programs that are robust to image changes for image classification. This thesis proposes methods for domain-independent binary classification in images using GP to automatically identify regions within an image that have the potential to improve classification while considering the limitation of having a small training set. Experimental results show that in over 67percent of cases the new methods significantly outperform the use of existing hand-crafted features and features automatically detected by other methods. This thesis proposes the first GP approach for automatically evolving an illumination-invariant dense image descriptor that detects automatically designed keypoints, and performs feature extraction using only a few instances of each class. The experimental results show improvement of 86percent on average compared to two GP-based methods, and can significantly outperform domain-expert hand-crafted descriptors in more than 89percent of the cases. This thesis also considers rotation variation of images and proposes a method for automatically evolving rotation-invariant image descriptors through integrating a set of first-order statistics as terminals. Compared to hand-crafted descriptors, the experimental results reveal that the proposed method has significantly better performance in more than 83percent of the cases. This thesis proposes a new GP representation that allows the system to automatically choose the length of the feature vector side-by-side with evolving an image descriptor. Automatically determining the length of the feature vector helps to reduce the number of the parameters to be set. The results show that this method has evolved descriptors with a very small feature vector which yet still significantly outperform the competitive methods in more than 91percent of the cases. This thesis proposes a method for transfer learning by model in GP, where an image descriptor evolved on instances of a related problem (source domain) is applied directly to solve a problem being tackled (target domain). The results show that the new method evolves image descriptors that have better generalisability compared to hand-crafted image descriptors. Those automatically evolved descriptors show positive influence on classifying the target domain datasets in more than 56percent of the cases.", notes = "supervisors: Mengjie Zhang and Mark Johnston", } @Article{Al-Sahaf:EC, author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mengjie Zhang", title = "Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances", journal = "Evolutionary Computation", year = "2021", volume = "29", number = "3", pages = "331--366", month = "Fall", keywords = "genetic algorithms, genetic programming, ANN, image descriptor, multi-tree, image classification, feature extraction", ISSN = "1063-6560", URL = "https://doi.org/10.1162/evco_a_00284", DOI = "doi:10.1162/evco_a_00284", size = "36 pages", abstract = "The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, for example, corners, line-segments, and shapes, in an image and extracting features from those key points. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by using a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.", } @InProceedings{eurogp:Al-SakranKJ05, author = "Sameer H. Al-Sakran and John R. Koza and Lee W. Jones", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "Automated Re-invention of a Previously Patented Optical Lens System Using Genetic Programming", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "25--37", DOI = "doi:10.1007/978-3-540-31989-4_3", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "The three dozen or so known instances of human-competitive designs produced by genetic programming for antennas, mechanical systems, circuits, and controllers raise the question of whether the genetic programming can be extended to the design of complex structures from other fields. This paper discusses efforts to apply genetic programming to the automated design of optical lens systems. The paper can be read from two different perspectives. First, broadly, it chronicles the step-by-step process by which the authors approached the problem of applying genetic programming to a domain that was new to them. Second, more narrowly, it describes the use of genetic programming to re-create the complete design for the previously patented Tackaberry-Muller optical lens system. Genetic programming accomplished this {"}from scratch{"} without starting from a pre-specified number of lens and a pre-specified layout and without starting from a pre-existing good design. The genetically evolved design for the Tackaberry-Muller lens system is an example, in the field of optical design, of a human-competitive result produced by genetic programming.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @InProceedings{Al_Sallami:2012:wce, title = "Genetic Programming Testing Model", author = "Nada M. A. {Al Sallami}", booktitle = "Proceedings of the World Congress on Engineering (WCE'12)", year = "2012", editor = "S. I. Ao and Len Gelman and David WL Hukins and Andrew Hunter and A. M. Korsunsky", series = "Lecture Notes in Engineering and Computer Science", pages = "737--741", address = "London, UK", month = jul # " 4-6", organization = "International Association of Engineers", publisher = "Newswood Limited", keywords = "genetic algorithms, genetic programming, SBSE, model-based testing, test generator, finite state machine", isbn13 = "978-988-19252-1-3", URL = "http://www.iaeng.org/publication/WCE2012/WCE2012_pp737-741.pdf", size = "5", abstract = "Software testing requires the use of a model to guide such efforts as test selection and test verification. In this case, testers are performing model-based testing. This paper introduces model-based testing and discusses its tasks in general terms with proposed finite state models. These FSMs depend on software's semantic rather than its structure, , it use input-output specification and trajectory information to evolve and test general software. Finally, we close with a discussion of how our model-based testing can be used with genetic programming test generator.", notes = "volume II: The 2012 International Conference of Computational Intelligence and Intelligent Systems", } @Article{Alsberg:2000:CILS, author = "Bjorn K. Alsberg and Nathalie Marchand-Geneste and Ross D. King", title = "A new {3D} molecular structure representation using quantum topology with application to structure-property relationships", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2000", volume = "54", pages = "75--91", number = "2", month = "29 " # dec, keywords = "genetic algorithms, genetic programming, Structure representation using quantum topology, StruQT, Quantitative structure-activity relationships, QSAR, Quantitative structure-property relationships, QSPR, Atoms in molecules, AIM, Quantum chemistry, Bader theory, Multivariate analysis, Partial least squares regression, 3D structure representation, Variable selection", ISSN = "0169-7439", owner = "wlangdon", broken = "http://www.sciencedirect.com/science/article/B6TFP-426XTF7-1/2/36265a259de8f80d4918ee6612612218", DOI = "doi:10.1016/S0169-7439(00)00101-5", abstract = "We present a new 3D molecular structure representation based on Richard F.W. Bader's quantum topological atoms in molecules (AIM) theory for use in quantitative structure-property/activity relationship (QSPR/QSAR) modelling. Central to this structure representation using quantum topology (StruQT) are critical points located on the electron density distribution of the molecules. Other gradient fields such as the Laplacian of the electron density distribution can also be used. The type of critical point of particular interest is the bond critical point (BCP) which is here characterised by using the following three parameters: electron density [rho], the Laplacian [nabla]2[rho] and the ellipticity [epsi]. This representation has the advantage that there is no need to probe a large number of lattice points in 3D space to capture the important parts of the 3D electronic structure as is necessary in, e.g. comparative field analysis (CoMFA). We tested the new structure representation by predicting the wavelength of the lowest UV transition for a system of 18 anthocyanidins. Different quantitative structure-property relationship (QSPR) models are constructed using several chemometric/machine learning methods such as standard partial least squares regression (PLS), truncated PLS variable selection, genetic algorithm-based variable selection and genetic programming (GP). These models identified bonds that either take part in decreasing or increasing the dominant excitation wavelength. The models also correctly emphasised on the involvement of the conjugated [pi] system for predicting the wavelength through flagging the BCP ellipticity parameters as important for this particular data set.", } @InProceedings{Alshahwan:2018:SSBSE, author = "Nadia Alshahwan and Xinbo Gao and Mark Harman and Yue Jia and Ke Mao and Alexander Mols and Taijin Tei and Ilya Zorin", title = "Deploying Search Based Software Engineering with {Sapienz} at {Facebook}", booktitle = "SSBSE 2018", year = "2018", editor = "Thelma Elita Colanzi and Phil McMinn", volume = "11036", series = "LNCS", pages = "3--45", address = "Montpellier, France", month = "8-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-3-319-99241-9", URL = "https://discovery.ucl.ac.uk/id/eprint/10060107/", URL = "https://rdcu.be/dBszz", DOI = "doi:10.1007/978-3-319-99241-9_1", video_url = "https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/", size = "22 pages", abstract = "We describe the deployment of the Sapienz Search Based Software Engineering (SBSE) testing system. Sapienz has been deployed in production at Facebook since September 2017 to design test cases, localise and triage crashes to developers and to monitor their fixes. Since then, running in fully continuous integration within Facebook's production development process, Sapienz has been testing Facebook's Android app, which consists of millions of lines of code and is used daily by hundreds of millions of people around the globe. We continue to build on the Sapienz infrastructure, extending it to provide other software engineering services, applying it to other apps and platforms, and hope this will yield further industrial interest in and uptake of SBSE (and hybridisations of SBSE) as a result.", notes = "This paper was written to accompany the keynote by Mark Harman at the 10th Symposium on Search-Based Software Engineering (SSBSE 2018), Montpellier September 8-10, 2018. The paper represents the work of all the authors in realising the deployment of search based approaches to large-scale software engineering at Facebook. Author name order is alphabetical; the order is thus not intended to denote any information about the relative contribution of each author. Ke Mao will also be giving a related talk about Sapienz deployment to developers at the @Scale developers conference in San Jose, USA on 13 September 2018 https://atscaleconference.com/events/the-2018-scale-conference/ A video of a previous talk about the initial Sapienz deployment, presented at the F8 developers conference in May 2018, is also publicly available (also as high quality video recording and with no paywall): https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/ also known as \cite{ssbse18-keynote}", } @InProceedings{Alshahwan:2019:GI, author = "Nadia Alshahwan", title = "Industrial experience of Genetic Improvement in {Facebook}", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "1", address = "Montreal", month = "28 " # may, publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, genetic improvement, APR", isbn13 = "978-1-7281-2268-7", URL = "https://doi.org/10.1109/GI.2019.00010", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2019/Alshahwan_2019_GI.pdf", DOI = "doi:10.1109/GI.2019.00010", acmid = "3339021", size = "1 page", abstract = "Facebook recently had their first experience with Genetic Improvement (GI) by developing and deploying the automated bug fixing tool SapFix. The experience was successful resulting in landed fixes but also very educational. This paper will briefly outline some of the challenges for GI that were highlighted by this experience as well as a look at future directions in the area of mobile apps.", notes = "GI-2019 http://geneticimprovementofsoftware.com part of \cite{Petke:2019:ICSEworkshop}", } @InProceedings{Alshahwan:2023:ICST, author = "Nadia Alshahwan and Mark Harman and Alexandru Marginean", title = "Software Testing Research Challenges: An Industrial Perspective", booktitle = "16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023)", year = "2023", editor = "Sreedevi Sampath", pages = "1--10", address = "Dublin, Ireland", month = "16-20 " # apr, note = "Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Automated Software Engineering, Software Testing, Automated Program Repair, APR, Artificial Intelligence, AI, Automated Remediation, regression testing", isbn13 = "978-1-6654-5667-8", URL = "https://research.facebook.com/file/1235985840680898/Software-Testing-Research-Challenges--An-Industrial-Perspective.pdf", URL = "https://conf.researchr.org/track/icst-2023/icst-2023-keynotes", DOI = "doi:10.1109/ICST57152.2023.00008", size = "10 pages", abstract = "There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.", notes = "descretization of performance execution time may lead to performance test flakiness 'need more research ... on small performance [changes] ... in noisy ... [and better] confidence intervals' 'Technologies that significantly impede developer velocity are typically discarded' 'expect rapid uptake of automated [unit] test generation' 'including the generation of suitable test oracles' and 'appropriate mocks'. 'need techniques for constructing realistic values for complex data types' e2e = end-to-end testing. test carving. [incremental] 'Delta mutation [testing] is highly change aware' 'return on investment of test effort' 'In industrial settings, other non-functional criteria are also highly important, including memory consumption, power consumption, code footprint size, scroll performance, image rendering speed/quality, server to client latency, and CPU cycle consumption' 'fitness without requiring overall system rebuild' 'A/B testing surrogate[s]' 'to pay off technical debt' 'Automated software transplantation is an important special case of refactoring.' 'the donor system can thus be used as a test oracle' ... 'software transplantation is fully automatable.' [generative AI] 'tendency to hallucinate renders it relatively unreliable on its own. However'... [SE has ground truth]. [Large Language Model] 'AI techniques that mimic existing [human] coding styles' Codex LLM. 'Replacing execution with prediction' ChatGPT LLM. 'LLMs [like undergraduates] are prone to hallucination' 'use [LLM] AI for performance optimisation' Importance of when/where to use technology. 'it is essential to deploy into the continuous integration system at a time of maximum relevance'. 'Maximum automation is achieved when human decision time is minimised.' 'fuzzing [fuzz testing] technology has achieved widespread deployment' Testability Transformation Instagram Product Foundation, Meta Platforms Inc. Facebook", } @Misc{alshahwan2024assured, author = "Nadia Alshahwan and Mark Harman and Inna Harper and Alexandru Marginean and Shubho Sengupta and Eddy Wang", title = "Assured {LLM}-Based Software Engineering", howpublished = "arXiv", year = "2024", month = "6 " # feb, note = "InteNSE 2024 Keynote", address = "Lisbon, Portugal", keywords = "genetic algorithms, genetic programming, genetic improvement, ANN, LLMSE, SBSE, prompt search space, facebook, automatic test oracle, refactoring, APR, searchable prompting language", eprint = "2402.04380", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2402.04380", size = "6 pages", abstract = "How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal", notes = "https://conf.researchr.org/home/icse-2024/intense-2024 https://intense24.github.io/", } @InProceedings{Alshahwan:2024:FSEcomp, author = "Nadia Alshahwan and Jubin Chheda and Anastasia Finogenova and Beliz Gokkaya and Mark Harman and Inna Harper and Alexandru Marginean and Shubho Sengupta and Eddy Wang", title = "Automated Unit Test Improvement using Large Language Models at Meta", year = "2024", booktitle = "Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering", series = "FSE 2024", pages = "185--196", address = "Porto de Galinhas, Brazil", month = jul # " 15-19", publisher = "ACM", keywords = "TestGen-LLM, Automated Test Generation, Genetic Improvement, LLMs, Large Language Models, ANN, SBSE, Unit Testing", isbn13 = "979-8-4007-0658-5", URL = "https://doi.org/10.1145/3663529.3663839", DOI = "doi:10.1145/3663529.3663839", size = "12 pages", abstract = "This paper describes Meta TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms. In an evaluation on Reels and Stories products for Instagram, 75percent of TestGen-LLMs test cases built correctly, 57percent passed reliably, and 25percent increased coverage. During Meta Instagram and Facebook test-a-thons, it improved 11.5percent of all classes to which it was applied, with 73percent of its recommendations being accepted for production deployment by Meta software engineers. We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement.", notes = "Not GP?", } @InProceedings{DBLP:conf/gecco/AlshammariLHZ09, author = "Riyad Alshammari and Peter Lichodzijewski and Malcolm I. Heywood and A. Nur Zincir-Heywood", title = "Classifying SSH encrypted traffic with minimum packet header features using genetic programming", booktitle = "GECCO-2009 Defense applications of computational intelligence workshop", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "2539--2546", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570358", abstract = "The classification of Encrypted Traffic, namely Secure Shell (SSH), on the fly from network TCP traffic represents a particularly challenging application domain for machine learning. Solutions should ideally be both simple - therefore efficient to deploy - and accurate. Recent advances to team based Genetic Programming provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain and increasing the throughput of solutions. Thus, in this work we have investigated the identification of SSH encrypted traffic based on packet header features without using IP addresses, port numbers and payload data. Evaluation of C4.5 and AdaBoost - representing current best practice - against the Symbiotic Bid-based (SBB) paradigm of team-based Genetic Programming (GP) under data sets common and independent from the training condition indicates that SBB based GP solutions are capable of providing simpler solutions without sacrificing accuracy. ", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Alshammari:2010:cec, author = "Riyad Alshammari and A. Nur Zincir-Heywood", title = "Unveiling Skype encrypted tunnels using GP", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "The classification of Encrypted Traffic, namely Skype, from network traffic represents a particularly challenging problem. Solutions should ideally be both simple -therefore efficient to deploy -and accurate. Recent advances to team-based Genetic Programming provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviours. Thus, in this work we have investigated the identification of Skype encrypted traffic using Symbiotic Bid-Based (SBB) paradigm of team based Genetic Programming (GP) found on flow features without using IP addresses, port numbers and payload data. Evaluation of SBB-GP against C4.5 and AdaBoost -representing current best practice -indicates that SBB-GP solutions are capable of providing simpler solutions in terms number of features used and the complexity of the solution/model without sacrificing accuracy.", DOI = "doi:10.1109/CEC.2010.5586288", notes = "WCCI 2010. Also known as \cite{5586288}", } @InProceedings{Alshammari:2010:CNSM, author = "Riyad Alshammari and A. Nur Zincir-Heywood", title = "An investigation on the identification of {VoIP} traffic: Case study on Gtalk and Skype", booktitle = "2010 International Conference on Network and Service Management (CNSM)", year = "2010", month = "25-29 " # oct, pages = "310--313", abstract = "The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviours, in effect providing further insight into the problem domain. Thus, the objective of this work is to classify VoIP encrypted traffic, where Gtalk and Skype applications are taken as good representatives. To this end, three different machine learning based approaches, namely, C4.5, AdaBoost and Genetic Programming (GP), are evaluated under data sets common and independent from the training condition. In this case, flow based features are employed without using the IP addresses, source/destination ports and payload information. Results indicate that C4.5 based machine learning approach has the best performance.", keywords = "genetic algorithms, genetic programming, AdaBoost, C4.5, Gtalk, IP address, Skype, VoIP encrypted traffic, machine learning, source/destination port, Internet telephony, learning (artificial intelligence), telecommunication traffic", DOI = "doi:10.1109/CNSM.2010.5691210", notes = "Also known as \cite{5691210}", } @InProceedings{Alshammari:2011:IMLltbtptsaVt, title = "Is Machine Learning losing the battle to produce transportable signatures against VoIP traffic?", author = "Riyad Alshammari and A. Nur Zincir-Heywood", pages = "1542--1549", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, AdaBoost, C5.0, VoIP traffic classification, consecutive sampling, machine learning, naive Bayesian, random sampling, transportable signatures, voice over IP, Bayes methods, Internet telephony, learning (artificial intelligence), telecommunication security, telecommunication traffic", DOI = "doi:10.1109/CEC.2011.5949799", abstract = "Traffic classification becomes more challenging since the traditional techniques such as port numbers or deep packet inspection are ineffective against voice over IP (VoIP) applications, which uses non-standard ports and encryption. Statistical information based on network layer with the use of machine learning (ML) can achieve high classification accuracy and produce transportable signatures. However, the ability of ML to find transportable signatures depends mainly on the training data sets. In this paper, we explore the importance of sampling training data sets for the ML algorithms, specifically Genetic Programming, C5.0, Naive Bayesian and AdaBoost, to find transportable signatures. To this end, we employed two techniques for sampling network training data sets, namely random sampling and consecutive sampling. Results show that random sampling and 90-minute consecutive sampling have the best performance in terms of accuracy using C5.0 and SBB, respectively. In terms of complexity, the size of C5.0 solutions increases as the training size increases, whereas SBB finds simpler solutions.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{Alshammari:2015:JKSUCIS, author = "Riyad Alshammari and A. Nur Zincir-Heywood", title = "Identification of {VoIP} encrypted traffic using a machine learning approach", journal = "Journal of King Saud University - Computer and Information Sciences", volume = "27", number = "1", pages = "77--92", year = "2015", keywords = "genetic algorithms, genetic programming, Machine learning, Encrypted traffic, Robustness, Network signatures", ISSN = "1319-1578", DOI = "doi:10.1016/j.jksuci.2014.03.013", URL = "http://www.sciencedirect.com/science/article/pii/S1319157814000561", abstract = "We investigate the performance of three different machine learning algorithms, namely C5.0, AdaBoost and Genetic programming (GP), to generate robust classifiers for identifying VoIP encrypted traffic. To this end, a novel approach (Alshammari and Zincir-Heywood, 2011) based on machine learning is employed to generate robust signatures for classifying VoIP encrypted traffic. We apply statistical calculation on network flows to extract a feature set without including payload information, and information based on the source and destination of ports number and IP addresses. Our results show that finding and employing the most suitable sampling and machine learning technique can improve the performance of classifying VoIP significantly.", } @Article{AlShammari:2016:Energy, author = "Eiman Tamah Al-Shammari and Afram Keivani and Shahaboddin Shamshirband and Ali Mostafaeipour and Por Lip Yee and Dalibor Petkovic and Sudheer Ch", title = "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm", journal = "Energy", volume = "95", pages = "266--273", year = "2016", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2015.11.079", URL = "http://www.sciencedirect.com/science/article/pii/S0360544215016424", abstract = "District heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.", keywords = "genetic algorithms, genetic programming, District heating systems, Heat load, Estimation, Prediction, Support Vector Machines, Firefly algorithm", } @Article{ALSHARIF:2022:JBE, author = "Rashed Alsharif and Mehrdad Arashpour and Emadaldin Mohammadi Golafshani and M. Reza Hosseini and Victor Chang and Jenny Zhou", title = "Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings", journal = "Journal of Building Engineering", volume = "46", pages = "103846", year = "2022", ISSN = "2352-7102", DOI = "doi:10.1016/j.jobe.2021.103846", URL = "https://www.sciencedirect.com/science/article/pii/S2352710221017046", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Energy simulation, Metabolic rate, Predicted mean vote (PMV), Sustainability", abstract = "The housing sector consumes a significant amount of energy worldwide, which is mainly attributed to operating energy systems for the provision of thermally comfortable indoor environments. Although the literature in this field has focused on investigating critical factors in energy consumption, only a few studies have conducted a quantitative sensitivity analysis for thermal occupant factors (TOF) (i.e., metabolic rate and clothing level). Therefore, this paper introduces a framework for testing the criticality of TOF with a cross-comparison against building-related factors, considering the constraint of occupant thermal comfort. Using a building energy simulation model, the energy consumption of a case study is simulated, and building energy model alternatives are generated. The scope includes TOF and building envelope factors, with an established orthogonal experimental design. A popular branch of machine learning (ML) called linear genetic programming (LGP) is used to analyse the generated data from the experiment. Finally, a sensitivity analysis is conducted using the developed LGP model to determine and rank the criticality of the considered factors. The findings reveal that occupants' metabolic rate and clothing level have relevancy factors of -0.48 and -0.38 respectively, which ranked them 2nd and 3rd against building envelope factors for achieving energy-efficient comfortable houses. This research contributes to the literature by introducing a framework that couples orthogonal experiment design with ML techniques to quantify the criticality of TOF and rank them against building-envelope factors", } @Article{alshayeb:2021:Energies, author = "Suhaib Alshayeb and Aleksandar Stevanovic and B. Brian Park", title = "{Field-Based} Prediction Models for Stop Penalty in Traffic Signal Timing Optimization", journal = "Energies", year = "2021", volume = "14", number = "21", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/14/21/7431", DOI = "doi:10.3390/en14217431", abstract = "Transportation agencies optimise signals to improve safety, mobility, and the environment. One commonly used objective function to optimise signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimise fuel consumption (FC). The critical component of the PI is the stop penalty K, which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is used to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behaviour, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models' quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimising signals to reduce FC.", notes = "also known as \cite{en14217431}", } @InProceedings{Alsheddy:2012:CEC, title = "Off-line Parameter Tuning for Guided Local Search Using Genetic Programming", author = "Abdullah Alsheddy and Michael Kampouridis", pages = "112--116", booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation", year = "2012", editor = "Xiaodong Li", month = "10-15 " # jun, DOI = "doi:10.1109/CEC.2012.6256155", address = "Brisbane, Australia", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Heuristics, metaheuristics and hyper-heuristics", abstract = "Guided Local Search (GLS), which is a simple meta-heuristic with many successful applications, has lambda as the only parameter to tune. There has been no attempt to automatically tune this parameter, resulting in a parameterless GLS. Such a result is a very practical objective to facilitate the use of meta-heuristics for end-users (e.g. practitioners and researchers). In this paper, we propose a novel parameter tuning approach by using Genetic Programming (GP). GP is employed to evolve an optimal formula that GLS can use to dynamically compute lambda as a function of instance-dependent characteristics. Computational experiments on the travelling salesman problem demonstrate the feasibility and effectiveness of this approach, producing parameterless formulae with which the performance of GLS is competitive (if not better) than the standard GLS.", notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Alsina:2015:ieeeSSCI, author = "Emanuel F. Alsina and Nicola Capodieci and Giacomo Cabri and Alberto Regattieri", booktitle = "2015 IEEE Symposium Series on Computational Intelligence", title = "The Influence of the Picking Times of the Components in Time and Space Assembly Line Balancing Problems: An Approach with Evolutionary Algorithms", year = "2015", pages = "1021--1028", abstract = "The balancing of assembly lines is one of the most studied industrial problems, both in academic and practical fields. The workable application of the solutions passes through a reliable simplification of the real-world assembly line systems. Time and space assembly line balancing problems consider a realistic versions of the assembly lines, involving the optimisation of the entire line cycle time, the number of stations to install, and the area of these stations. Components, necessary to complete the assembly tasks, have different picking times depending on the area where they are allocated. The implementation in the real world of a line balanced disregarding the distribution of the tasks which use unwieldy components can result unfeasible. The aim of this paper is to present a method which balances the line in terms of time and space, hence optimises the allocation of the components using an evolutionary approach. In particular, a method which combines the bin packing problem with a genetic algorithm and a genetic programming is presented. The proposed method can be able to find different solutions to the line balancing problem and then evolve they in order to optimise the allocation of the components in certain areas in the workstation.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2015.148", month = dec, notes = "Dept. of Phys., Inf. & Math., Univ. of Modena & Reggio Emilia, Modena, Italy Also known as \cite{7376724}", } @PhdThesis{Alsina:thesis, author = "Emanuel Federico Alsina", title = "Models for the prediction and management of complex systems in industrial and dynamic environments", title_it = "Modelli per la previsione e la gestione di sistemi complessi in ambienti dinamici e industriali", school = "Universita degli studi di Modena e Reggio Emilia", year = "2016", address = "Italy", keywords = "genetic algorithms, genetic programming", URN = "etd-11262015-110057", URL = "https://morethesis.unimore.it/theses/available/etd-11262015-110057/", URL = "https://morethesis.unimore.it/theses/available/etd-11262015-110057/unrestricted/thesis.pdf", size = "137 pages", abstract = "The world in which we live is becoming more and more complex. Modelling the reality means to create simplifications and abstractions of that, in order to figure out what is going on in this modern and complex world in which we live. Nowadays, models have become crucial to make better decisions. Models help us to be clearer thinkers, and to understand how to transform data in useful information. There are too many data out there, models take these data and structure them into information, and then into knowledge. Two main topics are discussed in this work: (1) how to model complex systems, and (2) how to make predictions within complex systems, in industrial and dynamic environments. The purpose of this thesis is to present a series of models developed to support the decision makers in the complexity management. The first topic is addressed presenting some models concerning the balancing of assembly lines, machine degradation in production lines, operation schedule, and the positing of cranes in automated warehousing. In particular, concerning the assembly lines, two bio-inspired models which optimize the global picking time of the components considering their physical allocation are presented. Moreover, the use of a multi-agent model able to simultaneously consider different factors that affect machines in a production line is analysed. This approach takes into account the ageing and the degradation of the machines, the repairs, the replacement, and the preventive maintenance activities. Furthermore, in order to present how to manage the complexity intrinsic into the operations scheduling, a model inspired by the behaviour of an ant colony is showed. Finally, another multi-agent model is showed, which is able to find the optimal dwell point in automated storage retrieval systems exploiting an idea deriving from force-fields. After that, an entire chapter is dedicated to the prediction in complex systems. Prediction in industrial and dynamic environments is a challenge that professionals and academics have to face more and more. Some models able to capture non-linear relationships between temporal events are presented. These models are applied to different fields, from the reliability of mechanical and electrical components, to renewable energy. In the final analysis, models able to predict the users behaviors within online social communities are introduced. In these cases, various machine learning approaches (such as artificial neural networks, logistic regressions, and random trees) are detailed. This thesis want to be an inspiration for those people which have to manage the complexity in industrial and dynamic environments, showing examples and results, in order to explain how to make this world a little more understandable.", notes = "Supervisor: Giacomo Cabri", } @InProceedings{Alsulaiman:2009:ieeeCISDA, author = "Fawaz A. Alsulaiman and Nizar Sakr and Julio J. Valdes and Abdulmotaleb {El Saddik} and Nicolas D. Georganas", title = "Feature selection and classification in genetic programming: Application to haptic-based biometric data", booktitle = "IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009", year = "2009", month = jul, pages = "1--7", keywords = "genetic algorithms, genetic programming, gene expression programming, analytic function, dimensionality reducers, feature selection, haptic dataset, haptic-based biometric data, haptic-based biometrics problem, high-dimensional haptic feature space, perfect classification model, feature extraction, haptic interfaces, pattern classification", DOI = "doi:10.1109/CISDA.2009.5356540", abstract = "In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.", notes = "Also known as \cite{5356540}", } @InProceedings{Alsulaiman:2012:CISDA, author = "Fawaz A. Alsulaiman and Julio J. Valdes and Abdulmotaleb {El Saddik}", booktitle = "Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on", title = "Identity verification based on haptic handwritten signatures: Genetic programming with unbalanced data", year = "2012", DOI = "doi:10.1109/CISDA.2012.6291531", abstract = "In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favourably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.", keywords = "genetic algorithms, genetic programming, handwriting recognition, haptic interfaces, image classification, GP classification, GP classifiers, fitness functions, genetic programming classification, haptic data types, haptic features, haptic-based handwritten signature verification, unbalance dataset problem, user identity verification, visual features, Biological cells, Biometrics, Force, Gene expression, Haptic interfaces, Vectors", notes = "Also known as \cite{6291531}", } @Article{journals/tomccap/AlsulaimanSVE13, author = "Fawaz A. Alsulaiman and Nizar Sakr and Julio J. Valdes and Abdulmotaleb El-Saddik", title = "Identity verification based on handwritten signatures with haptic information using genetic programming", journal = "ACM Transactions on Multimedia Computing, Communications, and Applications", year = "2013", volume = "9", number = "2", pages = "11:1--11:21", articleno = "11", month = may, keywords = "genetic algorithms, genetic programming, Biometrics, Haptics, classification, user verification", acmid = "2457453", publisher = "ACM", ISSN = "1551-6857", bibdate = "2013-06-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tomccap/tomccap9.html#AlsulaimanSVE13", URL = "http://doi.acm.org/http://dx.doi.org/10.1145/2457450.2457453", DOI = "doi:10.1145/2457450.2457453", size = "21 pages", abstract = "In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbours, naive Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favourably with the classical methods and use a much fewer number of attributes (with simple function sets).", notes = "Also known as \cite{Alsulaiman:2013:IVB:2457450.2457453} TOMCCAP", } @InProceedings{Alsulaiman:2013:HAVE, author = "Fawaz A. Alsulaiman and Julio J. Valdes and Abdulmotaleb {El Saddik}", booktitle = "IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE 2013)", title = "Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework", year = "2013", month = oct, pages = "98--102", keywords = "genetic algorithms, genetic programming, handwriting recognition, haptic interfaces, GP framework, evolutionary processes, false rejection rate, haptic based handwritten signatures, identity verification, novel fitness functions, Accuracy, Educational institutions, Evolutionary computation, Gene expression, Haptic interfaces, Programming", DOI = "doi:10.1109/HAVE.2013.6679618", size = "5 pages", abstract = "Fitness functions are the evaluation measures driving evolutionary processes towards solutions. In this paper, three fitness functions are proposed for solving the unbalanced dataset problem in Haptic-based handwritten signatures using genetic programming (GP). The use of these specifically designed fitness functions produced simpler analytical expressions than those obtained with currently available fitness measures, while keeping comparable classification accuracy. The functions introduced in this paper capture explicitly the nature of unbalanced data, exhibit better dimensionality reduction and have better False Rejection Rate.", notes = "Also known as \cite{6679618}", } @PhdThesis{Alsulaiman_Fawaz_Abdulaziz_A_2013_thesis, author = "Fawaz Abdulaziz A. Alsulaiman", title = "Towards a Continuous User Authentication Using Haptic Information", school = "School of Electrical Engineering and Computer Science, University of Ottawa", year = "2013", address = "Canada", keywords = "genetic algorithms, genetic programming, User Authentication, Identity Verification, User Identification, Haptics, Haptic-enabled Interpersonal Communication System", URL = "https://ruor.uottawa.ca/bitstream/10393/23946/3/Alsulaiman_Fawaz_Abdulaziz_A_2013_thesis.pdf", URL = "https://www.bac-lac.gc.ca/eng/services/theses/Pages/item.aspx?idNumber=1033202147", URL = "https://ruor.uottawa.ca/handle/10393/23946", size = "129 pages", abstract = "With the advancement in multimedia systems and the increased interest in haptics to be used in interpersonal communication systems, where users can see, show, hear, tell, touch and be touched, mouse and keyboard are no longer dominant input devices. Touch, speech and vision will soon be the main methods of human computer interaction. Moreover, as interpersonal communication usage increases, the need for securing user authentication grows. In this research, we examine a user's identification and verification based on haptic information. We divide our research into three main steps. The first step is to examine a pre-defined task, namely a handwritten signature with haptic information. The user target in this task is to mimic the legitimate signature in order to be verified. As a second step, we consider the user's identification and verification based on user drawings. The user target is predefined, however there are no restrictions imposed on the order or on the level of details required for the drawing. Lastly, we examine the feasibility and possibility of distinguishing users based on their haptic interaction through an interpersonal communication system. In this third step, there are no restrictions on user movements, however a free movement to touch the remote party is expected. In order to achieve our goal, many classification and feature reduction techniques have been discovered and some new ones were proposed. Moreover, in this work we use evolutionary computing in user verification and identification. Analysis of haptic features and their significance on distinguishing users is hence examined. The results show a use of visual features by Genetic Programming (GP) towards identity verification, with a probability equal to 50percent while the remaining haptic features were used with a probability of approximately 50percent. Moreover, with a handwritten signature application, a verification success rate of 97.93percent with False Acceptance Rate (FAR) of 1.28percent and 11.54percent False Rejection Rate (FRR) is achieved with the use of genetic programming enhanced with the random over sampled data set. In addition, with a totally free user movement in a haptic-enabled interpersonal communication system, an identification success rate of 83.3percent is achieved when random forest classifier is used.", notes = "OCLC number: 1033202147 supervisor: Abdulmotaleb El Saddi", } @Article{Altamiranda:2011:ieeeLAT, author = "J. Altamiranda and J. Aguilar and C. Delamarche", title = "Similarity of Amyloid Protein Motif using an Hybrid Intelligent System", journal = "IEEE Latin America Transactions (Revista IEEE America Latina)", year = "2011", month = sep, volume = "9", number = "5", pages = "700--710", note = "In Spanish", keywords = "genetic algorithms, genetic programming, AMYPdb database, amyloid protein motif, backpropagation artificial neural network, biological problem, hybrid intelligent system, nonhomologous protein family, protein sequence, regular expression, backpropagation, biology computing, neural nets, proteins", DOI = "doi:10.1109/TLA.2011.6030978", ISSN = "1548-0992", size = "11 pages", abstract = "The main objective of this research is to define and develop a comparison method of regular expressions, and apply it to amyloid proteins. In general, the biological problem that we study is concerning the search for similarities between non-homologous protein families, using regular expressions, with the goal of discover and identify specific regions conserved in the protein sequence, and in this way determine that proteins have a common origin. From the computer point of view, the problem consists of comparison of protein motifs expressed using regular expressions. A motif is a small region in a previously characterised protein, with a functional or structural significance in the protein sequence. In this work we proposed a hybrid method of motifs comparison based on the Genetic Programming, to generate the populations derived from every regular expression under comparison, and the Backpropagation Artificial Neural Network, for the comparison between them. The method of motifs comparison is tested using the database AMYPdb, and it allows discover possible similarities between amyloid families.", notes = "Also known as \cite{6030978}", } @InProceedings{Altamiranda:2013:CLEI, author = "Junior Altamiranda and Jose Aguilar and Chistian Delamarche", booktitle = "XXXIX Latin American Computing Conference (CLEI 2013)", title = "Comparison and fusion model in protein motifs", year = "2013", month = "7-11 " # oct, address = "Naiguata", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Bioinformatics, Neural Network, ANN, ACO, Ant Colony Optimization", isbn13 = "978-1-4799-2957-3", DOI = "doi:10.1109/CLEI.2013.6670618", size = "12 pages", abstract = "Motifs are useful in biology to highlight the nucleotides/amino-acids that are involved in structure, function, regulation and evolution, or to infer homology between genes/proteins. PROSITE is a strategy to model protein motifs as Regular Expressions and Position Frequency Matrices. Multiple tools have been proposed to discover biological motifs, but not for the case of the motifs comparison problem, which is NP-Complete due to flexibility and independence at each position. In this paper we present a formal model to compare two protein motifs based on the Genetic Programming to generate the population of sequences derived from every regular expression under comparison and on a Neural Network Backpropagation to calculate a motif similarity score as fitness function. Additionally, we present a fusion formal method for two similar motifs based on the Ant Colony Optimisation technique. The comparison and fusion method was tested using amyloid protein motifs.", notes = "Chistian Delamarche = Christian Delamarche Also known as \cite{6670618}", } @InCollection{kinnear:altenberg, author = "Lee Altenberg", title = "The Evolution of Evolvability in Genetic Programming", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", year = "1994", editor = "Kenneth E. {Kinnear, Jr.}", pages = "47--74", chapter = "3", keywords = "genetic algorithms, genetic programming", URL = "http://dynamics.org/~altenber/PAPERS/EEGP/", URL = "http://dynamics.org/Altenberg/FILES/LeeEEGP.pdf", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap3.pdf", DOI = "doi:10.7551/mitpress/1108.003.0009", size = "29 pages", abstract = "The notion of ``evolvability'' --- the ability of a population to produce variants fitter than any yet existing --- is developed as it applies to genetic algorithms. A theoretical analysis of the dynamics of genetic programming predicts the existence of a novel, emergent selection phenomenon: the evolution of evolvability. This is produced by the proliferation, within programs, of blocks of code that have a higher chance of increasing fitness when added to programs. Selection can then come to mold the {\em variational} aspects of the way evolved programs are represented. A model of code proliferation within programs is analyzed to illustrate this effect. The mathematical and conceptual framework includes: the definition of evolvability as a measure of performance for genetic algorithms; application of Price's {\em Covariance and Selection Theorem} to show how the fitness function, representation, and genetic operators must interact to produce evolvability --- namely, that genetic operators produce offspring with fitnesses specifically correlated with their parent's fitnesses; how blocks of code emerge as a new level of replicator, proliferating as a function of their ``constructional fitness'', which is distinct from their schema fitness; and how programs may change from innovative code to conservative code as the populations mature. Several new selection techniques and genetic operators are proposed in order to give better control over the evolution of evolvability and improved evolutionary performance. Copyright 1996 Lee Altenberg", notes = " Price's Covariance and Selection Theorem 1970 Nature 227 pages 520-521 Fisher's Theorem 1930 'The Genetical Theory of Natural Selection, Clarendon Press, Oxford, UK pages 30-37' Generally better theory for GP -> additional fitness (of blocks) Also known as \cite{Altenberg:1994EEGP} Part of \cite{kinnear:book}", } @InProceedings{Altenberg:1994EBR, author = "Lee Altenberg", year = "1994", pages = "182--187", title = "Evolving better representations through selective genome growth", booktitle = "Proceedings of the 1st IEEE Conference on Evolutionary Computation", publisher = "IEEE", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher_address = "Piscataway, NJ, USA", volume = "1", keywords = "genetic algorithms, genetic programming", URL = "http://dynamics.org/~altenber/PAPERS/EBR/", URL = "http://dynamics.org/Altenberg/FILES/LeeEBR.pdf", abstract = "The choice of how to represent the search space for a genetic algorithm (GA) is critical to the GA's performance. Representations are usually engineered by hand and fixed for the duration of the GA run. Here a new method is described in which the degrees of freedom of the representation --- i.e. the genes -- are increased incrementally. The phenotypic effects of the new genes are randomly drawn from a space of different functional effects. Only those genes that initially increase fitness are kept. The genotype-phenotype map that results from this selection during the constructional of the genome allows better adaptation. This effect is illustrated with the NK landscape model. The resulting genotype-phenotype maps are much less epistatic than generic maps would be. They have extremely low values of ``K'' --- the number of fitness components affected by each gene. Moreover, these maps are exquisitely tuned to the specifics of the random fitness functions, and achieve fitnesses many standard deviations above generic NK landscapes with the same \gp\ maps. The evolved maps create adaptive landscapes that are much smoother than generic NK landscapes ever are. Thus a caveat should be made when making arguments about the applicability of generic properties of complex systems to evolved systems. This method may help to solve the problem of choice of representations in genetic algorithms. Copyright 1996 Lee Altenberg", notes = " ", } @InProceedings{Altenberg:1994EPIGP, author = "Lee Altenberg", year = "1994", title = "Emergent phenomena in genetic programming", booktitle = "Evolutionary Programming --- Proceedings of the Third Annual Conference", editor = "Anthony V. Sebald and Lawrence J. Fogel", publisher = "World Scientific Publishing", pages = "233--241", address = "San Diego, CA, USA", month = "24-26 " # feb, keywords = "genetic algorithms, genetic programming", ISBN = "981-02-1810-9", URL = "http://dynamics.org/~altenber/PAPERS/EPIGP/", URL = "http://dynamics.org/Altenberg/FILES/LeeEPIGP.pdf", URL = "http://dynamics.org/~altenber/FTP/LeeEPIGP.ps", URL = "http://citeseer.ist.psu.edu/398393.html", abstract = "Evolutionary computation systems exhibit various emergent phenomena, primary of which is adaptation. In genetic programming, because of the indeterminate nature of the representation, the evolution of both recombination distributions and representations can emerge from the population dynamics. A review of ideas on these phenomena is presented, including theory on the evolution of evolvability through differential proliferation of subexpressions within programs. An analysis is given of a model of genetic programming dynamics that is supportive of the ``Soft Brood Selection'' conjecture, which was proposed as a means to counteract the emergence of highly conservative code, and instead favor highly evolvable code. Copyright 1996 Lee Altenberg", notes = " EP-94 http://www.wspc.com.sg/books/compsci/2401.html broken sep 2019 http://www.natural-selection.com/eps/EP94.html", } @InProceedings{Altenberg:1995STPT, author = "Lee Altenberg", year = "1994", title = "The {Schema} {Theorem} and {Price}'s {Theorem}", booktitle = "Foundations of Genetic Algorithms 3", editor = "L. Darrell Whitley and Michael D. Vose", publisher = "Morgan Kaufmann", publisher_address = "San Francisco, CA, USA", address = "Estes Park, Colorado, USA", pages = "23--49", month = "31 " # jul # "--2 " # aug, organisation = "International Society for Genetic Algorithms", note = "Published 1995", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-356-5", URL = "http://dynamics.org/~altenber/PAPERS/STPT/", URL = "http://dynamics.org/Altenberg/FILES/LeeSTPT.pdf", DOI = "doi:10.1016/B978-1-55860-356-1.50006-6", abstract = "Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a ``missing'' schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of ``adaptive landscape'' analysis is examined and counterexamples offered to the commonly used correlation statistic. Instead, an alternative statistic---the transmission function in the fitness domain--- is proposed as the optimal statistic for estimating GA performance from limited samples. Copyright 1996 Lee Altenberg", notes = "FOGA-3 Deals with GAs as a whole, not specifically GP.", } @InCollection{Altenberg:1995GGEGPM, author = "Lee Altenberg", year = "1992", title = "Genome growth and the evolution of the genotype-phenotype map", booktitle = "Evolution as a Computational Process", editor = "Wolfgang Banzhaf and Frank H. Eeckman", publisher = "Springer-Verlag", address = "Monterey, California, USA", publisher_address = "Berlin, Germany", pages = "205--259", volume = "899", series = "Lecture Notes in Computer Science", month = jul, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-49176-7", URL = "http://dynamics.org/~altenber/PAPERS/GGEGPM/", URL = "http://dynamics.org/Altenberg/FILES/LeeGGEGPM.pdf", URL = "https://rdcu.be/cUkY7", DOI = "doi:10.1007/3-540-59046-3_11", size = "55 pages", abstract = "The evolution of new genes is distinct from evolution through allelic substitution in that new genes bring with them new degrees of freedom for genetic variability. Selection in the evolution of new genes can therefore act to sculpt the dimensions of variability in the genome. This ``constructional'' selection effect is an evolutionary mechanism, in addition to genetic modification, that can affect the variational properties of the genome and its evolvability. One consequence is a form of genic selection: genes with large potential for generating new useful genes when duplicated ought to proliferate in the genome, rendering it ever more capable of generating adaptive variants. A second consequence is that alleles of new genes whose creation produced a selective advantage may be more likely to also produce a selective advantage, provided that gene creation and allelic variation have correlated phenotypic effects. A fitness distribution model is analyzed which demonstrates these two effects quantitatively. These are effects that select on the nature of the genotype-phenotype map. New genes that perturb numerous functions under stabilizing selection, i.e. with high pleiotropy, are unlikely to be advantageous. Therefore, genes coming into the genome ought to exhibit low pleiotropy during their creation. If subsequent offspring genes also have low pleiotropy, then genic selection can occur. If subsequent allelic variation also has low pleiotropy, then that too should have a higher chance of not being deleterious. The effects on pleiotropy are illustrated with two model genotype-phenotype maps: Wagner's linear quantitative-genetic model with Gaussian selection, and Kauffman's ``NK'' adaptive landscape model. Constructional selection is compared with other processes and ideas about the evolution of constraints, evolvability, and the genotype-phenotype map. Empirical phenomena such as dissociability in development, morphological integration, and exon shuffling are discussed in the context of this evolutionary process. Copyright 1996 Lee Altenberg", notes = " from the preface: Lee Altenberg considers the genotype-phenotype mapping and demonstrates the advantage of his constructional selection in the process of adaptation. Specifically, he considers the variational aspect of the representation problem and pleiotropy Published 1995", } @Unpublished{Altenberg:and:Feldman:1995SGTEMG2, author = "Lee Altenberg and Marcus W. Feldman", year = "1995", title = "Selection, generalized transmission, and the evolution of modifier genes. {II}. {M}odifier polymorphisms", note = "In preparation", URL = "ftp://ftp.mhpcc.edu/pub/incoming/altenberg/LeeSGTEMG2MP.ps.Z", notes = " ", } @InCollection{Altenberg:2004:MESLLQ, title = "Modularity in Evolution: Some Low-Level Questions", author = "Lee Altenberg", booktitle = "Modularity: Understanding the Development and Evolution of Complex Natural Systems", editor = "Diego Rasskin-Gutman and Werner Callebaut", publisher = "MIT Press", address = "Cambridge, MA, USA", year = "2005", chapter = "5", pages = "99--128", month = jun, keywords = "genetic algorithms, genetic programming", ISBN = "0-262-03326-7", URL = "http://dynamics.org/Altenberg/FILES/LeeMESLLQ.pdf", abstract = "Intuitive notions about the advantages of modularity for evolvability run into the problem of how we parse the organism into traits. In order to resolve the question of multiplicity, there needs to be a way to get the human observer out of the way, and define modularity in terms of physical processes. I will offer two candidate ideas towards this resolution: the dimensionality of phenotypic variation, and the causal screening off of phenotypic variables by other phenotypic variables. With this framework, the evolutionary advantages that have been attributed to modularity do not derive from modularity per se. Rather, they require that there be an 'alignment' between the spaces of phenotypic variation, and the selection gradients that are available to the organism. Modularity may facilitate such alignment, but it is not sufficient; the appropriate phenotype-fitness map in conjunction with the genotype-phenotype map is also necessary for evolvability. Conclusion I have endeavoured in this essay to delve into some of the low-level conceptual issues associated with the idea of modularity in the genotype-phenotype map. My main proposal is that the evolutionary advantages that have been attributed to modularity do not derive from modularity per se. Rather, they require that there be an 'alignment' between the spaces of phenotypic variation, and the selection gradients that are available to the organism. Modularity in the genotype-phenotype map may make such an alignment more readily attained, but it is not sufficient; the appropriate phenotype-fitness map in conjunction with the genotype-phenotype map is also necessary for evolvability.", notes = "Quantitative mutational effects under the 'House of Cards' vs. ``random-walk'' assumptions. http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10484&mode=toc", size = "32 pages", } @InCollection{Altenberg:2004:OPSAED, title = "Open Problems in the Spectral Analysis of Evolutionary Dynamics", author = "Lee Altenberg", booktitle = "Frontiers of Evolutionary Computation", editor = "Anil Menon", series = "Genetic Algorithms And Evolutionary Computation Series", volume = "11", chapter = "4", publisher = "Kluwer Academic Publishers", address = "Boston, MA, USA", year = "2004", pages = "73--102", keywords = "genetic algorithms, genetic programming", ISBN = "1-4020-7524-3", URL = "http://dynamics.org/Altenberg/FILES/LeeOPSAED.pdf", DOI = "doi:10.1007/1-4020-7782-3_4", abstract = "For broad classes of selection and genetic operators, the dynamics of evolution can be completely characterised by the spectra of the operators that define the dynamics, in both infinite and finite populations. These classes include generalised mutation, frequency-independent selection, uniparental inheritance. Several open questions exist regarding these spectra: 1. For a given fitness function, what genetic operators and operator intensities are optimal for finding the fittest genotype? The concept of rapid first hitting time, an analog of Sinclair's rapidly mixing Markov chains, is examined. 2. What is the relationship between the spectra of deterministic infinite population models, and the spectra of the Markov processes derived from them in the case of finite populations? 3. Karlin proved a fundamental relationship between selection, rates of transformation under genetic operators, and the consequent asymptotic mean fitness of the population. Developed to analyse the stability of polymorphisms in subdivided populations, the theorem has been applied to unify the reduction principle for self-adaptation, and has other applications as well. Many other problems could be solved if it were generalised to account for the interaction of different genetic operators. Can Karlin's theorem on operator intensity be extended to account for mixed genetic operators?", notes = "Revised 2010", size = "26 pages", } @Article{altenberg:2004:ESSFSA, author = "Lee Altenberg", year = "2005", title = "Evolvability Suppression to Stabilize Far-Sighted Adaptations", journal = "Artificial Life", volume = "11", number = "3", pages = "427--443", month = "Fall", keywords = "genetic algorithms", ISSN = "1064-5462", DOI = "doi:10.1162/106454605774270633", size = "18 pages", abstract = "The opportunistic character of adaptation through natural selection can lead to `evolutionary pathologies'---situations in which traits evolve that promote the extinction of the population. Such pathologies include imprudent predation and other forms of habitat over-exploitation or the `tragedy of the commons', adaptation to temporally unreliable resources, cheating and other antisocial behaviour, infectious pathogen carrier states, parthenogenesis, and cancer, an intra-organismal evolutionary pathology. It is known that hierarchical population dynamics can protect a population from invasion by pathological genes. Can it also alter the genotype so as to prevent the generation of such genes in the first place, i.e. suppress the evolvability of evolutionary pathologies? A model is constructed in which one locus controls the expression of the pathological trait, and a series of modifier loci exist which can prevent the expression of this trait. It is found that multiple `evolvability checkpoint' genes can evolve to prevent the generation of variants that cause evolutionary pathologies. The consequences of this finding are discussed.", } @Article{Altenberg:2014:GPEM, author = "Lee Altenberg", title = "Mathematics awaits: commentary on ''Genetic Programming and Emergence'' by Wolfgang Banzhaf", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "87--89", month = mar, keywords = "genetic algorithms, genetic programming, Evolvability, Robustness, Subtree exchange, Mathematics, Matrix theory, Lagrange distribution", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9198-5", size = "3 pages", abstract = "Banzhaf provides a portal to the subject of emergence, noting contentious concepts while not getting sucked into fruitless debate. Banzhaf refutes arguments against downward causation much as Samuel Johnson kicks a stone to refute Berkeley by pointing to concrete examples in genetic programming, such as the growth of repetitive patterns within programs. Repetitive patterns are theoretically predicted to emerge from the evolution of evolvability and robustness under subtree exchange. Selection and genetic operators are co-equal creators of these emergent phenomena. GP systems entirely formal, and thus their emergent phenomena are essentially mathematical. The emergence of Lagrangian distributions for tree shapes under subtree exchange, for example, gives a glimpse of the possibilities for mathematical understanding of emergence in GP. The mathematics underlying emergence in genetic programming should be pursued with vigour.", notes = "\cite{Banzhaf:2014:GPEM}", } @Article{Altenberg:2014:GPEMb, author = "Lee Altenberg", title = "Evolvability and robustness in artificial evolving systems: three perturbations", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "3", pages = "275--280", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9223-3", size = "6 pages", } @InCollection{Altenberg:2016:EC, author = "Lee Altenberg", year = "2016", title = "Evolutionary Computation", booktitle = "The Encyclopedia of Evolutionary Biology", editor = "Richard M. Kliman", publisher = "Academic Press", volume = "2", pages = "40--47", address = "Oxford, UK", keywords = "genetic algorithms, genetic programming, Crossover, Encoding, Evolutionary algorithm, Evolvability, Genetic algorithm, Genetic operator, No free lunch theorems, Objective function, Optimization, Representation, Search space, Selection operator, Simulated annealing", isbn13 = "978-0-12-800426-5", URL = "https://www.sciencedirect.com/science/article/pii/B9780128000496003073", DOI = "doi:10.1016/B978-0-12-800049-6.00307-3", abstract = "Evolutionary computation is a method of solving engineering problems using algorithms that mimic Darwinian natural selection and Mendelian genetics, applied especially to optimization problems that are difficult to solve from first principles. Earliest beginnings were in the 1950s, and by the mid-1990s it had developed as an academic field with its own journals, conferences, and faculty. Several phenomena discovered in evolutionary biology were also discovered in parallel in evolutionary computation, including the evolvability problem, genetic modification, constructive neutral evolution, and genetic robustness. The related field of artificial life focuses on computational systems in which replication, natural selection, and ecological interactions are all emergent.", } @Article{Altenberg:2017:GPEM, author = "Lee Altenberg", title = "Probing the axioms of evolutionary algorithm design: Commentary on ``On the mapping of genotype to phenotype in evolutionary algorithms'' by {Peter A. Whigham, Grant Dick, and James Maclaurin}", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "3", pages = "363--367", month = sep, note = "Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-017-9290-3", size = "5 pages", abstract = "Properties such as continuity, locality, and modularity may seem necessary when designing representations and variation operators for evolutionary algorithms, but a closer look at what happens when evolutionary algorithms perform well reveals counterexamples to such schemes. Moreover, these variational properties can themselves evolve in sufficiently complex open-ended systems. These properties of evolutionary algorithms remain very much open questions.", notes = "Introduction in \cite{Spector:2017:GPEM} An author's reply to this comment is available at http://dx.doi.org/10.1007/s10710-017-9289-9 \cite{Whigham:2017:GPEM2}. This comment refers to the article available at: http://dx.doi.org/10.1007/s10710-017-9288-x \cite{Whigham:2017:GPEM}.", } @Article{Althoefer:2010:ICGA, author = "Ingo Althoefer", title = "Automatic Generation and Evaluation of Recombination Games. Doctoral Dissertation by Cameron Browne, Review", journal = "ICGA Journal", year = "2010", volume = "33", number = "4", keywords = "genetic algorithms, genetic programming", URL = "https://chessprogramming.wikispaces.com/ICGA+Journal", notes = "Review of \cite{CameronBrowne:thesis}", } @Article{ALTHOEY:2023:cscm, author = "Fadi Althoey and Muhammad Naveed Akhter and Zohaib Sattar Nagra and Hamad Hassan Awan and Fayez Alanazi and Mohsin Ali Khan and Muhammad Faisal Javed and Sayed M. Eldin and Yasin Onuralp Ozkilic", title = "Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study", journal = "Case Studies in Construction Materials", volume = "18", pages = "e01774", year = "2023", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2022.e01774", URL = "https://www.sciencedirect.com/science/article/pii/S2214509522009068", keywords = "genetic algorithms, genetic programming, Marshall Mix Parameter, Deep Learning, Prediction models, Asphalt, Bio-Inspired models", abstract = "This research study uses four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree Bagging (DT-Bagging) for the development of new and advanced models for prediction of Marshall Stability (MS), and Marshall Flow (MF) of asphalt mixes. A comprehensive and detailed database of 343 data points was established for both MS and MF. The predicting variables were chosen among the four most influential, and easy-to-determine parameters. The models were trained, tested, validated, and the outcomes of the newly developed models were compared with actual outcomes. The root squared error (RSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), regression coefficient (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed that in the case of MS, the rising order of input significance was bulk specific gravity of compacted aggregate, Gmb (38.56 percent) > Percentage of Aggregates, Ps (19.84 percent) > Bulk Specific Gravity of Aggregate, Gsb (19.43 percent) > maximum specific gravity paving mix, Gmm (7.62 percent), while in case of MF the order followed was: Ps (36.93 percent) > Gsb (14.11 percent) > Gmb (10.85 percent) > Gmm (10.19 percent). The outcomes of parametric analysis (PA) consistency of results in relation to previous research findings. The DT-Bagging model outperformed all other models with values of 0.971 and 0.980 (R), 16.88 and 0.24 (MAE), 28.27 and 0.36 (RMSE), 0.069 and 0.041 (RSE), 0.020 and 0.032 (RRMSE), 0.010 and 0.016 (PI), 0.931 and 0.959 (NSE), for MS and MF, respectively. The results of the comparison analysis showed that ANN, ANFIS, MEP, and DT-Bagging are all effective and reliable approaches for the estimation of MS and MF. The MEP-derived mathematical expressions represent the novelty of MEP and are relatively simple and reliable. Roverall values for MS and MF were in the order of DT-Bagging >MEP >ANFIS >ANN with all values exceeding the permitted range of 0.80 for both MS and MF. Hence, all the modeling approaches showed higher performance, possessed high generalization and predication capabilities, and assess the relative significance of input parameters in the prediction of MS and MF. Hence, the findings of this research study would assist in the safer, faster, and sustainable prediction of MS and MF, from the standpoint of resources and time required to perform the Marshall tests", } @Article{Altomare:2013:JoH, author = "C. Altomare and X. Gironella and D. Laucelli", title = "Evolutionary data-modelling of an innovative low reflective vertical quay", journal = "Journal of Hydroinformatics", year = "2013", volume = "15", number = "3", pages = "763--779", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, data-mining, evolutionary polynomial regression, low reflective vertical quay, wave reflection", URL = "https://iwaponline.com/jh/article-pdf/15/3/763/387059/763.pdf", DOI = "doi:10.2166/hydro.2012.219", size = "17 pages", abstract = "Vertical walls are commonly used as berthing structures. However, conventional vertical quays may have serious technical and environmental problems, as they reflect almost all the energy of the incident waves, thus affecting operational conditions and structural strength. These drawbacks can be overcome by the use of low reflective structures, but for some instances no theoretical equations exist to determine the relationship between the reflection coefficient and parameters that affect the structural response. Therefore, this study tries to fill this gap by examining the wave reflection of an absorbing gravity wall by means of evolutionary polynomial regression, a hybrid evolutionary modelling paradigm that combines the best features of conventional numerical regression and genetic programming. The method implements a multi-modelling approach in which a multi-objective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions and fitting to data. A database of physical laboratory observations is used to predict the reflection as a function of a set of variables that characterize wave conditions and structure features. The proposed modelling paradigm proved to be a useful tool for data analysis and is able to find feasible explicit models featured by an appreciable generalization performance.", notes = "This content is only available as a PDF.", } @Article{altomare:2020:JMSE, author = "Corrado Altomare and Daniele B. Laucelli and Hajime Mase and Xavi Gironella", title = "Determination of {Semi-Empirical} Models for Mean Wave Overtopping Using an Evolutionary Polynomial Paradigm", journal = "Journal of Marine Science and Engineering", year = "2020", volume = "8", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/8/8/570", DOI = "doi:10.3390/jmse8080570", abstract = "The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. This technique is here employed to dig into the relationship between the mean discharge and main hydraulic and structural parameters that characterise the problem under study. The parameters are chosen based on the existing and most used semi-empirical formulas for wave overtopping assessment. Besides the structural freeboard or local wave height, the unified models highlight the importance of local water depth and wave period in combination with foreshore slope and dike slope on the overtopping phenomena, which are combined in a unique parameter that is defined either as equivalent or imaginary slope. The obtained models aim to represent a trade-off between accuracy and parsimony. The final formula is simple but can be employed for a preliminary assessment of overtopping rates, covering the full range of dike slopes, from mild to vertical walls, and of water depths from the shoreline to deep water, including structures with emergent toes.", notes = "also known as \cite{jmse8080570}", } @InProceedings{Aluko:2014:CIFEr, author = "Babatunde Aluko and Dafni Smonou and Michael Kampouridis and Edward Tsang", booktitle = "IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr 2104)", title = "Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm", year = "2014", month = "27-28 " # mar, pages = "333--340", size = "8 pages", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIFEr.2014.6924092", abstract = "Hyper-heuristics have successfully been applied to a vast number of search and optimisation problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analysed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.", notes = "Also known as \cite{6924092}", } @Article{alvarado-iniesta:JoIM, author = "Alejandro Alvarado-Iniesta and Luis Gonzalo Guillen-Anaya and Luis Alberto Rodriguez-Picon and Raul Neco-Caberta", title = "Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach", journal = "Journal of Intelligent Manufacturing", year = "2020", volume = "31", pages = "19--32", month = jan, keywords = "genetic algorithms, genetic programming, Structural optimization, Multi-objective optimization, Finite element analysis, Decision making", URL = "http://link.springer.com/article/10.1007/s10845-018-1432-9", DOI = "doi:10.1007/s10845-018-1432-9", abstract = "the optimization of an engine mount design from a multi-objective. Our methodology is divided into three phases: phase one focuses on data collection through computer simulations. The objectives considered during the analyses are: total mass, first natural frequency and maximum von Mises stress. In phase two, a surrogate model by means of genetic programming is generated for each one of the objectives. Moreover, a local search procedure is incorporated into the overall genetic programming algorithm for improving its performance. Finally, in phase three, instead of steering the search to finding the approximate Pareto front, a local exploration approach based on a change in the weight space is used to lead a search into user defined directions turning the decision making more intuitive.", } @Article{alvarado-iniesta:2017:IJAMT, author = "Alejandro Alvarado-Iniesta and Diego A. Tlapa-Mendoza and Jorge Limon-Romero and Luis C. Mendez-Gonzalez", title = "Multi-objective optimization of an aluminum torch brazing process by means of genetic programming and {R-NSGA-II}", journal = "The International Journal of Advanced Manufacturing Technology", year = "2017", volume = "91", number = "9 - 12", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s00170-017-0102-y", DOI = "doi:10.1007/s00170-017-0102-y", } @Article{Alvarez:2007:JMS, author = "A. Alvarez and Alejandro Orfila and G. Basterretxea and J. Tintore and G. Vizoso and A. Fornes", title = "Forecasting front displacements with a satellite based ocean forecasting (SOFT) system", journal = "Journal of Marine Systems", year = "2007", volume = "65", number = "1-4", pages = "299--313", month = mar, note = "Marine Environmental Monitoring and Prediction - Selected papers from the 36th International Liege Colloquium on Ocean Dynamics", keywords = "genetic algorithms, genetic programming, Satellite data, Ocean prediction, Front evolution", DOI = "doi:10.1016/j.jmarsys.2005.11.017", abstract = "Relatively long term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems predicting the sea surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly focused on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatiotemporal variability is more complex and propagating structures are frequently present. In this case, traditional SOFT systems based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction systems. Instead, SOFT systems based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases. In this work we study and compare the performance of an EOF and CEOF based SOFT systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The SOFT system was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from both SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.", } @InCollection{alvarez:2003:SVMASTI, author = "Gabriel Alvarez", title = "Standard Versus Micro-Genetic Algorithms for Seismic Trace Inversion", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2003:gagp}", } @InProceedings{Alvarez:2016:GECCO, author = "Isidro M. Alvarez and Will N. Browne and Mengjie Zhang", title = "Human-inspired Scaling in Learning Classifier Systems: Case Study on the n-bit Multiplexer Problem Set", booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "429--436", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4206-3", DOI = "doi:10.1145/2908812.2908813", abstract = "Learning classifier systems (LCSs) originated from artificial cognitive systems research, but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge in order to solve more difficult problems in the same or a related domain. The past work showed that the reuse of knowledge through the adoption of code fragments, GP-like sub-trees, into the XCS learning classifier system framework could provide advances in scaling. However, unless the pattern underlying the complete domain can be described by the selected LCS representation of the problem, a limit of scaling will eventually be reached. This is due to LCSs divide and conquer approach rule-based solutions, which entails an increasing number of rules (subclauses) to describe a problem as it scales. Inspired by human problem solving abilities, the novel work in this paper seeks to reuse learned knowledge and learned functionality to scale to complex problems by transferring them from simpler problems. Progress is demonstrated on the benchmark Multiplexer (Mux) domain, albeit the developed approach is applicable to other scalable domains. The fundamental axioms necessary for learning are proposed. The methods for transfer learning in LCSs are developed. Also, learning is recast as a decomposition into a series of sub-problems. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, it is possible to learn a general solution to any n-bit Mux problem for the first time. This is verified by tests on the 264, 521 and 1034 bit Mux problems.", notes = "GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)", } @InProceedings{alvarez:1998:, author = "Luis F. Alvarez and Vassili V. Toropov", title = "Application of Genetic Programming to the Choice of a Structure of Global Approximations", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "1", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "GP-98LB", } @InProceedings{oai:CiteSeerPSU:512359, author = "Luis F. Alvarez and Vassili V. Toropov and David C. Hughes and Ashraf F. Ashour", title = "Approximation model building using genetic programming methodology: applications", booktitle = "Second ISSMO/AIAA Internet Conference on Approximations and Fast Reanalysis in Engineering Optimization", year = "2000", editor = "Thouraya Baranger and Fred van Keulen", month = "25 " # may # "-2 " # jun, keywords = "genetic algorithms, genetic programming", URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/Fred4.pdf", broken = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/fred.html", URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/FRED4.PS", URL = "http://citeseer.ist.psu.edu/512359.html", citeseer-isreferencedby = "oai:CiteSeerPSU:81525", citeseer-references = "oai:CiteSeerPSU:60878", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:512359", rights = "unrestricted", abstract = "Genetic Programming methodology is used for the creation of approximation functions obtained by the response surface methodology. Two important aspects of the problems are addressed: the choice of the plan of experiment and the model tuning using the least-squares response surface fitting. Several examples show the applications of the technique to problems where the values of response functions are obtained either by numerical simulation or laboratory experimentation.", notes = "Multicriteria Optimization of the Manufacturing Process for Roman Cement", } @PhdThesis{Alvarez:thesis, author = "L. F. Alvarez", title = "Design Optimization based on Genetic Programming", school = "Department of Civil and Environmental Engineering, University of Bradford", year = "2000", address = "UK", keywords = "genetic algorithms, genetic programming, Design Optimization, Response Surface Methodology", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/abstract.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/contents.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter1.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter2.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter3.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter4.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter5.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter7.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/references.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/appendixA.pdf", URL = "http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/appendixB.pdf", size = "v + 132 pages", abstract = "This thesis addresses two problems arising in many real-life design optimization applications: the high computational cost of function evaluations and the presence of numerical noise in the function values. The response surface methodology is used to construct approximations of the original model. A major difficulty in building highly accurate response surfaces is the selection of the structure of an approximation function. A methodology has been developed for the approximation model building using genetic programming. It is implemented in a computer code introducing two new features: the use of design sensitivity information when available, and the allocation and evaluation of tuning parameters in separation from the evolutionary process. A combination of a genetic algorithm and a gradient-based algorithm is used for tuning of the approximation functions. The problem of the choice of a design of experiments in the response surface methodology has been reviewed and a space-filling plan adopted. The developed methodology and software have been applied to design optimization problems with numerically simulated and experimental responses, demonstrating their considerable potential. The applications cover the approximation of a response function obtained by a finite element model for the detection of damage in steel frames, the creation of an empirical model for the prediction of the shear strength in concrete deep beams and a multicriteria optimization of the process of calcination of Roman cement.", notes = "Approximation model building for design optimization using the response surface methodology and genetic programming. Luis Francisco Alvarez Barrioluengo Supervisor V.V. Toropov", } @Article{Alvarez-Diaz:2003:ael, author = "Marcos Alvarez-Diaz and Alberto Alvarez", title = "Forecasting exchange rates using genetic algorithms", journal = "Applied Economics Letters", year = "2003", volume = "10", number = "6", pages = "319--322", month = apr, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/13504850210158250", abstract = "A novel approach is employed to investigate the predictability of weekly data on the euro/dollar, British pound/dollar, Deutsch mark/dollar, Japanese yen/dollar, French franc/dollar and Canadian dollar/dollar exchange rates. A functional search procedure based on the Darwinian theories of natural evolution and survival, called genetic algorithms (hereinafter GA), was used to find an analytical function that best approximates the time variability of the studied exchange rates. In all cases, the mathematical models found by the GA predict slightly better than the random walk model. The models are heavily dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small. In consequence, the results agree with previous works establishing explicitly that nonlinear nature of exchange rates cannot be exploited to substantially improve forecasting.", } @Article{Alvarez-Diaz:2005:EE, author = "Marcos Alvarez-Diaz and Alberto Alvarez", title = "Genetic multi-model composite forecast for non-linear prediction of exchange rates", journal = "Empirical Economics", year = "2005", volume = "30", number = "3", pages = "643--663", month = oct, keywords = "genetic algorithms, genetic programming, Composite-forecast or data-fusion, neural networks, ANN, exchange-rate forecasting", ISSN = "0377-7332", DOI = "doi:10.1007/s00181-005-0249-5", size = "21 pages", abstract = "The existence of non-linear deterministic structures in the dynamics of exchange rates has already been amply demonstrated. In this paper, we attempt to exploit these non-linear structures employing forecasting techniques, such as Genetic Programming and Neural Networks, in the specific case of the Yen/US$ and Pound Sterling/US$ exchange rates. Forecasts obtained from genetic programming and neural networks are then genetically fused to verify whether synergy provides an improvement in the predictions. Our analysis considers both point predictions and the anticipating of either depreciations or appreciations.", } @Article{Alvarez-Diaz:2006:jbe, author = "Marcos Alvarez-Diaz and Marcos Dominquez-Torreiro", title = "Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery", journal = "Journal of Bioeconomics", year = "2006", volume = "8", number = "1", pages = "55--65", month = apr, keywords = "genetic algorithms, genetic programming, bioeconomic modeling, linear and non-linear forecasting", ISSN = "1387-6996", DOI = "doi:10.1007/s10818-005-0494-x", abstract = "The Neo-classical approach to fisheries management is based on designing and applying bioeconomic models. Traditionally, the basic bioeconomic models have used pre-established non-linear functional forms (logistic, Cobb-Douglas) in order to try to reflect the dynamics of the renewable resources under study. This assumption might cause misspecification problems and, in consequence, a loss of predictive ability. In this work we intend to verify if there is a bias motivated by employing the said non-linear parametric perspective. For this purpose, we employ a novel non-linear and non-parametric prediction method, called Genetic Algorithms, and we compare its results with those obtained from the traditional methods.", notes = " p 64 {"}Unlike a uni-variant analysis, DARWIN now allows us to look for functional relationships between two or more time-series.{"}", } @PhdThesis{Marcos_Alvarez-Diaz:thesis, author = "Marcos Alvarez-Diaz", title = "Exchange rates forecasting using nonparametric methods", school = "Columbia University", year = "2006", address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-542-91527-7", URL = "http://search.proquest.com/docview/305345652", size = "105 pages", abstract = "The existence of non-linear deterministic structures in the dynamics of exchange rates has already been amply demonstrated in the literature. With my research, I try to explain if we can exploit these non-linear structures in order to improve our predictive ability and, secondly, if we can use these predictions to generate profitable strategies in the Foreign Exchange Market. To this purpose, I employ different nonparametric forecasting methods such as Nearest Neighbours, Genetic Programming, Artificial Neural Networks, Data-Fusion or an Evolutionary Neural Network. My analysis will be centre on the specific case of the Yen/US$ and Pound Sterling/US$ exchange rates and it considers both point predictions and the anticipating of either depreciations or appreciations. My results reveal a slight forecasting ability for one-period-ahead which is lost when more periods ahead are considered, and my trading strategy obtains above-normal profits. However, when transaction costs are incorporated, the profits practically disappear or become negative", notes = "UMI Microform 3237194 ProQuest Dissertations Publishing, 2006. 3237194", } @Article{AlvarezDiaz2008161, author = "Marcos Alvarez-Diaz and Gonzalo {Caballero Miguez}", title = "The quality of institutions: A genetic programming approach", journal = "Economic Modelling", volume = "25", number = "1", pages = "161--169", year = "2008", ISSN = "0264-9993", DOI = "doi:10.1016/j.econmod.2007.05.001", URL = "http://www.sciencedirect.com/science/article/B6VB1-4P0VD80-1/2/c0bb8da3af64aa1ea6b0a4f90e4790b0", keywords = "genetic algorithms, genetic programming, Quality of institutions, Institutional determinants, Non-parametric perspective", abstract = "The new institutional economics has studied the determinants of the quality of institutions. Traditionally, the majority of the empirical literature has adopted a parametric and linear approach. These forms impose ad hoc functional structures, sometimes introducing relationships between variables that are forced and misleading. This paper analyses the determinants of the quality of institutions using a non-parametric and non-linear approach. Specifically, we employ a Genetic Program (GP) to study the functional relation between the quality of institutions and a set of historical, economical, geographical, religious and social variables. Besides this, we compare the obtained results with those employing a parametric perspective (Ordinary Least Square Regression). Following the empirical results of our application, we can conclude that the parametric perspective adopted in previous papers about institutional quality could be accurate.", } @TechReport{Alvarez-Diaz:funcas401, author = "Marcos Alvarez-Diaz and Gonzalo {Caballero Miguez} and Mario Solino", title = "The institutional determinants of {CO2} emissions: A computational modelling approach using Artificial Neural Networks and Genetic Programming", institution = "Fundacion de las Cajas de Ahorros", year = "2008", type = "FUNCAS Working Paper", number = "401", address = "Madrid", month = jul, keywords = "genetic algorithms, genetic programming, ANN", URL = "https://dialnet.unirioja.es/ejemplar/212749", broken = "http://www.funcas.es/Publicaciones/InformacionArticulos/Publicaciones.asp?ID=1411", notes = "see \cite{Alvarez-Diaz:2011:EM}", } @Article{Alvarez-Diaz:2009:IJCEE, title = "Forecasting tourist arrivals to {Balearic} {Islands} using genetic programming", author = "Marcos Alvarez-Diaz and Josep Mateu-Sbert and Jaume Rossello-Nadal", year = "2009", volume = "1", journal = "International Journal of Computational Economics and Econometrics", number = "1", pages = "64--75", month = nov # "~06", keywords = "genetic algorithms, genetic programming, tourism forecasting, Diebold-Mariano test, tourist arrivals, Balearic Islands, UK, United Kingdom, Germany, Spain", URL = "http://www.inderscience.com/link.php?id=29153", DOI = "doi:10.1504/IJCEE.2009.029153", publisher = "Inderscience Publishers", ISSN = "1757-1189", bibsource = "OAI-PMH server at www.inderscience.com", abstract = "Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.", } @Article{AlvarezDiaz2009, author = "Marcos {Alvarez Diaz} and Manuel Gonzalez Gomez and Angeles {Saavedra Gonzalez} and Jacobo {De Una Alvarez}", title = "On dichotomous choice contingent valuation data analysis: Semiparametric methods and Genetic Programming", journal = "Journal of Forest Economics", year = "2010", volume = "16", number = "2", pages = "145--156", month = apr, keywords = "genetic algorithms, genetic programming, Dichotomous choice contingent valuation, Genetic program, Parametric techniques, Proportional hazard model", ISSN = "1104-6899", DOI = "doi:10.1016/j.jfe.2009.02.002", broken = "http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1", size = "12 pages", abstract = "The aim of this paper is twofold. Firstly, we introduce a novel semi-parametric technique called Genetic Programming to estimate and explain the willingness to pay to maintain environmental conditions of a specific natural park in Spain. To the authors' knowledge, this is the first time in which Genetic Programming is employed in contingent valuation. Secondly, we investigate the existence of bias due to the functional rigidity of the traditional parametric techniques commonly employed in a contingent valuation problem. We applied standard parametric methods (logit and probit) and compared with results obtained using semi parametric methods (a proportional hazard model and a genetic program). The parametric and semiparametric methods give similar results in terms of the variables finally chosen in the model. Therefore, the results confirm the internal validity of our contingent valuation exercise.", notes = "2022 https://www.nowpublishers.com/JFE", } @Article{Alvarez-Diaz:2010:AEL, title = "Forecasting exchange rates using local regression", author = "Marcos Alvarez-Diaz and Alberto Alvarez", journal = "Applied Economics Letters", year = "2010", volume = "17", number = "5", pages = "509--514", month = mar, keywords = "genetic algorithms, genetic programming, local search", ISSN = "1350-4851", URL = "http://hdl.handle.net/10261/54902", URL = "https://ideas.repec.org/a/taf/apeclt/v17y2010i5p509-514.html", DOI = "doi:10.1080/13504850801987217", oai = "oai:RePEc:taf:apeclt:v:17:y:2010:i:5:p:509-514", size = "6 pages", abstract = "In this article we use a generalisation of the standard nearest neighbours, called local regression (LR), to study the predictability of the yen/US dollar and pound sterling/US dollar exchange rates. We also compare our results with those previously obtained with global methods such as neural networks, genetic programming, data fusion and evolutionary neural networks. We want to verify if we can generalise to the exchange rate forecasting problem the belief that local methods beat global ones.", notes = "In this letter we have used LR to verify three aspects regarding to exchange rate forecasting for the Japanese yen and the British pound against US dollar. Firstly, we analyse their predictability discovering the existence of a short-term predictable structure in the temporal evolution of both currencies. Secondly, we confirm the homogeneity behaviour in terms of forecasting for weekly exchange rates and, finally, we also verify that local methods do not always beat to the global ones in an exchange rate forecasting exercise.", } @Article{Alvarez-Diaz:2010:AFE, author = "Marcos {Alvarez Diaz}", title = "Speculative strategies in the foreign exchange market based on genetic programming predictions", journal = "Applied Financial Economics", year = "2010", volume = "20", number = "6", pages = "465--476", month = mar, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/09603100903459782", oai = "oai:RePEc:taf:apfiec:v:20:y:2010:i:6:p:465-476", abstract = "In this article, we investigate the out-of-sample forecasting ability of a Genetic Program (GP) to approach the dynamic evolution of the yen/US dollar and British pound/US dollar exchange rates, and verify whether the method can beat the random walk model. Later on, we use the predicted values to generate a trading rule and we check the possibility of obtaining extraordinary profits in the foreign exchange market. Our results reveal a slight forecasting ability for one-period-ahead, which is lost when more periods ahead are considered. On the other hand, our trading strategy obtains above-normal profits. However, when transaction costs are incorporated, the profits practically disappear or become negative.", notes = "Department of Economics, University of Vigo, Galicia, Spain", } @Article{Alvarez-Diaz:2011:EM, author = "Marcos Alvarez-Diaz and Gonzalo Caballero-Miguez and Mario Solino", title = "The institutional determinants of {CO2} emissions: a computational modeling approach using Artificial Neural Networks and Genetic Programming", journal = "Environmetrics", year = "2011", volume = "22", number = "1", pages = "42--49", month = feb, keywords = "genetic algorithms, genetic programming, artificial neural networks, ANN, computational methods, CO2 emissions, institutional determinants", URL = "https://doi.org/10.1002/env.1025", URL = "https://onlinelibrary.wiley.com/doi/abs/10.1002/env.1025", URL = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.1025", DOI = "doi:10.1002/env.1025", size = "8 pages", abstract = "Understanding the complex process of climate change implies the knowledge of all possible determinants of CO2 emissions. This paper studies the influence of several institutional determinants on CO2 emissions, clarifying which variables are relevant to explain this influence. For this aim, Genetic Programming and Artificial Neural Networks are used to find an optimal functional relationship between the CO2 emissions and a set of historical, economic, geographical, religious, and social variables, which are considered as a good approximation to the institutional quality of a country. Besides this, the paper compares the results using these computational methods with that employing a more traditional parametric perspective: ordinary least squares regression (OLS). Following the empirical results of the cross-country application, this paper generates new evidence on the binomial institutions and CO2 emissions. Specifically, all methods conclude a significant influence of ethnolinguistic fractionalization (ETHF) on CO2 emissions.", notes = "Replaces \cite{Alvarez-Diaz:funcas401}?", } @Article{Alvarez-Diaz:2019:Forecasting, author = "Marcos Alvarez-Diaz and Manuel Gonzalez-Gomez and Maria Soledad Otero-Giraldez", title = "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming", journal = "Forecasting", year = "2019", volume = "1", number = "1", pages = "90--106", note = "Special Issue Applications of Forecasting by Hybrid Artificial Intelligent Technologies", keywords = "genetic algorithms, genetic programming, ANN, international tourism demand forecasting, artificial neural networks, SARIMA, spain", ISSN = "2571-9394", bibsource = "OAI-PMH server at oai.repec.org", identifier = "RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666", oai = "oai:RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666", URL = "https://www.mdpi.com/2571-9394/1/1/7/", URL = "https://www.mdpi.com/2571-9394/1/1/7.pdf", DOI = "doi:10.3390/forecast1010007", abstract = "This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.", } @Article{Alvarez-Diaz:2020:EE, author = "Marcos Alvarez-Diaz", title = "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods", journal = "Empirical Economics", year = "2020", volume = "59", pages = "1285--1305", month = sep, keywords = "genetic algorithms, genetic programming, ANN, KNN, oil price, Forecasting, ARIMA, M-GARCH, Neural networks, Nearest-neighbour method", DOI = "doi:10.1007/s00181-019-01665-w", abstract = "Can we accurately predict the Brent oil price? If so, which forecasting method can provide the most accurate forecasts? To unravel these questions, we aim at predicting the weekly Brent oil price growth rate by using several forecasting methods that are based on different approaches. Basically, we assess and compare the out-of-sample performances of linear parametric models (the ARIMA, the ARFIMA and the autoregressive model), a nonlinear parametric model (the GARCH-in-Mean model) and different nonparametric data-driven methods (a nonlinear autoregressive artificial neural network, genetic programming and the nearest-neighbor method). The results obtained show that (1) all methods are capable of predicting accurately both the value and the directional change in the Brent oil price, (2) there are no significant forecasting differences among the methods and (3) the volatility of the series could be an important factor to enhance our predictive ability.", notes = "Department of Fundaments of Economic Analysis and History, and Economic Institutions, University of Vigo, Vigo, Spain", } @Misc{journals/corr/abs-2005-07669, author = "Jeovane Honorio Alves and Lucas Ferrari de Oliveira", title = "Optimizing Neural Architecture Search using Limited {GPU} Time in a Dynamic Search Space: A Gene Expression Programming Approach", howpublished = "arXiv", year = "2020", volume = "abs/2005.07669", keywords = "genetic algorithms, genetic programming, gene expression programming, GPU", URL = "https://arxiv.org/abs/2005.07669", bibdate = "2020-05-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-07669", } @Misc{oai:arXiv.org:1002.2012, title = "Implementing Genetic Algorithms on Arduino Micro-Controllers", author = "Nuno Alves", year = "2010", month = feb # "~09", size = "10 pages", abstract = "Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimisation and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimisations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1002.2012", keywords = "genetic algorithms, computer science, neural and evolutionary computing", URL = "http://arxiv.org/abs/1002.2012", URL = "http://arxiv.org/pdf/1002.2012v1.pdf", notes = "not GP", } @Article{ALVISO:2020:Fuel, author = "Dario Alviso and Guillermo Artana and Thomas Duriez", title = "Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming", journal = "Fuel", volume = "264", pages = "116844", year = "2020", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2019.116844", URL = "http://www.sciencedirect.com/science/article/pii/S0016236119321982", keywords = "genetic algorithms, genetic programming, Biodiesel, Fatty acid, Properties, Regression analysis", abstract = "This paper presents regression analysis of biodiesel physico-chemical properties as a function of fatty acid composition using an experimental database. The study is done by using 48 edible and non-edible oils-based biodiesel available data. Regression equations are presented as a function of fatty acid composition (saturated and unsaturated methyl esters). The physico-chemical properties studied are kinematic viscosity, flash point, cloud point, pour point (PP), cold filter plugging point, cetane (CN) and iodine numbers. The regression technique chosen to carry out this work is genetic programming (GP). Unlike multiple linear regression (MLR) strategies available in literature, GP provides generic, non-parametric regression among variables. For all properties analyzed, the performance of the regression is systematically better for GP than MLR. Indeed, the RSME related to the experimental database is lower for GP models, from approx3percent for CN to approx55percent for PP, in comparison to the best MLR model for each property. Particularly, most GP regression models reproduce correctly the dependence of properties on the saturated and unsaturated methyl esters", } @Article{ALVISO:2021:JFCA, author = "Dario Alviso and Cristhian Zarate and Guillermo Artana and Thomas Duriez", title = "Regressions of the dielectric constant and speed of sound of vegetable oils from their composition and temperature using genetic programming", journal = "Journal of Food Composition and Analysis", volume = "104", pages = "104175", year = "2021", ISSN = "0889-1575", DOI = "doi:10.1016/j.jfca.2021.104175", URL = "https://www.sciencedirect.com/science/article/pii/S0889157521003756", keywords = "genetic algorithms, genetic programming, Vegetable oils, Regression, Dielectric constant, Speed of sound, Fatty acid", abstract = "The dielectric constant (DC) and speed of sound (SoS) have been measured in many studies on vegetable oils (VOs). These measurements can be applied for quality control, for the detection of contaminants, and in works related to heated and frying VOs. There are several hundreds of VOs with potential use in the food industry, and for most of them, the DC and SoS values are not yet available. This paper proposes regression models of the DC and SoS of VOs as a function of their composition (saturated and unsaturated fatty acids) and the temperature. A regression study was conducted using available experimental databases including a total of 57 and 56 data in the range of 20-50 degreeC for the DC and SoS, respectively. The equations are obtained using genetic programming (GP). The goal is to minimize the mean absolute error (MAE) between the values of the measured and predicted DC and SoS for several VOs. The resulting GP regression equations reproduce correctly the dependencies of the DC and SoS of VOs on the saturated and unsaturated fatty acids. The validation of these equations is carried out by comparing their results to those of the experimental databases. The MAE values of the regression equations concerning the databases for DC and SoS of VOs are 0.02 and 1.0 m/s, respectively", } @Article{ALVISO:2021:Fuel, author = "Dario Alviso and Cristhian Zarate and Thomas Duriez", title = "Modeling of vegetable oils cloud point, pour point, cetane number and iodine number from their composition using genetic programming", journal = "Fuel", volume = "284", pages = "119026", year = "2021", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2020.119026", URL = "https://www.sciencedirect.com/science/article/pii/S0016236120320226", keywords = "genetic algorithms, genetic programming, Vegetable oils, Fatty acid, Cetane number, Iodine number, Cloud point, Pour point", abstract = "Vegetable oils (VOs) are composed of 90-98percent of triglycerides, i.e. esters composed of three fatty acids and glycerol, and small amounts of mono- and di-glycerides. Due to their physico-chemical properties, VOs have been considered for uses especially in large ships, in stationary engines and low and medium speed diesel engines, in pure form or in blends with fuel oil, diesel, biodiesel and alcohols. There are about 350 VOs with potential as fuel sources, and for most of them, physico-chemical properties values have not yet been measured. In this context, regression models using only VOs fatty acid composition are very useful. In the present paper, regression analysis of VOs cloud point (CP), pour point (PP), cetane number (CN) and iodine number (IN) as a function of saturated and unsaturated fatty acids is conducted. The study is done by using 4 experimental databases including 88 different data of VOs. Concerning the regression technique, genetic programming (GP) has been chosen. The cost function of GP is defined to minimize the Mean Absolute Error (MAE) between experimental and predicted values of each property. The resulting GP models consisting of terms including saturated and unsaturated fatty acids reproduce correctly the dependencies of all four properties on those acids. And they are validated by showing that their results are in good agreement to the experimental databases. In fact, MAE values of the proposed models with respect to the databases for CP, PP, CN and IN are lower than 4.51 degreeC, 4.54 degreeC, 3.64 and 8.01, respectively", } @Article{Alweshah:2015:IJCA, author = "Mohammed Alweshah and Walid Ahmed and Hamza Aldabbas", title = "Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models", journal = "International Journal of Computer Applications", year = "2015", volume = "125", number = "3", pages = "20--25", month = sep, keywords = "genetic algorithms, genetic programming", publisher = "Foundation of Computer Science (FCS), NY, USA", address = "New York, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume125/number3/22413-2015905864", URL = "https://www.ijcaonline.org/research/volume125/number3/alweshah-2015-ijca-905864.pdf", DOI = "doi:10.5120/ijca2015905864", size = "6 pages", abstract = "Building reliability growth models to predict software reliability and identify and remove errors is both a necessity and a challenge for software testing engineers and project managers. Being able to predict the number of faults in software helps significantly in determining the software release date and in effectively managing project resources. Most of the growth models consider two or three parameters to estimate the accumulated faults in the testing process. Interest in using evolutionary computation to solve prediction and modeling problems has grown in recent years. In this paper, we explore the use of genetic programming (GP) as a tool to help in building growth models that can accurately predict the number of faults in software early on in the testing process. The proposed GP model is based on a recursive relation derived from the history of measured faults. The developed model is tested on real-time control, military, and operating system applications. The dataset was developed by John Musa of Bell Telephone Laboratories. The results of a comparison of the GP model with the traditional and simpler auto-regression model are presented.", notes = "www.ijcaonline.org Al-Balqa Applied University, Salt, Jordan", } @InProceedings{DBLP:conf/ijcci/AlyasiriCK18, author = "Hasanen Alyasiri and John A. Clark and Daniel Kudenko", title = "Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System", booktitle = "Proceedings of the 10th International Joint Conference on Computational Intelligence, IJCCI 2018", editor = "Christophe Sabourin and Juan Julian Merelo Guervos and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", pages = "176--183", publisher = "SciTePress", year = "2018", address = "Seville, Spain", month = sep # " 18-20", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Intrusion Detection System, Stacking Ensemble", isbn13 = "978-989-758-327-8", ISSN = "2184-2825", URL = "https://www.scitepress.org/Papers/2018/69259/69259.pdf", URL = "https://doi.org/10.5220/0006925901760183", DOI = "doi:10.5220/0006925901760183", timestamp = "Thu, 26 Sep 2019 16:43:57 +0200", biburl = "https://dblp.org/rec/conf/ijcci/AlyasiriCK18.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "8 pages", abstract = "With cyber-attacks becoming a regular feature in daily business and attackers continuously evolving their techniques, we are witnessing ever more sophisticated and targeted threats. Various artificial intelligence algorithms have been deployed to analyse such incidents. Extracting knowledge allows the discovery of new attack methods, intrusion scenarios, and attackers objectives and strategies, all of which can help distinguish attacks from legitimate behaviour. Among those algorithms, Evolutionary Computation (EC) techniques have seen significant application. Research has shown it is possible to use EC methods to construct IDS detection rules. we show how Cartesian Genetic Programming (CGP) can construct the behaviour rule upon which an intrusion detection will be able to make decisions regarding the nature of the activity observed in the system. The CGP framework evolves human readable solutions that provide an explanation of the logic behind its evolved decisions. Experiments are conducted on up-to-date cybersecurity datasets and compared with state of the art paradigms. We also introduce ensemble learning paradigm, indicating how CGP can be used as stacking technique to improve the learning performance.", notes = "Also known as \cite{alyasiri2018applying} Department of Computer Science, University of York, UK", } @PhdThesis{Hasanen_Thesis_2018, author = "Hasanen Alyasiri", title = "Developing Efficient and Effective Intrusion Detection System using Evolutionary Computation", school = "Computer Science, University of York", year = "2018", address = "UK", month = nov, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://etheses.whiterose.ac.uk/id/eprint/23699", URL = "http://etheses.whiterose.ac.uk/23699/1/Hasanen_Thesis_2018.pdf", size = "157 pages", abstract = "The internet and computer networks have become an essential tool in distributed computing organisations especially because they enable the collaboration between components of heterogeneous systems. The efficiency and flexibility of online services have attracted many applications, but as they have grown in popularity so have the numbers of attacks on them. Thus, security teams must deal with numerous threats where the threat landscape is continuously evolving. The traditional security solutions are by no means enough to create a secure environment, intrusion detection systems (IDSs), which observe system works and detect intrusions, are usually used to complement other defense techniques. However, threats are becoming more sophisticated, with attackers using new attack methods or modifying existing ones. Furthermore, building an effective and efficient IDS is a challenging research problem due to the environment resource restrictions and its constant evolution. To mitigate these problems, we propose to use machine learning techniques to assist with the IDS building effort. In this thesis, Evolutionary Computation (EC) algorithms are empirically investigated for synthesising intrusion detection programs. EC can construct programs for raising intrusion alerts automatically. One novel proposed approach, i.e. Cartesian Genetic Programming, has proved particularly effective. We also used an ensemble-learning paradigm, in which EC algorithms were used as a meta-learning method to produce detectors. The latter is more fully worked out than the former and has proved a significant success. An efficient IDS should always take into account the resource restrictions of the deployed systems. Memory usage and processing speed are critical requirements. We apply a multi-objective approach to find trade-offs among intrusion detection capability and resource consumption of programs and optimise these objectives simultaneously. High complexity and the large size of detectors are identified as general issues with the current approaches. The multi-objective approach is used to evolve Pareto fronts for detectors that aim to maintain the simplicity of the generated patterns. We also investigate the potential application of these algorithms to detect unknown attacks.", notes = "supervisor: John A. Clark Identification Number/EthosID: uk.bl.ethos.772979", } @InProceedings{alyasiri2020evolving, author = "Hasanen Alyasiri", title = "Evolving Rules for Detecting Cross-Site Scripting Attacks Using Genetic Programming", booktitle = "2nd International Conference on Advances in Cyber Security, ACeS 2020", year = "2020", editor = "Mohammed Anbar and Nibras Abdullah and Selvakumar Manickam", volume = "1347", series = "Communications in Computer and Information Science", pages = "642--656", address = "Penang, Malaysia", month = dec # " 8-9", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-33-6834-7", timestamp = "Mon, 15 Feb 2021 12:59:21 +0100", biburl = "https://dblp.org/rec/conf/aces/Alyasiri20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://link.springer.com/chapter/10.1007/978-981-33-6835-4_42", DOI = "doi:10.1007/978-981-33-6835-4_42", abstract = "Web services are now a critical element of many of our day-to-day activities. Their applications are one of the fastest-growing industries around. The security issues related to these services are a major concern to their providers and are directly relevant to the everyday lives of system users. Cross-Site Scripting (XSS) is a standout amongst common web application security attacks. Protection against XSS injection attacks needs more work. Machine learning has considerable potential to provide protection in this critical domain. In this article, we show how genetic programming can be used to evolve detection rules for XSS attacks. We conducted our experiments on a publicly available and up-to-date dataset. The experimental results showed that the proposed method is an effective countermeasure against XSS attacks. We then investigated the computational cost of the detection rules. The best-evolved rule has a processing time of 177.87 ms and consumes memory of 8600 bytes.", notes = "Also known as \cite{DBLP:conf/aces/Alyasiri20} Department of Computer Science, University of Kufa, Kufa, Iraq", } @InProceedings{alyasiri2021grammatical, author = "Hasanen Alyasiri and John A Clark and Ali Malik and Ruairi {de Frein}", title = "Grammatical Evolution for Detecting Cyberattacks in Internet of Things Environments", booktitle = "2021 International Conference on Computer Communications and Networks (ICCCN)", year = "2021", address = "Athens, Greece", month = "19-22 " # jul, organization = "IEEE", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-1-6654-4835-2", URL = "https://ieeexplore.ieee.org/abstract/document/9522283", DOI = "doi:10.1109/ICCCN52240.2021.9522283", abstract = "The Internet of Things (IoT) is revolutionising nearly every aspect of modern life, playing an ever greater role in both industrial and domestic sectors. The increasing frequency of cyber-incidents is a consequence of the pervasiveness of IoT. Threats are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams must deal with a diverse and complex threat landscape that is constantly evolving. Traditional security solutions cannot protect such systems adequately and so researchers have begun to use Machine Learning algorithms to discover effective defense systems. we investigate how one approach from the domain of evolutionary computation, grammatical evolution, can be used to identify cyberattacks in IoT environments. The experiments were conducted on up-to-date datasets and compared with state-of-the-art algorithms. The potential application of evolutionary computation-based approaches to detect unknown attacks is also examined and discussed.", notes = "Also known as \cite{9522283} University of Kufa, Iraq", } @Article{Amandi:2018:GPEM, author = "Analia Amandi", title = "Ryan J. Urbanowicz, and Will N. Browne: Introduction to learning classifier systems Springer, 2017, 123 pp, ISBN 978-3-662-55007-6", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "4", pages = "569--570", month = dec, note = "Book review", keywords = "genetic algorithms, LCS", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-018-9322-7", size = "2 pages", notes = "p570 'appropriate for anyone who wants to know and begin to use Learning Classifier Systems'", } @Article{Amar:2020:jNGSE, author = "Menad Nait Amar and Mohammed Abdelfetah Ghriga and Hocine Ouaer and Mohamed El Amine Ben Seghier and Binh Thai Pham and Pal Ostebo Andersen", title = "Modeling viscosity of {CO2} at high temperature and pressure conditions", journal = "Journal of Natural Gas Science and Engineering", year = "2020", volume = "77", pages = "103271", month = may, keywords = "genetic algorithms, genetic programming, gene expression programming, ANN, carbon dioxide, correlations, data-driven, GEP, MLP, viscosity, chemical sciences/polymers, material chemistry, physical chemistry", ISSN = "1875-5100", publisher = "HAL CCSD; Elsevier", URL = "https://hal.archives-ouvertes.fr/hal-02534736", DOI = "doi:10.1016/j.jngse.2020.103271", abstract = "The present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.", annote = "Institut des sciences analytiques et de physico-chimie pour l'environnement et les materiaux (IPREM) ; Universite de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS); Institute of Research and Development; Duy-Tan University; University of Stavanger ; University of Stavanger", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", description = "International audience", identifier = "hal-02534736; DOI: 10.1016/j.jngse.2020.103271", language = "en", oai = "oai:HAL:hal-02534736v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jngse.2020.103271", } @Article{Amar:2019:ChemSci, author = "Yehia Amar and Artur M. Schweidtmann and Paul Deutsch and Liwei Cao and Alexei Lapkin", title = "Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis", journal = "Chemical Science", year = "2019", volume = "10", number = "27", pages = "6697--6706", month = jul, note = "Edge Article", keywords = "genetic algorithms, genetic programming, TPOT, gamultiobj, matlab, GP surrogate models, in silico modeling", publisher = "Royal Society of Chemistry", URL = "https://pubs.rsc.org/en/content/articlepdf/2019/sc/c9sc01844a", DOI = "doi:10.1039/C9SC01844A", size = "10 pages", abstract = "Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral alpha-beta unsaturated gamma-lactam. With two simultaneous objectives: high conversion and high diastereomeric excess, the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.", notes = "p6704 'the automated machine learning workflow was successfully used for the problem of solvent selection .. supplemented .. surrogate model' Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK http://rsc.li/chemical-science", } @PhdThesis{Amar:thesis, author = "Yehia Amar", title = "Accelerating process development of complex chemical reactions", school = "Department of Chemical Engineering and Biotechnology, University of Cambridge", year = "2019", address = "UK", keywords = "molecular descriptors, design of experiments, asymmetric hydrogenation, machine learning, process development", URL = "https://www.repository.cam.ac.uk/handle/1810/288220", DOI = "doi:10.17863/CAM.35535", abstract = "Process development of new complex reactions in the pharmaceutical and fine chemicals industries is challenging, and expensive. The field is beginning to see a bridging between fundamental first-principles investigations, and use of data-driven statistical methods, such as machine learning. Nonetheless, process development and optimisation in these industries is mostly driven by trial-and-error, and experience. Approaches that move beyond these are limited to the well-developed optimisation of continuous variables, and often do not yield physical insights. This thesis describes several new methods developed to address research questions related to this challenge. First, we investigated whether using physical knowledge could aid statistics-guided self-optimisation of a C-H activation reaction, in which the optimisation variables were continuous. We then considered algorithmic treatment of the more challenging discrete variables, focusing on solvents. We parametrised a library of 459 solvents with physically meaningful molecular descriptors. Our case study was a homogeneous Rh-catalysed asymmetric hydrogenation to produce a chiral gamma-lactam, with conversion and diastereoselectivity as objectives. We adapted a state-of-the-art multi-objective machine learning algorithm, based on Gaussian processes, to use the descriptors as inputs, and to create a surrogate model for each objective. The aim of the algorithm was to determine a set of Pareto solutions with a minimum experimental budget, whilst simultaneously addressing model uncertainty. We found that descriptors are a valuable tool for Design of Experiments, and can produce predictive and interpretable surrogate models. Subsequently, a physical investigation of this reaction led to the discovery of an efficient catalyst-ligand system, which we studied by operando NMR, and identified a parameterised kinetic model. Turning the focus then to ligands for asymmetric hydrogenation, we calculated versatile empirical descriptors based on the similarity of atomic environments, for 102 chiral ligands, to predict diastereoselectivity. Whilst the model fit was good, it failed to accurately predict the performance of an unseen ligand family, due to analogue bias. Physical knowledge has then guided the selection of symmetrised physico-chemical descriptors. This produced more accurate predictive models for diastereoselectivity, including for an unseen ligand family. The contribution of this thesis is a development of novel and effective workflows and methodologies for process development. These open the door for process chemists to save time and resources, freeing them up from routine work, to focus instead on creatively designing new chemistry for future real-world applications.", notes = "Is this GP? Ie does it use TPOT ? See also DOI:10.1039/c9sc01844a Supervisor: Alexei Lapkin", } @InProceedings{amaral:2022:GECCOcomp, author = "Ryan Amaral and Alexandru Ianta and Caleidgh Bayer and Robert Smith and Malcolm Heywood", title = "Benchmarking Genetic Programming in a Multi-action Reinforcement Learning Locomotion Task", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "522--525", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, real-valued actions, continuous control, reinforcement learning", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528766", abstract = "Reinforcement learning (RL) requires an agent to interact with an environment to maximize the cumulative rather than the immediate reward. Recently, there as been a significant growth in the availability of scalable RL tasks, e.g. OpenAI gym. However, most benchmarking studies concentrate on RL solutions based on some form of deep learning. In this work, we benchmark a family of linear genetic programming based approaches to the 2-d biped walker problem. The biped walker is an example of a RL environment described in terms of a multi-dimensional, real-valued 24-d input and 4-d action space. Specific recommendations are made regarding mechanisms to adopt that are able to consistently produce solutions, in this case using transfer from periodic restarts.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{amarteifio:2004:AL, author = "Saoirse Amarteifio and Michael O'Neill", title = "An Evolutionary Approach to Complex System Regulation Using Grammatical Evolution", booktitle = "Artificial Life {XI} Ninth International Conference on the Simulation and Synthesis of Living Systems", year = "2004", editor = "Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson", pages = "551--556", address = "Boston, Massachusetts", month = "12-15 " # sep, publisher = "The MIT Press", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "0-262-66183-7", URL = "http://ncra.ucd.ie/papers/alife2004.pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6278781", DOI = "doi:10.7551/mitpress/1429.003.0093", size = "6 pages", abstract = "Motivated by difficulties in engineering adaptive distributed systems, we consider a method to evolve cooperation in swarms to model dynamical systems. We consider an information processing swarm model that we find to be useful in studying control methods for adaptive distributed systems and attempt to evolve systems that form consistent patterns through the interaction of constituent agents or particles. This model considers artificial ants as walking sensors in an information-rich environment. Grammatical Evolution is combined with this swarming model as we evolve an ant's response to information. The fitness of the swarm depends on information processing by individual ants, which should lead to appropriate macroscopic spatial and/or temporal patterns. We discuss three primary issues, which are tractability, representation and fitness evaluation of dynamical systems and show how Grammatical Evolution supports a promising approach to addressing these concerns", notes = "ALIFE9", } @InProceedings{amarteifio:2005:CEC, author = "Saoirse Amarteifio and Michael O'Neill", title = "Coevolving Antibodies with a Rich Representation of Grammatical Evolution", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "1", pages = "904--911", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, grammatical evolution, genotype-phenotype mapping", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554779", size = "8 pages", abstract = "A number of natural anticipatory systems employ dual processes of feature definition and feature exploitation. Presented here, a coevolutionary dual process model based on the immune system, considers the effect of coevolving complementary templates to bias feature selection and recombination. This work considers the issue of module exploitation in evolutionary algorithms. Our approach is characterised by the use of rich representations in grammatical evolution.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @MastersThesis{amarteifio:2005:IAGPMWRRIX, title = "Interpreting a Genotype-Phenotype Map with Rich Representations in XMLGE", author = "Saoirse Amarteifio", school = "University of Limerick", year = "2005", type = "Master of Science in Computer Science", address = "University of Limerick, Ireland", keywords = "genetic algorithms, genetic programming, grammatical evolution, xml", URL = "http://ncra.ucd.ie/downloads/pub/SaoirseMScThesis.pdf", size = "177 pages", abstract = "A novel XML implementation of Grammatical Evolution is developed. This has a number of interesting features such as the use of XSLT for genetic operators and the use of reflection to build an object tree from an XML expression tree. This framework is designed to be used for remote or local evaluation of evolved program structures and provides a number of abstraction layers for program evaluation and evolution. A dynamical swarm system is evolved as a special-case function induction problem to illustrate the application of XMLGE. Particle behaviours are evolved to optimise colony performance. A dual process evolutionary algorithm based on the immune system using rich representations is developed. A dual process feature detection and feature integration model is described and the performance shown on benchmark GP problems. An adaptive feature detection method uses coevolving XPath antibodies to take selective interest in primary structures. Grammars are used to generate reciprocal binding structures (antibodies) given any primary domain grammar. A codon compression algorithm is developed which shows performance improvements on symbolic regression and multiplexer problems. The algorithm is based on questions about the information content of a genome. This also exploits information from the rich representation of XMLGE.", language = "en", } @Article{Amber:2015:EB, author = "K. P. Amber and M. W. Aslam and S. K. Hussain", title = "Electricity consumption forecasting models for administration buildings of the {UK} higher education sector", journal = "Energy and Buildings", volume = "90", pages = "127--136", year = "2015", keywords = "genetic algorithms, genetic programming, Electricity forecasting, Administration buildings, Multiple regression", ISSN = "0378-7788", DOI = "doi:10.1016/j.enbuild.2015.01.008", URL = "http://www.sciencedirect.com/science/article/pii/S0378778815000110", abstract = "Electricity consumption in the administration buildings of a typical higher education campus in the UK accounts for 26percent of the campus annual electricity consumption. A reliable forecast of electricity consumption helps energy managers in numerous ways such as in preparing future energy budgets and setting up energy consumption targets. In this paper, we developed two models, a multiple regression (MR) model and a genetic programming (GP) model to forecast daily electricity consumption of an administration building located at the Southwark campus of London South Bank University in London. Both models integrate five important independent variables, i.e. ambient temperature, solar radiation, relative humidity, wind speed and weekday index. Daily values of these variables were collected from year 2007 to year 2013. The data sets from year 2007 to 2012 are used for training the models while 2013 data set is used for testing the models. The predicted test results for both the models are analysed and compared with actual electricity consumption. At the end, some conclusions are drawn about the performance of both models regarding their forecasting capabilities. The results demonstrate that the GP model performs better with a Total Absolute Error (TAE) of 6percent compared to TAE of 7percent for MR model.", } @Article{AMBER:2018:Energy, author = "K. P. Amber and R. Ahmad and M. W. Aslam and A. Kousar and M. Usman and M. S. Khan", title = "Intelligent techniques for forecasting electricity consumption of buildings", journal = "Energy", volume = "157", pages = "886--893", year = "2018", keywords = "genetic algorithms, genetic programming, Electricity forecasting, ANN, DNN, GP, MR, SVM", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2018.05.155", URL = "http://www.sciencedirect.com/science/article/pii/S036054421830999X", abstract = "The increasing trend in building sector's energy demand calls for reliable and robust energy consumption forecasting models. This study aims to compare prediction capabilities of five different intelligent system techniques by forecasting electricity consumption of an administration building located in London, United Kingdom. These five techniques are; Multiple Regression (MR), Genetic Programming (GP), Artificial Neural Network (ANN), Deep Neural Network (DNN) and Support Vector Machine (SVM). The prediction models are developed based on five years of observed data of five different parameters such as solar radiation, temperature, wind speed, humidity and weekday index. Weekday index is an important parameter introduced to differentiate between working and non-working days. First four years data is used for training the models and to obtain prediction data for fifth year. Finally, the predicted electricity consumption of all models is compared with actual consumption of fifth year. Results demonstrate that ANN performs better than all other four techniques with a Mean Absolute Percentage Error (MAPE) of 6percent whereas MR, GP, SVM and DNN have MAPE of 8.5percent, 8.7percent, 9percent and 11percent, respectively. The applicability of this study could span to other building categories and will help energy management teams to forecast energy consumption of various buildings", keywords = "genetic algorithms, genetic programming, Electricity forecasting, ANN, DNN, GP, MR, SVM", } @InProceedings{amblard:2023:GGP, author = "Julien Amblard and Robert Filman and Gabriel Kopito", title = "{GPStar4:} A Flexible Framework for Experimenting with Genetic Programming", booktitle = "Graph-based Genetic Programming", year = "2023", editor = "Roman Kalkreuth and Thomas Baeck and Dennis G. Wilson and Paul Kaufmann and Leo Francoso Dal Piccol Sotto and Timothy Aktinson", pages = "1910--1915", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, experimental framework, directed acyclic graph representations, population algorithms", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596369", size = "6 pages", abstract = "GPStar4 is a flexible workbench for experimenting with population algorithms. It is a framework that defines a genetic cycle, with inflection points for implementing an algorithm's specific behaviors; it also provides a variety of implementations for these inflection points. A user of the system can select from the provided implementations and customize the places where alternative behavior is desired, or even create their own implementations. Components interact through a context mechanism that enables both mutable and immutable information sharing, type checking, computed defaults and event listeners.Interesting predefined components included in GPStar4 are implementations for classical tree-based expression structures; acyclic multigraphs with named ports, type systems for flat, hierarchical and attribute types, recursively defined populations using both subpopulation and build-from-parts semantics, and numeric and multi-objective fitnesses. Key enabling technologies for this flexibility include context mechanisms, choosers, and a variety of caches.GPStar4 can be run as an API library for other applications, as a command-line application, or as a stand-alone application with its own GUI.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{AMERYAN:2020:CS, author = "Ala Ameryan and Mansour Ghalehnovi and Mohsen Rashki", title = "Investigation of shear strength correlations and reliability assessments of sandwich structures by kriging method", journal = "Composite Structures", volume = "253", pages = "112782", year = "2020", ISSN = "0263-8223", DOI = "doi:10.1016/j.compstruct.2020.112782", URL = "http://www.sciencedirect.com/science/article/pii/S0263822320327082", keywords = "genetic algorithms, genetic programming, Structural reliability, Kriging, Sandwich structures, Finite element, Experimental data, Failure probability", abstract = "Steel-concrete-steel (SCS) sandwich composite structure with corrugated-strip connectors (CSC) is the promising structure which is applied in offshore and building structures. The behavior prediction of shear connections is of major importance in SCS structures. The present study evaluated the existing shear strength correlations of SCS sandwich structures exploiting experimental data and Finite Element Analysis (FEA). The considered system is a double steel skin sandwich structure with CSC (DSCS). Due to the limitation of the literature regarding CSC development, some new correlations were proposed and studied relying on several FEA results through the Genetic Programming method. The accuracy of the estimated shear strength predicted by the existing and proposed equations was evaluated using the FEA data and push-out test results. The FE models were verified through experimental data. Moreover, the correlations were investigated based on reliability assessment due to the high importance of the reliability analysis of such structures. Given that high accuracy in estimating the shear strength fails to necessarily lead to acceptable results in structural reliability analysis, the reliability of the existing and proposed equations was evaluated using the Kriging model by considering experimental data. This meta-model could predict accurate values with a limited number of initial training samples", } @Article{amin:2022:Polymers, author = "Muhammad Nasir Amin and Mudassir Iqbal and Fadi Althoey and Kaffayatullah Khan and Muhammad Iftikhar Faraz and Muhammad Ghulam Qadir and Anas Abdulalim Alabdullah and Ali Ajwad", title = "Investigating the Bond Strength of {FRP} Rebars in Concrete under High Temperature Using Gene-Expression Programming Model", journal = "Polymers", year = "2022", volume = "14", number = "15", pages = "Article No. 2992", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2073-4360", URL = "https://www.mdpi.com/2073-4360/14/15/2992", DOI = "doi:10.3390/polym14152992", abstract = "In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70percent of the data was used for training the model and remaining 30percent was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations.", notes = "also known as \cite{polym14152992}", } @Article{amin:2022:Materials, author = "Muhammad Nasir Amin and Muhammad Raheel and Mudassir Iqbal and Kaffayatullah Khan and Muhammad Ghulam Qadir and Fazal E. Jalal and Anas Abdulalim Alabdullah and Ali Ajwad and Majdi Adel Al-Faiad and Abdullah Mohammad Abu-Arab", title = "Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming", journal = "Materials", year = "2022", volume = "15", number = "19", pages = "Article No. 6959", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1996-1944", URL = "https://www.mdpi.com/1996-1944/15/19/6959", DOI = "doi:10.3390/ma15196959", abstract = "The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.", notes = "also known as \cite{ma15196959}", } @Article{DBLP:journals/apin/AminiAH20, author = "Seyed Mohammad Hossein Hosseini Amini and Mohammad Abdollahi and Maryam Amir Haeri", title = "Rule-centred genetic programming {(RCGP):} an imperialist competitive approach", journal = "Appl. Intell.", volume = "50", number = "8", pages = "2589--2609", year = "2020", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s10489-019-01601-6", DOI = "doi:10.1007/s10489-019-01601-6", timestamp = "Thu, 06 Aug 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/apin/AminiAH20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{journals/nca/AminianJAGE11, author = "Pejman Aminian and Mohamad Reza Javid and Abazar Asghari and Amir Hossein Gandomi and Milad {Arab Esmaeili}", title = "A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method", journal = "Neural Computing and Applications", year = "2011", number = "8", volume = "20", pages = "1321--1332", publisher = "Springer", keywords = "genetic algorithms, genetic programming, base shear, steel frame structures, simulated annealing, nonlinear modelling", ISSN = "0941-0643", DOI = "doi:10.1007/s00521-011-0689-0", size = "12 pages", abstract = "This study presents a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. The base shear of steel frames was formulated in terms of the number of bays, number of storey, soil type, and situation of braced or unbraced. A classical GP model was developed to benchmark the GP/SA model. The comprehensive database used for the development of the correlations was obtained from finite element analysis. A parametric analysis was carried out to evaluate the sensitivity of the base shear to the variation of the influencing parameters. The GP/SA and classical GP correlations provide a better prediction performance than the widely used UBC code and a neural network-based model found in the literature. The developed correlations may be used as quick checks on solutions developed by deterministic analyses.", notes = "Special Issue: ISNN 2010", affiliation = "Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran", bibdate = "2011-10-18", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca20.html#AminianJAGE11", } @Article{Aminian:2013:NCA, author = "Pejman Aminian and Hadi Niroomand and Amir Hossein Gandomi and Amir Hossein Alavi and Milad {Arab Esmaeili}", title = "New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach", journal = "Neural Computing and Applications", year = "2013", volume = "23", number = "1", pages = "119--131", month = jul, keywords = "genetic algorithms, genetic programming, Linear genetic programming, Castellated beam, Load carrying capacity, Simulated annealing, Formulation", publisher = "Springer", language = "English", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007%2Fs00521-012-1138-4", DOI = "doi:10.1007/s00521-012-1138-4", size = "13 pages", abstract = "This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalisation capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.", } @InCollection{AmirHaeri:wsc17, author = "Maryam {Amir Haeri} and Mohammad Mehdi Ebadzadeh and Gianluigi Folino", title = "Statistical Genetic Programming: The Role of Diversity", booktitle = "Soft Computing in Industrial Applications", publisher = "Springer", year = "2014", editor = "Vaclav Snasel and Pavel Kroemer and Mario Koeppen and Gerald Schaefer", volume = "223", series = "Advances in Intelligent Systems and Computing", chapter = "4", pages = "37--48", month = "21 " # nov, note = "Proceedings of the 17th Online World Conference on Soft Computing in Industrial Applications", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-00929-2", URL = "http://dx.doi.org/10.1007/978-3-319-00930-8_4", DOI = "doi:10.1007/978-3-319-00930-8_4", language = "English", abstract = "In this chapter, a new GP-based algorithm is proposed. The algorithm, named SGP (Statistical GP), exploits statistical information, i.e. mean, variance and correlation-based operators, in order to improve the GP performance. SGP incorporates new genetic operators, i.e. Correlation Based Mutation, Correlation Based Crossover, and Variance Based Editing, to drive the search process towards fitter and shorter solutions. Furthermore, this work investigates the correlation between diversity and fitness in SGP, both in terms of phenotypic and genotypic diversity. First experiments conducted on four symbolic regression problems illustrate the goodness of the approach and permits to verify the different behaviour of SGP in comparison with standard GP from the point of view of the diversity and its correlation with the fitness.", notes = "http://dap.vsb.cz/wsc17/ WSC17 2012 Online Conference on Soft Computing in Industrial Applications Anywhere on Earth, December 10-21, 2012 Author Affiliations Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran ICAR-CNR, Rende, Italy", } @Article{AmirHaeri:2014:GPEM, author = "Maryam {Amir Haeri} and Mohammad Mehdi Ebadzadeh and Gianluigi Folino", title = "Improving GP generalization: a variance-based layered learning approach", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "1", pages = "27--55", month = mar, keywords = "genetic algorithms, genetic programming, VBLL-GP, Generalisation, Layered learning, Over fitting, Variance", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9220-6", size = "29 pages", abstract = "This paper introduces a new method that improves the generalisation ability of genetic programming (GP) for symbolic regression problems, named variance-based layered learning GP. In this approach, several datasets, called primitive training sets, are derived from the original training data. They are generated from less complex to more complex, for a suitable complexity measure. The last primitive dataset is still less complex than the original training set. The approach decomposes the evolution process into several hierarchical layers. The first layer of the evolution starts using the least complex (smoothest) primitive training set. In the next layers, more complex primitive sets are given to the GP engine. Finally, the original training data is given to the algorithm. We use the variance of the output values of a function as a measure of the functional complexity. This measure is used in order to generate smoother training data, and controlling the functional complexity of the solutions to reduce the overfitting. The experiments, conducted on four real-world and three artificial symbolic regression problems, demonstrate that the approach enhances the generalization ability of the GP, and reduces the complexity of the obtained solutions.", notes = "LD50, Bioavailablity, concrete, Pollen, UBall5D, RatPol2D Author Affiliations: 1. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran 2. ICAR-CNR, Rende, Italy", } @Article{journals/asc/HaeriEF17, author = "Maryam Amir Haeri and Mohammad Mehdi Ebadzadeh and Gianluigi Folino", title = "Statistical genetic programming for symbolic regression", journal = "Applied Soft Computing", year = "2017", volume = "60", pages = "447--469", month = nov, keywords = "genetic algorithms, genetic programming, Symbolic regression, Well-structured subtree, Semi-well-structured tree, Well-structuredness measure, Correlation coefficient", bibdate = "2017-11-22", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc60.html#HaeriEF17", DOI = "doi:10.1016/j.asoc.2017.06.050", abstract = "In this paper, a new genetic programming (GP) algorithm for symbolic regression problems is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical information (such as variance, mean and correlation coefficient) to improve GP. To this end, we define well-structured trees as a tree with the following property: nodes which are closer to the root have a higher correlation with the target. It is shown experimentally that on average, the trees with structures closer to well-structured trees are smaller than other trees. SGP biases the search process to find solutions whose structures are closer to a well-structured tree. For this purpose, it extends the terminal set by some small well-structured subtrees, and starts the search process in a search space that is limited to semi-well-structured trees (i.e., trees with at least one well-structured subtree). Moreover, SGP incorporates new genetic operators, i.e., correlation-based mutation and correlation-based crossover, which use the correlation between outputs of each subtree and the targets, to improve the functionality. Furthermore, we suggest a variance-based editing operator which reduces the size of the trees. SGP uses the new operators to explore the search space in a way that it obtains more accurate and smaller solutions in less time. SGP is tested on several symbolic regression benchmarks. The results show that it increases the evolution rate, the accuracy of the solutions, and the generalization ability, and decreases the rate of code growth.", notes = "Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran", } @Article{journals/jifs/AmiriAT14, title = "Ground motion prediction equations ({GMPE}s) for elastic response spectra in the Iranian plateau using Gene Expression Programming ({GEP})", author = "Gholamreza Ghodrati Amiri and Mohamad Shamekhi Amiri and Zahra Tabrizian", journal = "Journal of Intelligent and Fuzzy Systems", year = "2014", number = "6", volume = "26", pages = "2825--2839", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2014-05-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs26.html#AmiriAT14", URL = "http://dx.doi.org/10.3233/IFS-130950", DOI = "doi:10.3233/IFS-130950", abstract = "This paper proposes ground-motion prediction equations (GMPEs) for the horizontal component of earthquake in Iranian plateau. These equations present the velocity and acceleration response spectra at 5percent damping ratio as continuous period functions, within range of 0.1 to 4 seconds. So far many equations have been presented and the recent suggested proportions are functions of several parameters. In this research, due to easy usage and lack of information in Iran, only the magnitude of earthquake, the distance between earthquake source and the location and the ground type are used as important factors. Iranian plateau is divided into two zones: Alborz-Central Iran and Zagros, each of which is divided into rock and soil region according to the ground type. Regarding the fact that the occurred and reported earthquakes in Iran are shallow, surface wave magnitude (Ms) is used in this study. Moreover, hypocentral distance is considered as distance between the earthquake source and the location. To obtain the velocity and acceleration response spectra, a Gene Expression Programming (GEP) algorithm is used which uses no constant regression model and the model is acquired smartly as a continuous period function. The consequences show a consistency with high proportionality coefficient among the observed and anticipated results", } @Article{Amiri:2013:SI, author = "Mohammad Amiri and Mahdi Eftekhari and Maryam Dehestani and Azita Tajaddini", title = "Modeling intermolecular potential of {He-F2} dimer from symmetry-adapted perturbation theory using multi-gene genetic programming", journal = "Scientia Iranica", year = "2013", volume = "20", number = "3", pages = "543--548", keywords = "genetic algorithms, genetic programming, Potential energy, SAPT, MGGP, Lennard-Jones potential, GPTIPS, Matlab", ISSN = "1026-3098", URL = "https://core.ac.uk/download/pdf/81997689.pdf", URL = "http://www.sciencedirect.com/science/article/pii/S1026309813000758", DOI = "doi:10.1016/j.scient.2012.12.040", size = "6 pages", abstract = "Any molecular dynamical calculation requires a precise knowledge of interaction potential as an input. In an appropriate form, such that the potential, with respect to the coordinates, can be evaluated easily and accurately at arbitrary geometries (in our study parameters for geometry are R and theta), a good potential energy expression can offer the exact intermolecular behaviour of systems. There are many methods to create mathematical expressions for the potential energy. In this study for the first time, we used the Multi-gene Genetic Programming (MGGP) method to generate a potential energy model for the He-F2 system. The MGGP method is one of the most powerful methods used for non-linear regression problems. A dataset of size 714 created by the SAPT 2008 program is used to generate models of MGGP. The results obtained show the power of MGGP for producing an efficient nonlinear regression model, in terms of accuracy and complexity.", } @Article{AMIRI:2021:CSCM, author = "Mostafa Amiri and Farzad Hatami and Emadaldin Mohammadi Golafshani", title = "Evaluating the synergic effect of waste rubber powder and recycled concrete aggregate on mechanical properties and durability of concrete", journal = "Case Studies in Construction Materials", volume = "15", pages = "e00639", year = "2021", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2021.e00639", URL = "https://www.sciencedirect.com/science/article/pii/S2214509521001546", keywords = "genetic algorithms, genetic programming, Waste rubber powder, Recycled concrete aggregate, Green concrete, Mechanical properties, Durability", abstract = "The use of waste materials in the concrete mixture can help human beings to preserve the environment and achieve environmentally-friendly concrete. In this study, the influences of simultaneous replacements of cement by waste rubber powder (WRP) and coarse aggregate by recycled concrete aggregate (RCA) on the mechanical properties and durability of concrete were investigated experimentally. To do so, concrete specimens containing the WRP with the replacement ratios of percent, 2.5 percent, and 5 percent by weight of cement, and the RCA with the replacement levels of percent, 25 percent, and 50 percent of coarse aggregate were prepared. Moreover, different water to binder ratios and binder content were used. Mechanical properties of the concrete specimens consisting of compressive, flexural, and tensile strengths and the durability test of rapid chloride migration test (RCMT) were carried out at different ages. It was observed that the mechanical properties of concrete decrease by raising the proportions of recycled materials in all replacement ratios. Because of the negative effects of the WRP and RCA on, respectively, the cement matrix and the interfacial transition zone, the reduction of the mechanical properties are higher for the concrete specimens with both recycled materials. In the case of durability, the migration rate of chloride ions in concrete reduces by increasing the WRP rates due to the blockage of micro-pores connections. However, adding the RCA has a negative effect on the durability performance of concrete. Finally, four equations were proposed and evaluated for the compressive, tensile, flexural strength reduction and durability factors of concrete containing the WRP and RCA using the genetic programming", } @Article{AMISH:2023:rineng, author = "Mohamed Amish and Eta Etta-Agbor", title = "Genetic programming application in predicting fluid loss severity", journal = "Results in Engineering", volume = "20", pages = "101464", year = "2023", ISSN = "2590-1230", DOI = "doi:10.1016/j.rineng.2023.101464", URL = "https://www.sciencedirect.com/science/article/pii/S2590123023005911", keywords = "genetic algorithms, genetic programming, Lost circulation, Machine learning, Multigene genetic algorithms, Drilling. non-productive time", abstract = "Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is used to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are used. The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations", } @Article{nc:Amit+Geman:1997, author = "Yali Amit and Donald Geman", title = "Shape Quantization and Recognition with Randomized Trees", journal = "Neural Computation", year = "1997", volume = "9", number = "7", pages = "1545--1588", month = oct, abstract = "We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labelled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred LATeX symbols. State-of-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on LATeX symbols is to analyse invariance, generalisation error and related issues, and a comparison with artificial neural networks methods is presented in this context.", notes = "MIT Press Cited by \cite{MatthewGSmith:2005:GPEM}.", } @Article{Ammar:2016:Neurocomputing, author = "Marwa Ammar and Souhir Bouaziz and Adel M. Alimi and Ajith Abraham", title = "Multi-agent architecture for Multiaobjective optimization of Flexible Neural Tree", journal = "Neurocomputing", volume = "214", pages = "307--316", year = "2016", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2016.06.019", URL = "http://www.sciencedirect.com/science/article/pii/S0925231216306579", abstract = "In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)' training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi-Dimensional Particle Swarm Optimization ( PMD _ PSO ) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree ( PMA _ FNT ). To assess the effectiveness of PMA _ FNT , four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature.", keywords = "genetic algorithms, genetic programming, Flexible Neural Tree, Multi-agent architecture, Multi-objective optimization, Evolutionary Computation algorithms, Negotiation, Classification", } @Article{Amoretti:2013:GPEM, author = "Michele Amoretti", title = "Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "2", pages = "127--153", month = jun, keywords = "genetic algorithms, Peer-to-peer network, Artificial evolution, Complex adaptive system", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9182-0", size = "27 pages", abstract = "A peer-to-peer (P2P) network is a complex system whose elements (peer nodes, or simply peers) cooperate to implement scalable distributed services. From a general point of view, the activities of a P2P system are consequences of external inputs coming from the environment, and of the internal feedback among nodes. The reaction of a peer to direct or indirect inputs from the environment is dictated by its functional structure, which is usually defined in terms of static rules (protocols) shared among peers. The introduction of artificial evolution mechanisms may improve the efficiency of P2P networks, with respect to resource consumption, while preserving high performance in response to the environmental needs. In this paper, we propose the distributed remodelling framework (DRF), a general approach for the design of efficient environment-driven peer-to-peer networks. As a case study, we consider an ultra-large-scale storage and computing system whose nodes perform lookups for resources provided by other nodes, to cope with task execution requests that cannot be fulfilled locally. Thanks to the DRF, workload modifications trigger reconfigurations at the level of single peers, from which global system adaptation emerges without centralised control.", } @InProceedings{amos:1998:DNAsbc, author = "Martyn Amos and Paul E. Dunne and Alan Gibbons", title = "DNA Simulation of {Boolean} Circuits", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "679--683", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "DNA Computing", ISBN = "1-55860-548-7", notes = "GP-98", } @InProceedings{Amte:2015:ICESA, author = "A. Y. Amte and P. S. Kate", booktitle = "2015 International Conference on Energy Systems and Applications", title = "Automatic generation of Lyapunov function using Genetic programming approach", year = "2015", pages = "771--775", abstract = "The paper introduces a novel approach for the automated generation of a Lyapunov function for the analysis of a given dynamic system using genetic programming (GP). Genetic programming is a branch of Genetic algorithm. It introduces the concept of GP for the automation of Lyapunov function in MATLAB used for various optimisation techniques. A Lyapunov function method used for transient stability assessment is discussed and hence discussion followed by the establishment of domain of attraction of stable equilibrium point. The results obtained by MATLAB coding for the generation of Lyapunov function of single machine infinite bus system is related by considering a ball rolling on the inner surface of a bowl which depicted in edition of Power System Analysis and Control.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICESA.2015.7503454", month = oct, notes = "Also known as \cite{7503454}", } @InProceedings{An2017aa, title = "{PyGGI}: {Python} General framework for Genetic Improvement", author = "Gabin An and Jinhan Kim and Seongmin Lee and Shin Yoo", booktitle = "Proceedings of Korea Software Congress", year = "2017", series = "KSC 2017", pages = "536--538", address = "Busan, South Korea", month = "20-22 " # dec, keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "https://coinse.kaist.ac.kr/publications/pdfs/An2017aa.pdf", URL = "http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07322214&language=en_EN#", code_url = "https://github.com/coinse/pyggi", size = "3 pages", abstract = "We present Python General Framework for Genetic Improvement (PYGGI, pronounced pigi), a lightweight general framework for Genetic Improvement (GI). It is designed to be a simple and easy to configure GI tool for multiple programming languages such as Java, C, or Python. Through two case studies, we show that PYGGI can modify source code of a given program either to improve non-functional properties or to automatically repair functional faults.", notes = "177 20A1A2-5 modification operators: deletion, copying, and replacement of source code as a list of code lines. Local search algorithm. triangle program http://www.kiise.or.kr/conference/kcc/2017/ Jan 2021 http://www.dbpia.co.kr/ in Korean only Also known as \cite{an:2017:ksc}", } @InProceedings{An:2018:GI, author = "Gabin An and Jinhan Kim and Shin Yoo", title = "Comparing Line and {AST} Granularity Level for Program Repair using {PyGGI}", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "19--26", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", isbn13 = "978-1-4503-5753-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/An_2018_GI.pdf", URL = "https://coinse.kaist.ac.kr/publications/pdfs/An2018to.pdf", DOI = "doi:10.1145/3194810.3194814", size = "8 pages", abstract = "PyGGI is a lightweight Python framework that can be used to implement generic Genetic Improvement algorithms at the API level. The original version of PyGGI only provided lexical modifications, i.e., modifications of the source code at the physical line granularity level. This paper introduces new extensions to PyGGI that enables syntactic modifications for Python code, i.e., modifications that operates at the AST granularity level. Taking advantage of the new extensions, we also present a case study that compares the lexical and syntactic search granularity level for automated program repair, using ten seeded faults in a real world open source Python project. The results show that search landscapes at the AST granularity level are more effective (i.e. eventually more likely to produce plausible patches) due to the smaller sizes of ingredient spaces (i.e., the space from which we search for the material to build a patch), but may require longer time for search because the larger number of syntactically intact candidates leads to more fitness evaluations.", notes = "Slides: http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/gi-pyggi.compressed.pdf GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @Article{An:2018:sigevolution, author = "Gabin An", title = "Genetic Improvement Workshop at {ICSE 2018}", journal = "SIGEVOlution", year = "2018", volume = "11", number = "4", pages = "11--13", month = dec, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.sigevolution.org/issues/SIGEVOlution1104.pdf", DOI = "doi:10.1145/3302542.3302544", size = "2 pages", abstract = "In Gothenburg, on 2nd June 2018, the fourth edition of Genetic Improvement (GI) Workshop was co-located with this year's ICSE (International Conference on Software Engineering), the biggest and probably the most prestigious software engineering conference...", notes = "http://geneticimprovementofsoftware.com/", } @InProceedings{an:2019:fse, author = "Gabin An and Aymeric Blot and Justyna Petke and Shin Yoo", title = "{PyGGI 2.0}: Language Independent Genetic Improvement Framework", booktitle = "Proceedings of the 27th Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering {ESEC/FSE} 2019)", year = "2019", editor = "Sven Apel and Alessandra Russo", pages = "1100--1104", address = "Tallinn, Estonia", month = aug # " 26--30", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, APR, SBSE, XML, srcML, Python", isbn13 = "9781450355728", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/an_2019_fse.pdf", URL = "https://esec-fse19.ut.ee/program/tool-demos/#", DOI = "doi:10.1145/3338906.3341184", video_url = "https://youtu.be/PxRUdlRDS40", code_url = "https://github.com/coinse/pyggi", size = "5 pages", abstract = "PyGGI is a research tool for Genetic Improvement (GI), that is designed to be versatile and easy to use. We present version 2.0 of PyGGI, the main feature of which is an XML-based intermediate program representation. It allows users to easily define GI operators and algorithms that can be reused with multiple target languages. Using the new version of PyGGI, we present two case studies. First, we conduct an Automated Program Repair (APR) experiment with the QuixBugs benchmark, one that contains defective programs in both Python and Java. Second, we replicate an existing work on runtime improvement through program specialisation for the MiniSAT satisfiability solver. PyGGI 2.0 was able to generate a patch for a bug not previously fixed by any APR tool. It was also able to achieve 14percent runtime improvement in the case of MiniSAT. The presented results show the applicability and the expressiveness of the new version of PyGGI. A video of the tool demo is at: https://youtu.be/PxRUdlRDS40", notes = "C++, C, C#, Java, Python Slides: http://crest.cs.ucl.ac.uk/fileadmin/crest/COWphotos/Other/Petke.pdf https://youtu.be/PxRUdlRDS40 4:48 minutes:seconds https://esec-fse19.ut.ee/", } @Proceedings{an:2024:GI, title = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", address = "Lisbon", month = "16 " # apr, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://geneticimprovementofsoftware.com/events/icse2024", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/an_2024_GI.pdf", DOI = "doi:10.1145/3643692", video_url = "https://youtube.com/playlist?list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S", size = "ix + 31", abstract = "The GI workshops continue to bring together researchers from across the world to exchange ideas about using optimisation techniques, particularly evolutionary computation, such as genetic programming, to improve existing software. Contents: \cite{Yoo:2024:GI} \cite{Blot:2024:GI} \cite{Baxter:2024:GI} \cite{callan:2024:GI} \cite{Craine:2024:GI} \cite{langdon:2024:GI} \cite{Nemeth:2024:GI} \cite{Sarmiento:2024:GI} See also \cite{langdon:2024:SEN} Published: 08 August 2024", } @PhdThesis{An:thesis, author = "Gabin An", title = "Synergizing Fault Localization and Continuous Integration to Streamline Bug Resolution in Large-Scale Software Systems", school = "Korea Advanced Institute of Science and Technology", year = "2024", address = "Daejeon, Korea", month = "4 " # jun, keywords = "SBSE, fault localization, SBFL, AutoFL, FONTE, Defects4J, BugsInPy, continuous integration, bug resolution, debugging, bug assignment, buginducing commit, large language model, LLN, ANN, GPT, SAP HANA, BIC", size = "101+ pages", abstract = "This thesis explores the synergistic interaction between Continuous Integration (CI) and Fault Localization (FL) within software development, aiming to enhance the efficiency and effectiveness of the bug resolution process. CI plays a critical role as developers frequently merge code changes into a central repository, followed by automated builds and tests to quickly detect bugs and maintain a unified code-base that supports effective collaboration for large-scale software systems. FL is an automated debugging technique designed to precisely detect the locations of bugs within the codebase, reducing the burden on developers. While CI and FL each aim to streamline software development and maintenance independently, their potential for interaction has not been fully explored. This research suggests that leveraging historical CI data can enable more effective application of FL, and that FL can improve the bug resolution process within CI systems. The thesis comprises three studies: identifying common root causes of test failures using diverse information sources available in the CI environment, efficiently identifying bug-inducing commits using FL and code change histories, and developing an explainable FL technique using large language models. Each study addresses specific challenges and provides novel solutions to simplify the debugging and maintenance stages of software development. The proposed solutions are empirically evaluated and thoroughly compared against their baselines using real-world open-source software and large-scale industry software", notes = "Not GP? Text in English. Supervisor Shin Yoo", } @InProceedings{Anand:2010:ICCSIT, author = "Deepa Anand and K. K. Bharadwaj", title = "Adaptive user similarity measures for recommender systems: A genetic programming approach", booktitle = "3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010)", year = "2010", month = "9-11 " # jul, volume = "8", pages = "121--125", abstract = "Recommender systems signify the shift from the paradigm of searching for items to discovering items and have been employed by an increasing number of e-commerce sites for matching users to their preferences. Collaborative Filtering is a popular recommendation technique which exploits the past user-item interactions to determine user similarity. The preferences of such similar users are leveraged to offer suggestions to the active user. Even though several techniques for similarity assessment have been suggested in literature, no technique has been proven to be optimal under all contexts/data conditions. Hence, we propose a two-stage process to assess user similarity, the first is to learn the optimal transformation function to convert the raw ratings data to preference data by employing genetic programming, and the second is to use the preference values, so derived, to compute user similarity. The application of such learnt user bias gives rise to adaptive similarity measures, i.e. similarity estimates that are dataset dependent and hence expected to work best under any data environment. We demonstrate the superiority of our proposed technique by contrasting it to traditional similarity estimation techniques on four different datasets representing varied data environments.", keywords = "genetic algorithms, genetic programming, adaptive user similarity measure, collaborative filtering, data environment, item discovery, item searching, optimal transformation function, preference value, raw ratings data, recommender system, similarity assessment, similarity estimation, user-item interaction, groupware, information filtering, recommender systems", DOI = "doi:10.1109/ICCSIT.2010.5563737", notes = "Sch. of Comput. & Syst. Sci., Jawaharlal Nehru Univ., Delhi, India Also known as \cite{5563737}", } @PhdThesis{Anand:thesis, author = "Deepa Anand", title = "Enhancing Accuracy of Recommender Systems through various approaches to Local and Global Similarity Measures", year = "2011", school = "Computer and System Sciences, Jawaharlal Nehru University", address = "New Delhi, India", month = jul, keywords = "genetic algorithms, genetic programming, recommender systems", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Anand_thesis.pdf", size = "172 pages", abstract = "Web 2.0 represents a paradigm shift in the way that internet is consumed. Users' role has evolved from that of passive consumers of content to active prosumers, implying a plethora of information sources and an ever increasing ocean of content. Collaborative Recommender systems have thus emerged as Web 2.0 personalisation tools which aid users in grappling with the overload of information by allowing the discovery of content in contrast to plain search popularised by prior web technologies. To this end Collaborative filtering (CF) exploit the preferences of users who have liked similar items in the past to help a user to identify interesting products and services. The success of CF algorithms, however, is hugely dependent on the technique designed to determine the set of users whose opinion is sought. Traditionally user closeness is assessed by matching their preferences on a set of common experiences that both share. The challenge with this kind of computation is the overabundance of available content to be experienced, at the user's disposal, thus rendering the user-preference space very sparse. The similarity so computed is thus unstable for user pairs sharing a small set of experiences and is in fact incomputable for most user pairs due to a lack of expressed common preferences. To remedy the sparsity problems we propose methods to enrich the set of user connections obtained using measures such as Pearson Correlation Coefficient (PCC) and Cosine Similarity (COS). We achieve this by leveraging on explicit trust elicitation and trust transitivity. When interacting with anonymous users online, in the absence of physical cues apparent in our daily life, trust provides a reliable measure of quality and guides the user decision process on whether or not to interact with an entity. These trust statements in addition to identifying malicious users also enhance user connectivity by establishing links between pairs of users whose closeness cannot be determined through preference data. In addition transitivity of trust can also be leveraged to further expand the set of neighbours to collaborate with. We first explore a bifurcated view of trust: functional and referral trust i.e. trust in an entity to recommend items and the trust in an entity to recommend recommenders and propose models to quantify referral trust. Such a referral-functional trust framework leads to more meaningful derivation of trust through transitivity resulting in better quality recommendations. Though trust has been extensively used in literature to support the CF process, distrust information has been explored very little in this context. We thus propose a tri-component computation of trust and distrust using preference, functional trust and referral trust in order to densify the network of user interconnections. To maintain a balance between increased coverage and the quality of recommendations, however, we quantify risk measures for each trust and distrust relationship so derived and prune the network to retain high quality relationships thus ensuring good connections formed between users through transitivity of trust and distrust. In the absence of supplemental information such as trust/distrust to provide extra knowledge about user links the local similarity connections can be harnessed to deem a pair of users similar if they are share preferences with the same set of users thus estimating the global similarity between user pairs. We investigate the effectiveness of various graph based global or indirect similarity computation schemes in enhancing the user or item neighborhood thus bettering the quality and number of recommendations obtained.", abstract = "In addition to the inadequacy of similarity measures such as PCC and VS in forming a rich user neighbourhood they are static and may not capture user matching satisfactorily and guarantee optimal performance under diverse data situations. We propose to learn similarity measures which not only adjust to the type of data at hand but also ensure optimal performance over time. Evolutionary techniques are employed to learn such adaptive similarity measures. Finally sparsity variant fusion of predictions from local and global similarity measures have been shown to offer quality recommendations. In particular the fact that local similarity measures suffice when the preference data is dense but overtaken in performance by global similarity links when preference data is scarce can be leveraged to fuse the recommendations from the two systems. We define sparsity not only for the overall system but also at the user and user-item level. We use these measures to suggest a fusion scheme tailored for each user and/or for each item to be predicted by estimating the apportionment of influence local and global similarity measures have on each prediction. We demonstrate the effectiveness of the proposed techniques through experiments performed on real world datasets.", notes = "Supvervisor: K. K. Bharadwaj. JNU", } @Article{Anand:2012:IJCSI, author = "Deepa Anand", title = "Feature Extraction for Collaborative Filtering: A Genetic Programming Approach", journal = "International Journal of Computer Science Issues", year = "2012", volume = "9", number = "5", pages = "348--354", month = sep, keywords = "genetic algorithms, genetic programming, Recommender Systems, Collaborative Filtering, Feature Extraction", publisher = "IJCSI Press", ISSN = "16940784", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:3fdb924cd5e50b8f275ce58daca88188", URL = "http://www.ijcsi.org/contents.php?volume=9&&issue=5", URL = "http://www.ijcsi.org/papers/IJCSI-9-5-1-348-354.pdf", size = "7 pages", abstract = "Collaborative filtering systems offer customised recommendations to users by exploiting the interrelationships between users and items. Users are assessed for their similarity in tastes and items preferred by similar users are offered as recommendations. However scalability and scarcity of data are the two major bottlenecks to effective recommendations. With web based RS typically having users in order of millions, timely recommendations pose a major challenge. Sparsity of ratings data also affects the quality of suggestions. To alleviate these problems we propose a genetic programming approach to feature extraction by employing GP to convert from user-item space to user-feature preference space where the feature space is much smaller than the item space. The advantage of this approach lies in the reduction of sparse high dimensional preference information into a compact and dense low dimensional preference data. The features are constructed using GP and the individuals are evolved to generate the most discriminative set of features. We compare our approach to content based feature extraction approach and demonstrate the effectiveness of the GP approach in generating the optimal feature set.", } @Article{ANASTASOPOULOS:2021:SoftwareX, author = "Nikolaos Anastasopoulos and Ioannis G. Tsoulos and Alexandros Tzallas", title = "{GenClass:} A parallel tool for data classification based on Grammatical Evolution", journal = "SoftwareX", volume = "16", pages = "100830", year = "2021", ISSN = "2352-7110", DOI = "doi:10.1016/j.softx.2021.100830", URL = "https://www.sciencedirect.com/science/article/pii/S2352711021001199", keywords = "genetic algorithms, genetic programming, Data classification, Grammatical evolution, Stochastic methods", abstract = "A genetic programming tool is proposed here for data classification. The tool is based on Grammatical Evolution technique and it is designed to exploit multicore computing systems using the OpenMP library. The tool constructs classification programs in a C-like programming language in order to classify the input data, producing simple if-else rules and upon termination the tool produces the classification rules in C and Python files. Also, the user can use his own Backus Normal Form (BNF) grammar through a command line option. The tool is tested on a wide range of classification problems and the produced results are compared against traditional techniques for data classification, yielding very promising results", } @Misc{DBLP:journals/corr/abs-2012-03527, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zlatan Car", title = "Estimation of Gas Turbine Shaft Torque and Fuel Flow of a {CODLAG} Propulsion System Using Genetic Programming Algorithm", howpublished = "arXiv", volume = "abs/2012.03527", year = "2020", month = "7 " # dec, keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Combined Diesel-Electric and Gas Propulsion System, Genetic Programming Algorithm, Gas Turbine Shaft Torque Estimation, Fuel Flow Estimation", eprinttype = "arXiv", eprint = "2012.03527", timestamp = "Wed, 09 Dec 2020 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2012-03527.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://arxiv.org/abs/2012.03527", size = "25 pages", abstract = "he publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been used to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively.", notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{DBLP:journals/hij/AndelicSLMC21, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Vedran Mrzljak and Zlatan Car", title = "Estimation of {COVID-19} epidemic curves using genetic programming algorithm", journal = "Health Informatics J.", volume = "27", number = "1", pages = "146045822097672", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1177/1460458220976728", DOI = "doi:10.1177/1460458220976728", timestamp = "Sun, 25 Jul 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/hij/AndelicSLMC21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{andelic:2021:IJERPH, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zdravko Jurilj and Tijana Sustersic and Andela Blagojevic and Alen Protic and Tomislav Cabov and Nenad Filipovic and Zlatan Car", title = "Estimation of {COVID-19} Epidemiology Curve of the United States Using Genetic Programming Algorithm", journal = "International Journal of Environmental Research and Public Health", year = "2021", volume = "18", number = "3", keywords = "genetic algorithms, genetic programming", ISSN = "1660-4601", URL = "https://www.mdpi.com/1660-4601/18/3/959", DOI = "doi:10.3390/ijerph18030959", abstract = "Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is used to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is used on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.", notes = "also known as \cite{ijerph18030959}", } @Article{andelic:2021:JMSE, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Igor Poljak and Vedran Mrzljak and Zlatan Car", title = "Use of Genetic Programming for the Estimation of {CODLAG} Propulsion System Parameters", journal = "Journal of Marine Science and Engineering", year = "2021", volume = "9", number = "6", keywords = "genetic algorithms, genetic programming", ISSN = "2077-1312", URL = "https://www.mdpi.com/2077-1312/9/6/612", DOI = "doi:10.3390/jmse9060612", abstract = "In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque using genetic programming (GP) algorithm. The dataset consists of 11,934 samples that were divided into training and testing portions in an 80:20 ratio. The training portion of the dataset which consisted of 9548 samples was used to train the GP algorithm to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller, port propeller, and total propeller torque, respectively. After the symbolic expressions were obtained the testing portion of the dataset which consisted of 2386 samples was used to measure estimation performance in terms of coefficient of correlation (R2) and Mean Absolute Error (MAE) metric, respectively. Based on the estimation performance in each case three best symbolic expressions were selected with and without decay state coefficients. From the conducted investigation, the highest R2 and lowest MAE values were achieved with symbolic expressions for the estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque without decay state coefficients while symbolic expressions with decay state coefficients have slightly lower estimation performance.", notes = "also known as \cite{jmse9060612}", } @InProceedings{Andelic:2022:SICAAI, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Matko Glucina and Jelena Musulin and Daniel Stifanic and Zlatan Car", title = "Utilization of Genetic Programming for Estimation of Molecular Structures Ground State Energies", booktitle = "1st Serbian International Conference on Applied Artificial Intelligence", year = "2022", address = "Kragujevac, Serbia", month = may # " 19-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, CHNOPS dataset, ground state energies", URL = "http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip", size = "4 pages", abstract = "GP to predict ground-state energies of molecules made up of C, H, N, O, P, and S (CHONPS) atoms. The GP was trained and tested on a publicly available dataset which consist of 16242 molecules where ground state energies were computed using the density functional theory (DFT). The optimal parameters of GP were chosen using the random parameter search method. After multiple GP executions, the best symbolic expression was chosen using a coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE). The best symbolic expression achieved R, MAE, and RMSE of 0.9434, 0.48, and 0.86, respectively.", notes = "http://www.aai2022.kg.ac.rs/aai-2022-papers/ SICAAI proceedings published by Springer in 2023 doi:10.1007/978-3-031-29717-5", } @Article{Andelic:2022:FI, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Matko Glucina", title = "Detection of Malicious Websites Using Symbolic Classifier", journal = "Future Internet", year = "2022", volume = "14", number = "12", pages = "Article no 358", month = nov, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, malicious websites, oversampling methods, symbolic classifier, undersampling methods", ISSN = "1999-5903", publisher = "MDPI", URL = "https://www.mdpi.com/1999-5903/14/12/358", DOI = "doi:10.3390/fi14120358", size = "30 pages", abstract = "Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. a novel approach is proposed which consists of the implementation of the genetic programming symbolic classifier (GPSC) algorithm on a publicly available dataset to obtain a simple symbolic expression (mathematical equation) which could detect malicious websites with high classification accuracy. Due to a large imbalance of classes in the initial dataset, several data sampling methods (random under-sampling/oversampling, ADASYN, SMOTE, BorderlineSMOTE, and KmeansSMOTE) were used to balance the dataset classes. For this investigation, the hyper-parameter search method was developed to find the combination of GPSC hyperparameters with which high classification accuracy could be achieved. The first investigation was conducted using GPSC with a random hyperparameter search method and each dataset variation was divided on a train and test dataset in a ratio of 70:30. To evaluate each symbolic expression, the performance of each symbolic expression was measured on the train and test dataset and the mean and standard deviation values of accuracy (ACC), AUC, precision, recall and f1-score were obtained. The second investigation was also conducted using GPSC with the random hyperparameter search method; however, 70percent, i.e., the train dataset, was used to perform 5-fold cross-validation. If the mean accuracy, AUC, precision, recall, and f1-score values were above 0.97 then final training and testing (train/test 70:30) were performed with GPSC with the same randomly chosen hyperparameters used in a 5-fold cross-validation process and the final mean and standard deviation values of the aforementioned evaluation methods were obtained. In both investigations, the best symbolic expression was obtained in the case where the dataset balanced with the KMeansSMOTE method was used for training and testing. The best symbolic expression obtained using GPSC with the random hyperparameter search method and classic train釦est procedure (70:30) on a dataset balanced with the KMeansSMOTE method achieved values of", notes = "Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia", } @Article{Andelic:2022:Sensors, author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zlatan Car", title = "The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data", journal = "Sensors", year = "2022", volume = "23", number = "1", pages = "Article no 169", month = dec, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, symbolic classifier, fire-alarm, oversampling methods, undersampling methods", publisher = "MDPI", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/23/1/169", DOI = "doi:10.3390/s23010169", size = "27 pages", abstract = "Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fire alarm systems (heat, smoke, flame, and fire gas detectors) are used in commercial, industrial and municipal areas. However, in addition to smoke, heat, infrared, ultraviolet light radiation, or gas, other parameters could indicate a fire, such as air temperature, air pressure, and humidity, among others. Collecting these parameters requires the development of a sensor fusion system. However, with such a system, it is necessary to develop a simple system based on artificial intelligence (AI) that will be able to detect fire with high accuracy using the information collected from the sensor fusion system. The novelty of this paper is to show the procedure of how a simple AI system can be created in form of symbolic expression obtained with a genetic programming symbolic classifier (GPSC) algorithm and can be used as an additional tool to detect fire with high classification accuracy. Since the investigation is based on an initially imbalanced and publicly available dataset (high number of samples classified as 1-Fire Alarm and small number of samples 0-No Fire Alarm), the idea is to implement various balancing methods such as random undersampling/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. The obtained balanced datasets were used in GPSC with random hyperparameter search combined with 5-fold cross-validation to obtain symbolic expressions that could detect fire with high classification accuracy. For this investigation, the random hyper-parameter search method and 5-fold cross-validation had to be developed. Each obtained symbolic expression was evaluated on train and test datasets to obtain mean and standard deviation values of accuracy (ACC ), area under the receiver operating characteristic curve (AUC , respectively. The symbolic expression using which best values of classification metrics were achieved is shown, and the final evaluation was performed on the original dataset.", } @Article{Andelic:2022:applsci, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm", journal = "Applied Sciences", year = "2022", volume = "13", number = "1", pages = "Article no 574", month = dec, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, ADASYN, borderline SMOTE, genetic programming-symbolic classifier, Hepatitis C, fibrosis, cirrhosis, SMOTE", publisher = "MDPI", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/1/574", DOI = "doi:10.3390/app13010574", size = "33 pages", abstract = "Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are ACC For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset.", } @Article{Andelic:2023:Machines, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier", journal = "Machines", year = "2023", volume = "11", number = "1", pages = "Article no 105", month = jan, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, classification of robot movement, oversampling methods, symbolic classifier, ultrasound sensors", publisher = "MDPI", ISSN = "2075-1702", URL = "https://www.mdpi.com/2075-1702/11/1/105", DOI = "doi:10.3390/machines11010105", size = "35 pages", abstract = "The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC. respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.", } @Article{Andelic:2023:applsci, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier", journal = "Applied Sciences", year = "2023", volume = "13", number = "3", pages = "Article no 1962", month = feb, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, oversampling methods, robot fault operation, random oversampling, symbolic classifier, SMOTE", publisher = "MDPI", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/3/1962", DOI = "doi:10.3390/app13031962", size = "23 pages", abstract = "In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of hyperparameter values and trained using a 5-fold cross-validation process is proposed to determine expressions for fault detection during robotic manipulator operation, using a dataset that was made publicly available by the original researchers. The original dataset was reduced to a binary dataset (fault vs. normal operation); however, due to the class imbalance random oversampling, and SMOTE methods were applied. The quality of best symbolic expressions (SEs) was based on the highest mean values of accuracy (ACC...are equal to 0.9978, 0.998, 1.0, 0.997, and 0.998, respectively. The investigation showed that using the described procedure, symbolically expressed models of a high classification performance are obtained for the purpose of detecting faults in the operation of robotic manipulators.", } @Article{Andelic:2023:applsci2, author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car", title = "Estimation of Interaction Locations in Super Cryogenic Dark Matter Search Detectors Using Genetic Programming-Symbolic Regression Method", journal = "Applied Sciences", year = "2023", volume = "13", number = "4", pages = "Article no 2059", month = feb, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, cross-validation, interaction location, SuperCDMS, symbolic regression", publisher = "MDPI", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/4/2059", DOI = "doi:10.3390/app13042059", size = "23 pages", abstract = "The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs) candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector; however, there are many other interactions (background interactions). To separate background interactions from the signal, it is necessary to measure the interaction energy and to reconstruct the location of the interaction between WIMPs and the nuclei. In recent years, some research papers have been investigating the reconstruction of interaction locations using artificial intelligence (AI) methods. In this paper, a genetic programming-symbolic regression (GPSR), with randomly tuned hyperparameters cross-validated via a five-fold procedure, was applied to the SuperCDMS experiment to estimate the interaction locations with high accuracy. To measure the estimation accuracy of obtaining the SEs, the mean and standard deviation (σ) values of R2, the root-mean-squared error (RMSE), and finally, the mean absolute error (MAE) were used. The investigation showed that using GPSR, SEs can be obtained that estimate the interaction locations with high accuracy. To improve the solution, the five best SEs were combined from the three best cases. The results demonstrated that a very high estimation accuracy can be achieved with the proposed methodology.", } @Article{Andelic:2023:Cancers, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Development of Symbolic Expressions Ensemble for Breast Cancer Type Classification Using Genetic Programming Symbolic Classifier and Decision Tree Classifier", journal = "Cancers", year = "2023", volume = "15", number = "13", pages = "article no. 3411", month = "29 " # jun, email = "nandelic@riteh.hr", keywords = "genetic algorithms, genetic programming, PCA, breast cancer, genetic programming symbolic classifier, 5-fold cross validation, random hyperparameter value search", ISSN = "2072-6694", URL = "https://www.mdpi.com/2072-6694/15/13/3411", DOI = "doi:10.3390/cancers15133411", size = "27 pages", abstract = "Breast cancer is a type of cancer with several sub-types. It occurs when cells in breast tissue grow out of control. The accurate sub-type classification of a patient diagnosed with breast cancer is mandatory for the application of proper treatment. Breast cancer classification based on gene expression is challenging even for artificial intelligence (AI) due to the large number of gene expressions. The idea in this paper is to use genetic programming symbolic classifier (GPSC) on the publicly available dataset to obtain a set of symbolic expressions (SEs) that can classify the breast cancer sub-type using gene expressions with high classification accuracy. The initial problem with the used dataset is a large number of input variables (54676 gene expressions), a small number of dataset samples (151 samples), and six classes of breast cancer sub-types that are highly imbalanced. The large number of input variables is solved with principal component analysis (PCA), while the small number of samples and the large imbalance between class samples are solved with the application of different oversampling methods generating different dataset variations. On each oversampled dataset, the GPSC with random hyperparameter values search (RHVS) method is trained using 5-fold cross validation (5CV) to obtain a set of SEs. The best set of SEs is chosen based on mean values of accuracy (ACC), the area under the receiving operating characteristic curve (AUC), precision, recall, and F1-score values. In this case, the highest classification accuracy is equal to 0.992 across all evaluation metric methods. The best set of SEs is additionally combined with a decision tree classifier, which slightly improves ACC to 0.994.", notes = "Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{Andelic:2023:Computers, author = "Nikola Andelic and Sandi {Baressi Segota} and Zlatan Car", title = "Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques", journal = "Computers", year = "2023", volume = "12", number = "12", pages = "242", email = "nikola.andjelic@uniri.hr", keywords = "genetic algorithms, genetic programming, genetic programming symbolic classifier, 5-fold cross-validation, malware software detection, oversampling techniques, random hyperparameter value search method", ISSN = "2073-431X", URL = "https://www.mdpi.com/2073-431X/12/12/242", DOI = "doi:10.3390/computers12120242", size = "19 pages", abstract = "Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were used to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.", notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{andelic:2023:Technologies, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Generating Mathematical Expressions for Estimation of Atomic Coordinates of Carbon Nanotubes Using Genetic Programming Symbolic Regression", journal = "Technologies", year = "2023", volume = "11", number = "6", pages = "Article No. 185", keywords = "genetic algorithms, genetic programming", ISSN = "2227-7080", URL = "https://www.mdpi.com/2227-7080/11/6/185", DOI = "doi:10.3390/technologies11060185", abstract = "The study addresses the formidable challenge of calculating atomic coordinates for carbon nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days. To tackle this issue, the research leverages the Genetic Programming Symbolic Regression (GPSR) method on a publicly available dataset. The primary aim is to assess if the resulting Mathematical Equations (MEs) from GPSR can accurately estimate calculated atomic coordinates obtained through DFT. Given the numerous hyperparameters in GPSR, a Random Hyperparameter Value Search (RHVS) method is devised to pinpoint the optimal combination of hyperparameter values, maximizing estimation accuracy. Two distinct approaches are considered. The first involves applying GPSR to estimate calculated coordinates (uc, vc, wc) using all input variables (initial atomic coordinates u, v, w, and integers n, m specifying the chiral vector). The second approach applies GPSR to estimate each calculated atomic coordinate using integers n and m alongside the corresponding initial atomic coordinates. This results in the creation of six different dataset variations. The GPSR algorithm undergoes training via a 5-fold cross-validation process. The evaluation metrics include the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and the depth and length of generated MEs. The findings from this approach demonstrate that GPSR can effectively estimate CNT atomic coordinates with high accuracy, as indicated by an impressive R2?1.0. This study not only contributes to the advancement of accurate estimation techniques for atomic coordinates but also introduces a systematic approach for optimising hyperparameters in GPSR, showcasing its potential for broader applications in materials science and computational chemistry.", notes = "also known as \cite{technologies11060185}", } @Article{ANDELIC:2024:engappai, author = "Nikola Anelic and Ivan Lorencin and Vedran Mrzljak and Zlatan Car", title = "On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm", journal = "Engineering Applications of Artificial Intelligence", volume = "133", pages = "108213", year = "2024", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2024.108213", URL = "https://www.sciencedirect.com/science/article/pii/S0952197624003713", keywords = "genetic algorithms, genetic programming, Averaging ensemble, Bland-Altman analysis, Combined cycle power plant, Random hyperparameter values search method", abstract = "This paper focuses on the estimation of electrical power output (Pe) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy Pe. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Kling-Gupta Efficiency (KGE), and Bland-Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE YVE-2 achieves the highest (Pe) accuracy, including R2=0.9368, MAE=3.3378, MSE=18.4800, RMSE=4.2985, MAPE=0.7354percent, and KGE=0.9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating Pe in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology", } @Article{ANDELIC:2024:ascom, author = "N. Anelic", title = "Improvement of pulsars detection using dataset balancing methods and symbolic classification ensemble", journal = "Astronomy and Computing", volume = "47", pages = "100801", year = "2024", ISSN = "2213-1337", DOI = "doi:10.1016/j.ascom.2024.100801", URL = "https://www.sciencedirect.com/science/article/pii/S2213133724000167", keywords = "genetic algorithms, genetic programming, Dataset balancing methods, Genetic programming symbolic classifier, Pulsars detection", abstract = "Highly accurate detection of pulsars is mandatory. With the application of machine learning (ML) algorithms, the detection of pulsars can certainly be improved if the dataset is balanced. In this paper, the publicly available dataset (HTRU2) is highly imbalanced so various balancing methods were applied. The balanced dataset was used in genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect pulsars with high classification accuracy. To find the optimal combination of GPSC hyperparameters the random hyperparameter search (RHS) method was developed and applied. The GPSC was trained using 5-fold cross-validation so after each training a total of 5 SEs were obtained. The best set of SEs are selected based on their classification performance and all of them are applied on the original dataset. The best classification accuracy (ACC), the area under receiver operating characteristic (AUC), precision, recall, and f1-score were achieved in the case of the dataset balanced with the AllKNN method i.e. all mean evaluation metric values are equal to 0.995. The ensemble consisted of 25 SEs that achieved the ACC=0.978, AUC=0.9452 , Precision=0.905, Recall=0.9963, and F1-Score=0.94877, on the original dataset", } @Article{andelic:2024:Information, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Enhancing Network Intrusion Detection: A Genetic Programming Symbolic Classifier Approach", journal = "Information", year = "2024", volume = "15", number = "3", pages = "Article No. 154", keywords = "genetic algorithms, genetic programming", ISSN = "2078-2489", URL = "https://www.mdpi.com/2078-2489/15/3/154", DOI = "doi:10.3390/info15030154", abstract = "This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of this study is to leverage a publicly available dataset, employing a Genetic Programming Symbolic Classifier (GPSC) to derive symbolic expressions (SEs) endowed with the capacity for exceedingly precise network intrusion detection. In order to augment the classification precision of the SEs, a pioneering Random Hyperparameter Value Search (RHVS) methodology was conceptualized and implemented to discern the optimal combination of GPSC hyperparameter values. The GPSC underwent training via a robust five-fold cross-validation regimen, mitigating class imbalances within the initial dataset through the application of diverse oversampling techniques, thereby engendering balanced dataset iterations. Subsequent to the acquisition of SEs, the identification of the optimal set ensued, predicated upon metrics inclusive of accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The selected SEs were subsequently subjected to rigorous testing on the original imbalanced dataset. The empirical findings of this research underscore the efficacy of the proposed methodology, with the derived symbolic expressions attaining an impressive classification accuracy of 0.9945. If the accuracy achieved in this research is compared to the average state-of-the-art accuracy, the accuracy obtained in this research represents the improvement of approximately 3.78percent. In summation, this investigation contributes salient insights into the efficacious deployment of GPSC and RHVS for the meticulous detection of network intrusions, thereby accentuating the potential for the establishment of resilient cybersecurity defenses.", notes = "also known as \cite{info15030154}", } @Article{Andelic:2024:Electronics, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "An Advanced Methodology for Crystal System Detection in Li-Ion Batteries", journal = "Electronics", year = "2024", volume = "13", number = "12", pages = "article number: 2278", month = jun, keywords = "genetic algorithms, genetic programming, crystal structure, genetic programming symbolic classifier, Lithium batteries, oversampling techniques, random hyperparameter value search method", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/13/12/2278", DOI = "doi:10.3390/electronics13122278", size = "32 pages", abstract = "Detecting the crystal system of lithium-ion batteries is crucial for optimising their performance and safety. Understanding the arrangement of atoms or ions within the battery electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was used to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0).", notes = "Also known as \cite{electronics13122278} ECML/PKDD2003 Discovery Challenge, Industrial Electronics Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{andelic:IJIS, author = "Nikola Andelic and Sandi {Baressi Segota} and Zlatan Car", title = "Robust password security: a genetic programming approach with imbalanced dataset handling", journal = "International Journal of Information Security", year = "2024", volume = "23", number = "3", pages = "1761--1786", month = jun, keywords = "genetic algorithms, genetic programming, Genetic programming symbolic classifier, Random hyperparameter value search method, Fivefold cross-validation, Oversampling and undersampling techniques, Password strength classification", ISSN = "1615-5262", URL = "http://link.springer.com/article/10.1007/s10207-024-00814-2", DOI = "doi:10.1007/s10207-024-00814-2", size = "26 pages", notes = "Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia", } @Article{Andelic:2024:Computers, author = "Nikola Andelic and Sandi {Baressi Segota}", title = "Achieving High Accuracy in Android Malware Detection through Genetic Programming Symbolic Classifier", journal = "Computers", year = "2024", volume = "13", number = "8", pages = "article number: 197", month = aug, keywords = "genetic algorithms, genetic programming, Android malware detection, genetic programming symbolic classifier, oversampling techniques, random hyperparameters values method", ISSN = "2073-431X", URL = "https://www.mdpi.com/2073-431X/13/8/197/pdf?version=1723704936", URL = "https://www.mdpi.com/2073-431X/13/8/197", DOI = "doi:10.3390/computers13080197", size = "31 pages", abstract = "The detection of Android malware is of paramount importance for safeguarding users personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, thereby preventing unauthorized access and potential damage. Moreover, effective malware detection is essential for maintaining device performance and reliability by mitigating the risks posed by malicious software. This paper introduces a novel approach to Android malware detection, leveraging a publicly available dataset in conjunction with a Genetic Programming Symbolic Classifier (GPSC). The primary objective is to generate symbolic expressions (SEs) that can accurately identify malware with high precision. To address the challenge of imbalanced class distribution within the dataset, various oversampling techniques are employed. Optimal hyperparameter configurations for GPSC are determined through a random hyperparameter values search (RHVS) method developed in this research. The GPSC model is trained using a 10-fold cross-validation (10FCV) technique, producing a set of 10 SEs for each dataset variation. Subsequently, the most effective SEs are integrated into a threshold-based voting ensemble (TBVE) system, which is then evaluated on the original dataset. The proposed methodology achieves a maximum accuracy of 0.956, thereby demonstrating its effectiveness for Android malware detection.", notes = "Also known as \cite{computers13080197} Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia", } @Article{DBLP:journals/peerj-cs/AndersI19, author = "Torsten Anders and Benjamin Inden", title = "Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina", journal = "PeerJ Comput. Sci.", volume = "5", pages = "e244", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.7717/peerj-cs.244", DOI = "doi:10.7717/peerj-cs.244", timestamp = "Tue, 16 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/peerj-cs/AndersI19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/peerjpre/AndersI19, author = "Torsten Anders and Benjamin Inden", title = "Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina", journal = "PeerJ Prepr.", volume = "7", pages = "e27731", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.7287/peerj.preprints.27731v1", DOI = "doi:10.7287/peerj.preprints.27731v1", timestamp = "Thu, 09 Jul 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/peerjpre/AndersI19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Andersen:2021:CEC, author = "Hayden Andersen and Andrew Lensen and Bing Xue", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms", year = "2021", editor = "Yew-Soon Ong", pages = "688--695", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "Clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-based (dis)similarity functions such as Euclidean Distance. However, these and most other predefined dissimilarity functions can be rather inflexible by considering each feature equally and not properly capturing feature interactions in the data. Genetic Programming is an evolutionary computation approach that evolves programs in an iterative process that naturally lends itself to the evolution of functions. This paper introduces a novel framework to automatically evolve dissimilarity measures for a provided clustering dataset and algorithm. The results show that the evolved functions create clusters exhibiting high measures of cluster quality.", keywords = "genetic algorithms, genetic programming, Measurement, Clustering methods, Clustering algorithms, Evolutionary computation, Euclidean distance, Iterative methods, Clustering, Similarity Function, Feature Selection", DOI = "doi:10.1109/CEC45853.2021.9504855", notes = "Also known as \cite{9504855}", } @InProceedings{andersen:2024:GECCOcomp, author = "Hayden Andersen and Andrew Lensen and Will Browne and Yi Mei", title = "Intepretable Local Explanations Through Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin", pages = "247--250", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, explainable AI, XAI, machine learning, Evolutionary Machine Learning: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654370", size = "4 pages", abstract = "As machine learning models become increasingly prevalent in everyday life, there is a growing demand for explanation of the predictions generated by these models. However, most models used by companies are black-boxes in nature, without the capacity to provide explanations to users. This reduces public trust in these models, and exists as a barrier to adoption of machine learning. Research into providing explanations to users has shown that local explanation techniques provide more acceptable explanations to users than attempting to explain an entire model, as a user often does not need to understand the entirety of a model.This work builds on prior work in the field to produce a competitive method for high-fidelity local explanations using genetic programming. Two different data representations targeted towards both users with and without machine learning experience are evaluated.The experimental results show comparable fidelity to the state-of-the art, while exhibiting more comprehensible explanations due to including fewer features in each explanation. The method enables decomposable explanations that are easy to interpret, while still capturing non-linear relationships in the original model.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Anderson:2022:GI, author = "Damien Anderson and Paul Harvey and Yusaku Kaneta and Petros Papadopoulos and Philip Rodgers and Marc Roper", title = "Towards evolution-based autonomy in large-scale systems", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1924--1925", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, SBFL, Internet based content delivery network, CDN, 5G, grammar, Varnish Configuration Language, VCL", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Anderson_2022_GI.pdf", DOI = "doi:10.1145/3520304.3533975", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/anderson-towards-evolutionary-based-autonomy-gi-gecco-22.pdf", video_url = "https://www.youtube.com/watch?v=xpKcZRsRgrQ&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=5", size = "2 pages", abstract = "To achieve truly autonomous behaviour systems need to adapt to not only previously unseen circumstances but also previously unimagined ones. A hierarchical evolutionary system is proposed which is capable of displaying the emergent behaviour necessary to achieve this goal. we report the practical challenges encountered in implementing this proposed approach in a large-scale open-source system.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 digital twin Meta-Evolutionary Controllers Varnish https://www.varnish-software.com/solutions/cdn http://paul-harvey.org/publication/towards-evolution-based-autonomy-in-large-scale-systems/ GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{anderson:ppsn2002:pp689, author = "Eike Falk Anderson", title = "Off-Line Evolution of Behaviour for Autonomous Agents in Real-Time Computer Games", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "689--699", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Games, Machine Learning, Fitness Evaluation", ISBN = "3-540-44139-5", DOI = "doi:10.1007/3-540-45712-7_66", abstract = "This paper describes and analyses a series of experiments intended to evolve a player for a variation of the classic arcade game Asteroids TM using steady state genetic programming. The player's behaviour is defined using a LISP like scripting language. While the game interprets scripts in real-time, such scripts are evolved off-line by a second program which simulates the realtime application. This method is used, as on-line evolution of the players would be too time consuming. A successful player needs to satisfy multiple conflicting objectives. This problem is addressed by the use of an automatically defined function (ADF) for each of these objectives in combination with task specific fitness functions. The overall fitness of evolved scripts is evaluated by a conventional fitness function. In addition to that, each of the ADFs is evaluated with a separate fitness function, tailored specifically to the objective that needs to be satisfied by that ADF.", } @TechReport{anderson:1994:profile, author = "Kenneth R. Anderson", title = "Courage in Profiling", institution = "BBN", year = "1994", month = "28 " # jul, keywords = "genetic algorithms, genetic programming, CASCOR1", URL = "http://openmap.bbn.com/~kanderso/performance/postscript/courage-in-profiles.ps", notes = "Compares speed of GP systems written in C (Tackett's SGPC which uses a tree representation) and Lisp (John Koza) on a symbolic regression problem, Optimised lisp performs better than expected. Koza lisp GP code performance improved 30 fold by use of profiling. Software: ftp://openmap.bbn.com/pub/kanderson/faster94/faster94/courage/koza3.lisp 2. You can easily convert your eval into a closure compiler: Paper: http://www.iro.umontreal.ca/~feeley/papers/complang87.ps.gz", } @InProceedings{andersson:1999:rmbGPrc, author = "Bjorn Andersson and Per Svensson and Peter Nordin and Mats Nordahl", title = "Reactive and Memory-Based Genetic Programming for Robot Control", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "161--172", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_13", abstract = "In this paper we introduce a new approach to genetic programming with memory in reinforcement learning situations, which selects memories in order to increase the probability of modelling the most relevant parts of memory space. We evolve maps directly from state to action, rather than maps that predict reward based on state and action, which reduces the complexity of the evolved mappings. The work is motivated by applications to the control of autonomous robots. Preliminary results in software simulations indicate an enhanced learning speed and quality.", notes = "EuroGP'99, part of \cite{poli:1999:GP} AIMGP machine code GP, memory, simulated robot", } @InProceedings{andersson:2000:4lrGP, author = "Bjorn Andersson and Per Svensson and Peter Nordin and Mats Nordahl", title = "On-line Evolution of Control for a Four-Legged Robot Using Genetic Programming", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "319--326", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, linear GP", ISBN = "3-540-67353-9", DOI = "doi:10.1007/3-540-45561-2_31", size = "8 pages", abstract = "We evolve a robotic controller for a four-legged real robot enabling it to walk dynamically. Evolution is performed on-line by a linear machine code GP system. The robot has eight degrees of freedom and is built from standard R/C servos. Different walking strategies are shown by the robot during evolution and the evolving system is robust against mechanical failures.", notes = "{"}Galloping only appears after many hours of training{"} p323. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61", } @InProceedings{Andersson:1998:ecmlc, author = "Claes Andersson and Mats G. Nordahl", title = "Evolving Coupled Map Lattices for Computation", booktitle = "Proceedings of the First European Workshop on Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", pages = "151--162", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055935", abstract = "Genetic Programming is used to evolve coupled map lattices for density classification. The most successful evolved rules depending only on nearest neighbors (r=1) show better performance than existing r=3 cellular automaton rules on this task.", notes = "EuroGP'98", affiliation = "Chalmers University of Technology Institute of Theoretical Physics S-412 96 Goteborg Sweden S-412 96 Goteborg Sweden", } @Misc{oai:CiteSeerPSU:491253, title = "The Rolling Stones - Genetic Programming in {AIP}", author = "Thord Andersson and Per-Erik Forssen", year = "2000", month = mar # "~06", abstract = "This report describes the design of a soccer playing agent developed in the scope of the AI Programming course. This agent uses a variant of the subsumption architecture [2]. The primitive behaviours that dene the intelligence of the agent are evolved using genetic programming [4]. We chose the genetic-programming approach instead of designs such as decision trees etc, since we wanted the intelligence in the agents to be truly articial, and not designed", citeseer-isreferencedby = "oai:CiteSeerPSU:76065", citeseer-references = "oai:CiteSeerPSU:107311; oai:CiteSeerPSU:125377", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:491253", rights = "unrestricted", URL = "http://www.ida.liu.se/~silco/AIP/Rolling-Stones.ps", URL = "http://citeseer.ist.psu.edu/491253.html", note = "student project", keywords = "genetic algorithms, genetic programming", size = "13 pages", } @InProceedings{ando:evows07, author = "Daichi Ando and Palle Dahlsted and Mats Nordahl and Hitoshi Iba", title = "Interactive GP with Tree Representation of Classical Music Pieces", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "577--584", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_63", abstract = "Research on the application of Interactive Evolutionary Computation(IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, we purpose a new IEC approach to music composition based on classical music theory. In this paper, we describe an established system according to the above idea, and detail of making success of composition a piece.", notes = "EvoWorkshops2007", } @InProceedings{Ando:2007:cec, author = "Daichi Ando and Hitoshi Iba", title = "Interactive Composition Aid System by Means of Tree Representation of Musical Phrase", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "4258--4265", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1814.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4425027", abstract = "Research on the application of Interactive Evolutionary Computation (IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, the authors purpose a new IEC approach to music composition based on classical music theory. In this paper, the authors describe an established system according to the above idea, and detail of making success of composition a piece.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{conf/icmc/Ando14, author = "Daichi Ando", title = "Real-time Breeding Composition System by means of Genetic Programming and Breeding Procedure", booktitle = "Proceedings International Computer Music Conference Proceedings SMC 2014", year = "2014", address = "Athens, Greece", month = "14-20 " # sep, publisher = "Michigan Publishing", keywords = "genetic algorithms, genetic programming", bibdate = "2016-01-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icmc/icmc2014.html#Ando14", URL = "http://hdl.handle.net/2027/spo.bbp2372.2014.062", URL = "http://quod.lib.umich.edu/cgi/p/pod/dod-idx/real-time-breeding-composition-system.pdf", URL = "http://quod.lib.umich.edu/i/icmc/bbp2372.2014", URL = "http://quod.lib.umich.edu/i/icmc/bbp2372.2014.062/--real-time-breeding-composition-system", size = "6 pages", abstract = "The use of laptop computers to produce real-time music and multimedia performances has increased significantly in recent years. In this paper, I propose a new method of generating club-style loop music in real time by means of interactive evolutionary computation (IEC). The method includes two features. The first is the concept of breeding without any consciousness of generation. The second is a multiple-ontogeny mechanism that generates several phenotypes from one genotype, incorporating ideas of co-evolution and multi-objective optimisation. The proposed method overcomes certain limitations of IEC, namely the burden of interactive evaluation and the narrow search domain resulting from handling few individuals. A performance system that generates club-style loop music from the photo album in mobile devices is implemented by means of the proposed method. This system is then tested, and the success of performances with the implemented system indicates that the proposed methods work effectively.", notes = "ICMC 2014", } @InProceedings{Ando:2009:ieeeSMC, author = "Jun Ando and Tomoharu Nagao", title = "Image classification and processing using modified parallel-ACTIT", booktitle = "IEEE International Conference on Systems, Man and Cybernetics, SMC 2009", year = "2009", month = oct, pages = "1787--1791", keywords = "genetic algorithms, genetic programming, automatic construction of tree-structural image transformation, image classification, image recognition, modified parallel-ACTIT, training image sets, image classification, tree data structures", DOI = "doi:10.1109/ICSMC.2009.5346894", ISSN = "1062-922X", abstract = "Image processing and recognition technologies are required to solve various problems. We have already proposed the system which automatically constructs image processing with Genetic Programming (GP), Automatic Construction of Tree-structural Image Transformation (ACTIT). However, it is necessary that training image sets are properly classified in advance if they have various characteristics. In this paper, we propose Modified Parallel-ACTIT which automatically classifies training image sets into several subpopulations. And it optimizes tree-structural image transformation for each training image sets in each subpopulations. We show experimentally that Modified Parallel-ACTIT is more effective in comparison with ordinary ACTIT.", notes = "Also known as \cite{5346894}", } @InProceedings{ando:2002:mgnbhg, author = "Shin Ando and Hitoshi Iba and Erina Sakamoto", title = "Modeling Genetic Network by Hybrid GP", booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002", editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton", pages = "291--296", year = "2002", publisher = "IEEE Press", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", ISBN = "0-7803-7278-6", month = "12-17 " # may, notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)", keywords = "genetic algorithms, genetic programming, artificial data, differential equations, evolutionary modelling method, genetic regulatory network modeling, hybrid algorithm, hybrid genetic programming, least mean square method, multiple runs, real world data, regulation, statistical analysis, time series, differential equations, least mean squares methods, statistical analysis", URL = "http://citeseer.ist.psu.edu/520794.html", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/17336/http:zSzzSzwww.miv.t.u-tokyo.ac.jpzSz~ibazSztmpzSzando.pdf/modeling-genetic-network-by.pdf", DOI = "doi:10.1109/CEC.2002.1006249", abstract = "We present an Evolutionary Modelling method for modeling genetic regulatory networks. The method features hybrid algorithm of Genetic Programming with statistical analysis to derive systems of differential equations. Genetic Programming and Least Mean Square method were combined to identify a concise form of regulation between the variables from a given set of time series. Also, results of multiple runs were statistically analysed to indicate the term with robust and significant influence. Our approach was evaluated in artificial data and real world data.", notes = "oai:CiteSeerPSU:520794", size = "6 pages", } @Article{ando:emi, author = "Shin Ando and Erina Sakamoto and Hitoshi Iba", title = "Evolutionary modeling and inference of gene network", journal = "Information Sciences", volume = "145", number = "3-4", month = sep, year = "2002", pages = "237--259", keywords = "genetic algorithms, genetic programming, Gene network, Evolutionary modeling, Time series prediction", URL = "http://www.sciencedirect.com/science/article/B6V0C-46WWB37-3/2/963172f8c0faa12d700376b07bfc96a5", ISSN = "0020-0255", DOI = "doi:10.1016/S0020-0255(02)00235-9", abstract = "we describe an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.", } @Article{ando:2004:GPEM, author = "Shin Ando and Hitoshi Iba", title = "Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "145--156", month = jun, keywords = "genetic algorithms, genetic programming, evolutionary computation, artificial immune system, wrapper approach, gene expression classification, cancer diagnosis", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023685.83861.69", abstract = "The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in Bioinformatics. The problem we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues. Two subtasks in such problem, feature subset selection and inductive learning has critical effect on each other. In the wrapper approach, combinatorial search of feature subset is done with performance of inductive learning as search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation is introduced, which uses Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals synergistic effect of genes in discrimination of the profiles.", notes = "Part of \cite{banzhaf:2004:biogec} Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1) Department of Electronics, School of Engineering, University of Tokyo, Yokohama, Japan (2) Department of Frontier Informatics, School of Frontier Science, University of Tokyo, Chiba, Japan", } @InProceedings{Andrade2012CIARP, author = "Felipe S. P. Andrade and Jurandy Almeida and Helio Pedrini and Ricardo {da S. Torres}", title = "Fusion of Local and Global Descriptors for Content-Based Image and Video Retrieval", booktitle = "17th Iberoamerican Congress on Pattern Recognition", year = "2012", pages = "845--853", address = "Buenos Aires, Argentina", keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de", URL = "http://dx.doi.org/10.1007/978-3-642-33275-3_104", DOI = "doi:10.1007/978-3-642-33275-3_104", abstract = "Recently, fusion of descriptors has become a trend for improving the performance in image and video retrieval tasks. Descriptors can be global or local, depending on how they analyse visual content. Most of existing works have focused on the fusion of a single type of descriptor. Different from all of them, this paper aims to analyze the impact of combining global and local descriptors. Here, we perform a comparative study of different types of descriptors and all of their possible combinations. Extensive experiments of a rigorous experimental design show that global and local descriptors complement each other, such that, when combined, they outperform other combinations or single descriptors.", } @InProceedings{Andrade:2020:SSCI, author = "Felipe S. P. Andrade and Claus Aranha and Ricardo {da Silva Torres}", title = "On the Use of Predation to Shape Evolutionary Computation", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "117--124", abstract = "Classic Evolutionary Algorithms often use elitist approaches, such as fitness functions, to select individuals for new generations. In this work, we consider an alternative strategy to simulate the selection process that relies on exploiting ecological interactions between individuals instead of explicitly using a fitness based in the search progress. To demonstrate this strategy, we present an Artificial Life system which simulates an ecosystem where different species are different bio-inspired meta-heuristics, and the main ecological relationship is the predation. Specifically, individuals from a Particle Swarm Optimization (PSO), with movement rules defined by Genetic Programming, survive by predating on individuals from an Artificial Bee Colony (ABC) system that operates on traditional optimization rules. This ecology is investigated on optimization benchmarks, and we observed the development of interesting ecological dynamics between the two species.", keywords = "genetic algorithms, genetic programming, Predator prey systems, Statistics, Sociology, Biological system modeling, Ecosystems, Particle swarm optimization, Artificial Life, Evolutionary Computation, Ecological Relationship", DOI = "doi:10.1109/SSCI47803.2020.9308209", month = dec, notes = "Also known as \cite{9308209}", } @Misc{andre:UGthesis, author = "David Andre", title = "Artificial Evolution of Intelligence: Lessons from natural evolution: An illustrative approach using Genetic Programming", school = "Stanford University, Symbolic Systems Program", year = "1994", type = "BS Honors Thesis", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.1367&rep=rep1&type=pdf", } @InCollection{kinnear:andre, title = "Automatically Defined Features: The Simultaneous Evolution of 2-Dimensional Feature Detectors and an Algorithm for Using Them", author = "David Andre", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "477--494", chapter = "23", keywords = "genetic algorithms, genetic programming", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap23.pdf", DOI = "doi:10.7551/mitpress/1108.003.0029", size = "18 pages", abstract = "Although automatically defined functions (ADFcts) with genetic programming (GP) appear to have great utility in a wide variety of domains, their application to the automatic discovery of 2-dimensional features has been only moderately successful [Koza 1993]. Boolean functions of pixel inputs, although very general, may not be the best representation for 2-dimensional features. This chapter describes a method for the simultaneous evolution of 2-dimensional hit-miss matrices and an algorithm to use these matrices in pattern recognition. Hit-miss matrices are templates that can be moved over part of an input pattern to check for a match. These matrices are evolved using a 2-dimensional genetic algorithm, while the algorithms controlling the templates are evolved using GP. The approach is applied to the problem of digit recognition, and is found to be successful at discovering individuals which can recognize very low resolution digits. Possibilities for expansion into a full-size character recognition system are discussed.", notes = " Mixture of GP and two dee GA Part of \cite{kinnear:book}", } @InProceedings{andre:maps, author = "David Andre", title = "Evolution of Mapmaking Ability: Strategies for the evolution of learning, planning, and memory using genetic programming", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "250--255", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, evolved representations, gold collection, information encoding, intelligent agent, learning, mapmaking evolution; memory, multi-phasic fitness environment, planning, brain models, cartography, cognitive systems, learning (artificial intelligence), planning (artificial intelligence)", DOI = "doi:10.1109/ICEC.1994.350007", size = "6 pages", abstract = "An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to genetic programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using genetic programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations. An illustrative problem of `gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans", } @InProceedings{ieee94:andre, author = "David Andre", title = "Learning and Upgrading Rules for an OCR System Using Genetic Programming", year = "1994", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "http://citeseer.ist.psu.edu/31976.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/802/http:zSzzSzwww.cs.berkeley.eduzSz~dandrezSzpaperszSzAndre_WCCI_94_OCR_Boundary.pdf/learning-and-upgrading-rules.pdf", DOI = "doi:10.1109/ICEC.1994.349906", size = "6 pages", notes = "Uses GP both to recognise C in various fonts and to maintain manually produced extremely high level code when a new font is added", } @InProceedings{Andre:1995:ammsp, author = "David Andre", title = "The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming", booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)", year = "1995", editor = "Larry J. Eshelman", pages = "248--255", address = "Pittsburgh, PA, USA", publisher_address = "San Francisco, CA, USA", month = "15-19 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, memory", ISBN = "1-55860-370-0", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Andre_1995_ammsp.pdf", size = "8 pages", notes = "Worlds 2x2, 4x4 and 8x8. Separate program trees for mapmaker and mapuser. ADFs used by mapusers only. Uses repeat, repeati (Repeat_Index), IncMem. Torrodial memory, isomophic to world. Steady state, Tournament selection (8) but with smaller (2) tournament group size for deletion. Evolved programs subjected to analysis and explanation. Evolved general solutions from limited test cases. Suggests simple strategies dominate more complex ones. GP better than random.", } @InProceedings{andre:1995:parallel, author = "David Andre and John R. Koza", title = "Parallel Genetic Programming on a Network of Transputers", booktitle = "Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications", year = "1995", editor = "Justinian P. Rosca", pages = "111--120", address = "Tahoe City, California, USA", month = "9 " # jul, keywords = "genetic algorithms, genetic programming, even 5 parity, island model, demes, INMOS, 30MHz 32 bit 16 MByte TRAM T805", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf", size = "10 pages", abstract = "... in the C programming language ... migration rates between 1 percent and 8 percent ... more than linear speed up.", notes = "Host microsoft windows Pentium PC (visual Basic), Debugger (tram), Boss process (tram), 64 mesh node (4 neighbours) trams. like \cite{Koza:1995:pGPnt} part of \cite{rosca:1995:ml}", } @InProceedings{andre:1995:apalmm, author = "David Andre", title = "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action", booktitle = "IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence", year = "1995", editor = "Chris S. Mellish", volume = "1", pages = "741--747", address = "Montreal, Quebec, Canada", publisher_address = "San Francisco, CA, USA", month = "20-25 " # aug, organisation = "IJCAII,AAAI,CSCSI", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, memory", ISBN = "1-55860-363-8", URL = "http://ijcai.org/Past%20Proceedings/IJCAI-95-VOL%201/pdf/097.pdf", URL = "https://dl.acm.org/citation.cfm?id=1625952", size = "7 pages", abstract = "An essential component of an intelligent agent is the ability to notice, encode, store, and use information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, a method for the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans using the stored mental models. In this method, agents are multi-part computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold' collection is used to demonstrate the method in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the method can evolve programs that store simple representations of their environments and use these representations to produce simple plans.", notes = "MAPMAKER searches for gold", } @InProceedings{andre:1996:GKL, author = "David Andre and Forrest H {Bennett III} and John R. Koza", title = "Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming", booktitle = "Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems", year = "1996", volume = "1", address = "Nara, Japan", publisher_address = "Cambridge, MA, USA", month = "16--18 " # may, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf", size = "10 pages", abstract = "A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326 percent. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it uses a fine-grained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space.", notes = "Alife-5 A longer version of this paper will be presented at the GP-96 conference. GP gets best solution to GKL problem {"}The population size used to evolve the current world's record for the GKL majority classification 1-dimensionall 2-sate 7-neighbor cellular authomata problem was 51,200. I believe Melanie Mitchell at the Santa Fe Institute has been doing continuing additional work on using GAs to evolve CA rules for various other problems.{"}", } @InCollection{andre:1996:aigp2, author = "David Andre and John R. Koza", title = "Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "317--337", chapter = "16", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277532", URL = "https://dl.acm.org/citation.cfm?id=270224", DOI = "doi:10.7551/mitpress/1109.003.0022", size = "21 pages", abstract = "This chapter describes the parallel implementation of genetic programming in the C programming language using a PC type computer (running Windows) acting as a host and a network of processing nodes using the transputer architecture. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. This approach is illustrated by a comparison of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required the least computational effort with an 5% migration rate. Moreover, this computational effort was less than that required for solving the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of genetic programming, the use of distributed sub-populations with only limited migration delivered more than linear speed-up in solving the problem.", } @InProceedings{andre:1996:camc, author = "David Andre and Forrest H {Bennett III} and John R. Koza", title = "Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "3--11", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf", size = "9 pages", abstract = "It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various human- written algorithms have appeared in the past two decades for the vexatious majority classification task for one-dimensional two-state cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy of 82.326%. This level of accuracy exceeds that of the original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other known rules produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate repertoire of domains and particles to represent and communicate information across the cellular space.", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap1.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{andre:1996:introns, author = "David Andre and Astro Teller", title = "A Study in Program Response and the Negative Effects of Introns in Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "12--20", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AndreTeller.ps", URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/TellerGP96/TellerGP96.html", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap2.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "9 pages", abstract = "The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods have been explored. One alternative is to treat a specific location in indexed memory as the response value when the program terminates. The purpose of this paper is to explore the applicability of this technique to tree-structured programs and to explore the intron effects that these studies bring to light. This paper's experimental results support the finding that this memory-based program response technique is an improvement for some, but not all, problems. In addition, this paper's experimental results support the finding that, contrary to past research and speculation, the addition or even facilitation of introns can seriously degrade the search performance of genetic programming.", notes = "GP-96 html version available from http://www.cs.cmu.edu/~astro/", } @InProceedings{andre:1996:parGP, author = "David Andre and John R. Koza", title = "A parallel implementation of genetic programming that achieves super-linear performance", booktitle = "Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications", year = "1996", editor = "Hamid R. Arabnia", volume = "III", pages = "1163--1174", address = "Sunnyvale", month = "9-11 " # aug, publisher = "CSREA", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/pdpta1996.pdf", size = "13 pages", abstract = "This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance on the same problem.", notes = "Awarded Best Paper Award PDPTA'96", } @InCollection{andre:1997:HEC, author = "David Andre", title = "Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section G8.1", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", broken = "doi:10.1201/9781420050387.ptg", URL = "https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921", URL = "https://www.worldcat.org/title/handbook-of-evolutionary-computation/oclc/1108947278", size = "8 pages", abstract = "Rule-based systems used for optical character recognition (OCR) are notoriously difficult to write, maintain, and upgrade. This case study describes a method for using genetic programming (GP) to automatically generate and upgrade rules for an OCR system. Sets of rules for recognizing a single character are encoded as LISP programs and are evolved using GP. The rule sets are programs that evolve to examine a set of preprocessed features using complex constructs including iteration, pointers, and memory. The system was successful at learning rules for large character sets consisting of multiple fonts and sizes, with good generalization to test sets. In addition, the method was found to be successful at updating human-coded rules written in C for new fonts. This research demonstrates the successful application of GP to a difficult, noisy, real-world problem, and introduces GP as a method for learning sets of rules.", notes = "invited chapter", } @Misc{andre:cs267, author = "David Andre", title = "Multi-level parallelism in automatically synthesizing soccer-playing programs for Robocup using genetic programming", year = "1998", keywords = "genetic algorithms, genetic programming, memory", URL = "http://citeseer.ist.psu.edu/245675.html", broken = "http://www.cs.berkeley.edu/~dandre/cs267/final/cs267_final.ps", broken = "http://www.cs.berkeley.edu/~dandre/cs267/final/project_final.htm", size = "18 pages", abstract = "Many of the various proposals for tomorrow's supercomputers have included clusters of multiprocessors as an essential component. However, when designing the systems of the future, it is important to insure that the nature of the parallelism provided matches up with some relevant and important set of algorithms. This project presents empirical program synthesis as an algorithm that can successfully exploit the multiple levels of interconnect present in an multi-SMP cluster system. When applying program synthesis techniques to difficult problems, it is often the case that two distinct levels of parallelism will emerge. First, many example programs must be tested -- and can often be tested in parallel. This matches up with the {"}slow{"} interconnect on a clump-based system. Second, the execution of a particular program can often be parallelized, especially if the program is complicated or requires interactions with a complex simulation. This level of parallelism, in contrast to the first, often requires fine-grained communication. Thus, this matches up with the {"}fast{"} level of the clump-based system. In particular, this project presents a multi-level parallel system for the automatic program synthesis of soccer-playing agents for the Robocup simulator competition using genetic programming. The system uses both the fast shared-memory communication of the SMP system as well as a much slower mechanism for the inter-SMP communication. The system is benchmarked on a variety of configurations, and speedup curves are presented. Additionally, a simple LogP analysis comparing the performance of the designed system with a single-processor based NOW system is presented. Finally, the Robocup project is reviewed and the future work outlined.", notes = "my ghostview (Jan 2002) barfs at cs267_final.ps but it prints", } @Article{AK97, author = "David Andre and John R. Koza", title = "A parallel implementation of genetic programming that achieves super-linear performance", journal = "Information Sciences", year = "1998", volume = "106", number = "3-4", pages = "201--218", keywords = "genetic algorithms, genetic programming", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03", URL = "http://www.davidandre.com/papers/isj97.ps", DOI = "doi:10.1016/S0020-0255(97)10011-1", size = "18 pages", abstract = "This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super-linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance.", notes = "Information Sciences broken Aug 2023 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt", } @InProceedings{andre:1998:tdcGPmdk, author = "David Andre and Forrest H {Bennett III} and John Koza and Martin A. Keane", title = "On the Theory of Designing Circuits using Genetic Programming and a Minimum of Domain Knowledge", booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence", year = "1998", pages = "130--135", address = "Anchorage, Alaska, USA", month = "5-9 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, amplifiers, analog circuit design, circuit evolution, computational circuits, embryonic circuit elimination, filters, knowledge representation, minimal domain knowledge, problem-specific knowledge, analogue circuits, circuit CAD, circuit optimisation, intelligent design assistants, knowledge representation, programming", ISBN = "0-7803-4869-9", file = "c023.pdf", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00699489", DOI = "doi:10.1109/ICEC.1998.699489", size = "6 pages", abstract = "The problem of analog circuit design is a difficult problem that is generally viewed as requiring human intelligence to solve. Considerable progress has been made in automating the design of certain categories of purely digital circuits; however, the design of analog electrical circuits and mixed analog-digital circuits has not proved to be as amenable to automation. When critical analog circuits are required for a project, skilled and highly trained experts are necessary. Previous work on applying genetic programming to the design of analog circuits has proved to be successful at evolving a wide variety of circuits, including filters, amplifiers, and computational circuits; however, previous approaches have required the specification of an appropriate embryonic circuit. This paper explores a method to eliminate even this small amount of problem specific knowledge, and, in addition, proves that the representation used is capable of producing all circuits.", notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence", } @InProceedings{Andre:1999:ETD, author = "D. Andre and A. Teller", title = "Evolving {Team Darwin United}", booktitle = "RoboCup-98: Robot Soccer World Cup II", year = "1999", editor = "M. Asada and H. Kitano", volume = "1604", series = "LNCS", pages = "346--351", address = "Paris, France", month = jul # " 1998", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66320-7", ISSN = "0302-9743", bibdate = "Mon Sep 13 16:57:02 MDT 1999", acknowledgement = ack-nhfb, URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Teller_Astro.ps", DOI = "doi:10.1007/3-540-48422-1_28", URL = "http://206.210.94.135/work/pdfs/Teller_Astro.pdf", size = "7 pages", abstract = "The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a lack of reliable strategic guidelines, the pervasive attitude has been that the problem can most successfully be attacked by human expertise, possibly assisted by some level of machine learning. This led, in RoboCup'97, to a field of simulator teams all of whose level and style of play were heavily influenced by the human designers of those teams. It is the thesis of our work that machine learning, if given the opportunity to design (learn) ``everything'' about how the simulator team operates, can develop a competitive simulator team that solves the problem using highly successful, if largely non- human, styles of play. To this end, Darwin United is a team of eleven players that have been evolved as a team of coordinated agents in the RoboCup simulator. Each agent is given a subset of the lowest level perceptual inputs and must learn to execute series of the most basic actions (turn, kick, dash) in order to participate as a member of the team. This paper presents our motivation, our approach, and the specific construction of our team that created itself from scratch.", notes = "LNCS 1604 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66320-7 READ and WRITE functions, ie memory, 8 programs control the 11 players however these 8 can use 8 shared ADFs", } @Misc{Andre:2021:GPTP, author = "David Andre", title = "{GP} considered Dangerous", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", note = "keynote", keywords = "genetic algorithms, genetic programming", video_url = "https://mediaspace.msu.edu/media/Andre_Keynote_GPTP_2021/1_gjyr7q9g", notes = "Google-X Alphabet [X] Not part of published proceedings", } @Article{Andreae:2008:IJKBIES, author = "Peter Andreae and Huayang Xie and Mengjie Zhang", title = "Genetic Programming for detecting rhythmic stress in spoken {English}", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2008", volume = "12", number = "1", pages = "15--28", keywords = "genetic algorithms, genetic programming", ISSN = "1327-2314", publisher = "IOS Press", broken = "http://iospress.metapress.com/content/k017m554023m5732/", URL = "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00139", DOI = "doi:10.3233/KES-2008-12103", size = "14 pages", abstract = "Rhythmic stress detection is an important but difficult problem in speech recognition. This paper describes an approach to the automatic detection of rhythmic stress in New Zealand spoken English using a linear genetic programming system with speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. In addition to the four standard arithmetic operators, this approach also uses other functions such as trigonometric and conditional functions in the function set to cope with the complexity of the task. The error rate on the training set is used as the fitness function. The approach is examined and compared to a decision tree approach and a support vector machine approach on a speech data set with 703 vowels segmented from 60 female adult utterances. The genetic programming approach achieved a maximum average accuracy of 92.6percent. The results suggest that the genetic programming approach developed in this paper outperforms the decision tree approach and the support vector machine approach for stress detection on this data set in terms of the detection accuracy, the ability of handling redundant features, and the automatic feature selection capability.", notes = "KES, see also \cite{xie:evows06}", } @InCollection{kinnear:andrews, author = "Martin Andrews and Richard Prager", title = "Genetic Programming for the Acquisition of Double Auction Market Strategies", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", chapter = "16", pages = "355--368", keywords = "genetic algorithms, genetic programming, SA", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", broken = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap16.pdf", DOI = "doi:10.7551/mitpress/1108.003.0022", size = "14 pages", abstract = "The Double Auction (DA) is the mechanism behind the minute-by-minute trading on many futures and commodity exchanges. Since 1990, DA tournaments have been held by the Santa Fe Institute. The competitors in the tournaments are strategies embodied in computer programmes written by a variety of economists, computer scientists and mathematicians. This paper describes how Genetic Programming (GP) methods have been used to create strategies superior, in local DA playoffs, to many of the hand-coded strategies. To isolate the contribution that the evolutionary process makes to the search for good strategies, we compare GP and Simulated Annealing (SA) optimisation of programmes. To reduce the cost of learning, we also investigate an approach that uses statistical measures to maintain a uniform population pressure.", notes = "'a GP approach was very successful in learning strategies for playing a simple game with complex dynamics' Ref Knobeln Contest: (broken) Sanfrancisco.ira.uka.de [129.13.13.110] /pub/knobeln Generational GP pop=300, touranment selection? size=2? Comparison with Simulated Annealing:SA also good but GP better Best GP exceeded performance of handcode routines (on average?) 65percent of time. Check details of what exctly this means. Set number of games played so could distinquish meadian from top quartile with 95percent confidence. Claims it helps, but doesnt seem to have either speeded things at lot or made much better result. Part of \cite{kinnear:book}", } @Article{Androutsopoulos:2019:GPEM, author = "Kelly Androutsopoulos", title = "Evelyne Lutton, Nathalie Perrot, Alberto Tonda: Evolutionary algorithms for food science and technology", subtitle = "Wiley, 2016, 182 pp, ISBN: 978-1-119-13683-5", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "147--149", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8cU", DOI = "doi:10.1007/s10710-018-9335-2", size = "3 pages", abstract = "...Evolutionary Algorithms for Food Science and Technology would be invaluable to anyone considering using EAs in food science...", notes = "Review of \cite{Lutton:book}", } @InCollection{androvitsaneas_intelligent_2019, title = "Intelligent {Data} {Analysis} in {Electric} {Power} {Engineering} {Applications}", isbn13 = "978-3-319-94030-4", URL = "https://doi.org/10.1007/978-3-319-94030-4_11", abstract = "This chapter presents various intelligent approaches for modelling, generalization and knowledge extraction from data, which are applied in different electric power engineering domains of the real world. Specifically, the chapter presents: (1) the application of ANNs, inductive ML, genetic programming and wavelet NNs, in the problem of ground resistance estimation, an important problem for the design of grounding systems in constructions, (2) the application of ANNs, genetic programming and nature inspired techniques such as gravitational search algorithm in the problem of estimating the value of critical flashover voltage of insulators, a well-known difficult topic of electric power systems, (3) the application of specific intelligent techniques (ANNs, fuzzy logic, etc.) in load forecasting problems and in optimization tasks in transmission lines. The presentation refers to previously conducted research related to the application domains and briefly analyses each domain of application, the data corresponding to the problem under consideration, while are also included a brief presentation of each intelligent technique and presentation and discussion of the results obtained. Intelligent approaches are proved to be handy tools for the specific applications as they succeed to generalize the operation and behaviour of specific parts of electric power systems, they manage to induce new, useful knowledge (mathematical relations, rules and rule based systems, etc.) and thus they effectively assist the proper design and operation of complex real world electric power systems.", booktitle = "Machine {Learning} {Paradigms}: {Advances} in {Data} {Analytics}", publisher = "Springer", author = "V. P. Androvitsaneas and K. Boulas and G. D. Dounias", editor = "George A. Tsihrintzis and Dionisios N. Sotiropoulos and Lakhmi C. Jain", year = "2019", DOI = "doi:10.1007/978-3-319-94030-4_11", volume = "149", series = "ISRL", keywords = "genetic algorithms, genetic programming, gene expression programming, electric power systems, Gravitational Search Algorithm, ground resistance estimation, insulators, wavelet neural nets", isbn13 = "978-3-319-94029-8", pages = "269--313", } @InProceedings{Ang:2008:cec, author = "J. H. Ang and E. J. Teoh and C. H. Tan and K. C. Goh and K. C. Tan", title = "Dimension Reduction Using Evolutionary Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3634--3641", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0777.pdf", DOI = "doi:10.1109/CEC.2008.4631290", abstract = "This paper presents a novel approach of hybridising two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @Article{angarita-zapata:2021:Sensors, author = "Juan S Angarita-Zapata and Gina Maestre-Gongora and Jenny Fajardo Calderin", title = "A Bibliometric Analysis and Benchmark of Machine Learning and {AutoML} in Crash Severity Prediction: The Case Study of Three Colombian Cities", journal = "Sensors (Basel, Switzerland)", year = "2021", volume = "21", number = "24", month = dec # " 16", keywords = "genetic algorithms, genetic programming, TPOT, Bayes Theorem, Benchmarking, Bibliometrics, Cities, Colombia, Machine Learning, Internet of Things, automated machine learning, crash severity prediction, intelligent transportation systems, supervised learning", ISSN = "1424-8220", DOI = "doi:10.3390/s21248401", abstract = "Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellin, Bogota, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.", notes = "PMID: 34960494", } @PhdThesis{angeline:dissertation, author = "Peter John Angeline", title = "Evolutionary Algorithms and Emergent Intelligence", school = "Ohio State University", year = "1993", address = "USA", size = "180 pages", keywords = "genetic algorithms, genetic programming", broken = "ftp://nervous.cis.ohio-state.edu/pub/papers/DISS/pja", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter0.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter1.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter2.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter3.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter4.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter5.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter6.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter7.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter8.ps.Z", URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/dissrefs.ps.Z", notes = " http://citeseer.ist.psu.edu/114089.html has introduction", } @InCollection{kinnear:angeline, title = "Genetic Programming and Emergent Intelligence", author = "Peter John Angeline", booktitle = "Advances in Genetic Programming", publisher = "MIT Press", editor = "Kenneth E. {Kinnear, Jr.}", year = "1994", pages = "75--98", chapter = "4", keywords = "genetic algorithms, genetic programming", URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap4.pdf", URL = "http://citeseer.ist.psu.edu/187189.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzaigp.pdf/angeline94genetic.pdf", URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888", DOI = "doi:10.7551/mitpress/1108.003.0009", size = "23 pages", abstract = "Genetic programming is but one of several problem solving methods based on a computational analogy to natural evolution. Such algorithms, collectively titled evolutionary computations, embody dynamics that permit task specific knowledge to emerge while solving the problem. In contrast to the traditional knowledge representations of artificial intelligence, this method of problem solving is termed emergent intelligence. This chapter describes some of the basics of emergent intelligence, its implementation in evolutionary computations, and its contributions to genetic programming. Demonstrations and guidelines on how to exploit emergent intelligence to extend the problem solving capabilities of genetic programming and other evolutionary computations are also presented.", notes = "'Contrasts GP with other Weak/strong AI methods, credit assignment, USEFUL, diplodity=redundancy=good, hierarchical code/decode of subroutines better than Koza ADF Loads of references' I realized that inherent dynamics of genetic programming encouraged certain emergent properties. The most important of these is that introns emerge naturally from the process to protect the developing program from crossover. Others in the field think this extra stuff in the genetic program is a bad thing, reflected by their choice of the term 'bloating' for the effect. This chapter is the first to take a positive view on GP introns and other emergent phenomena. I think this is the first paper to associate the 'extra' code in genetic programs with the intron concept. Part of \cite{kinnear:book}", } @InProceedings{icga93:angeline, author = "Peter J. Angeline and Jordan B. Pollack", title = "Competitive Environments Evolve Better Solutions for Complex Tasks", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", pages = "264--270", month = "17-21 " # jul, address = "University of Illinois at Urbana-Champaign", publisher_address = "2929 Campus Drive, Suite 260, San Mateo, CA 94403, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/icga5.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/icga5.ps.gz", URL = "http://www.natural-selection.com/Library/1993/icga93.ps.Z", size = "7 pages", abstract = "In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we demonstrate that competitive fitness functions, i.e. fitness functions that are dependent on the constituents of the population, can provide a more robust training environment than independent fitness functions. We describe three differing methods for competitive fitness, and discuss their respective advantages.", ISBN = "1-55860-299-2", notes = "very like thesis One method I investigated was called competitive fitness functions which is a fitness function that compares performance between members of the population to determine a ranking of individuals for reproduction. THis obviates the need for a quantitative model of the quality of solutions and replaces it with a more simplistic measure of {"}x is better than y{"}. The paper explores this concept using GLiB and appeared in ICGA93.", } @InProceedings{Angeline:1994:GPCS, author = "P. J. Angeline", title = "Genetic programming: A current snapshot", booktitle = "Proceedings of the Third Annual Conference on Evolutionary Programming", year = "1994", editor = "D. B. Fogel and W. Atmar", publisher = "Evolutionary Programming Society", keywords = "genetic algorithms, genetic programming", broken = "http://www.natural-selection.com/Library/1994/ep94-gp.ps.Z", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzep94-gp.pdf/angeline94genetic.pdf", URL = "http://citeseer.ist.psu.edu/147407.html", abstract = "Genetic programming is a promising variant of genetic algorithms that evolves dynamic, hierarchical structures often described as programs. Genetic programming's flexibility to tailor the representation language to the problem being solved, and its specially designed crossover operator provide a robust tool for evolving problem solutions. This paper provides an introduction to genetic programming, a short review of dynamic representations used in evolutionary systems and their relation to genetic programming, and a description of some of genetic programming's inherent properties. The paper concludes with a review of on going research and some potential future directions for the field.", } @InProceedings{Angeline:1992:EIS, author = "Peter J. Angeline and Jordan B. Pollack", title = "The evolutionary induction of subroutines", booktitle = "Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society", year = "1992", pages = "236--241", address = "Bloomington, Indiana, USA", publisher = "Lawrence Erlbaum", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/glib92.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/glib92.ps.gz", URL = "http://www.natural-selection.com/Library/1992/cogsci92.ps.Z", abstract = "we describe a genetic algorithm capable of evolving large programs by exploiting two new genetic operators which construct and deconstruct parameterized subroutines. These subroutines protect useful partial solutions and help to solve the scaling problem for a class of genetic problem solving methods. We demonstrate that our algorithm acquires useful subroutines by evolving a modular program from scratch to play and win at Tic-Tac-Toe against a flawed expert. This work also serves to amplify our previous note (Pollack, 1991) that a phase transition is the principle behind induction in dynamical cognitive models.", notes = "GLiB is an emergent method for discovering task-specific modular decompositions in genetic programs. At least this is how I used to talk about it. I know consider this an individual-level self-adaptive method for forming decompositions in genetic programs.", } @TechReport{Angeline:1993:CHLR, author = "P. J. Angeline and J. B. Pollack", title = "Coevolving High-Level Representations", institution = "Laboratory for Artificial Intelligence. The Ohio State University", year = "1993", type = "July", number = "Technical report 92-PA-COEVOLVE", keywords = "genetic algorithms, genetic programming", URL = "http://www.demo.cs.brandeis.edu/papers/alife3.pdf", abstract = "Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype's maximum size and complexity. In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly highlevel representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved languages are provided.", size = "15 pages", } @InProceedings{angeline:1993:ema, author = "Peter J. Angeline and Jordan Pollack", title = "Evolutionary Module Acquisition", booktitle = "Proceedings of the Second Annual Conference on Evolutionary Programming", year = "1993", editor = "D. Fogel and W. Atmar", pages = "154--163", address = "La Jolla, CA, USA", month = "25-26 " # feb, organisation = "The Evolutionary Programming Society", keywords = "genetic algorithms, genetic programming, FSM, GLiB", URL = "http://www.demo.cs.brandeis.edu/papers/ep93.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/ep93.ps.gz", URL = "http://www.natural-selection.com/Library/1993/ep93.ps.Z", size = "9 pages", abstract = "Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms, called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time.", notes = "Artificial Ant (John Muir). Finite State Machines. Genetic Library Builder ", } @InProceedings{Angeline:1991:CHLR, author = "P. J. Angeline and J. B. Pollack", title = "Coevolving high-level representations", booktitle = "Artificial Life III", year = "1994", editor = "Christopher G. Langton", volume = "XVII", series = "SFI Studies in the Sciences of Complexity", pages = "55--71", address = "Santa Fe, New Mexico", month = "15-19 " # jun # " 1992", publisher = "Addison-Wesley", keywords = "genetic algorithms, genetic programming", ISBN = "0-201-62492-3", URL = "http://www.demo.cs.brandeis.edu/papers/alife3.pdf", URL = "http://www.demo.cs.brandeis.edu/papers/alife3.ps.gz", URL = "https://www.amazon.co.uk/s?k=9780201624922", abstract = "Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype's maximum size and complexity. In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly high-level representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved languages are provided.", notes = "ALife3 Held June 1992 in Santa Fe, New Mexico, USA GLiB, Tower of Hanoi, Tic Tac Toe. Also in thesis.", } @Article{angeline:1994:BS, author = "Peter J. Angeline", title = "Genetic programming: On the programming of computers by means of natural selection,John R. Koza, A Bradford Book, MIT Press, Cambridge MA, 1992, ISBN 0-262-11170-5, xiv + 819pp., US\$55.00", journal = "Biosystems", year = "1994", volume = "33", number = "1", pages = "69--73", note = "Book review", keywords = "genetic algorithms, genetic programming", broken = "http://www.sciencedirect.com/science/article/B6T2K-49N8PP4-23/2/021e3e016b39a87da29046c37f423f73", DOI = "doi:10.1016/0303-2647(94)90062-0", notes = "Review of \cite{koza:book}", } @Article{angeline:1995:er, author = "Peter J. Angeline", title = "Evolution Revolution: An Introduction to the Special Track on Genetic and Evolutionary Programming", journal = "IEEE Expert", year = "1995", volume = "10", number = "3", pages = "6--10", month = jun, note = "Guest editor's introduction", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MIS.1995.10027", size = "4 pages", notes = "Dec 2011 NO PDF given with 10.1109/MIS.1995.10027 fab colour picture by Karl Sims 6 articles in special track; 2 use evolutionary programming, 2 use genetic programming ( \cite{Tackett:1995:mGP} and \cite{wong:1995:glp}) and 2 use hybrids ((GA and GP \cite{howard:1995:GA-P}) and (Riziki and Zmuda, August 1995 GA and EP morphological pattern recognition))", } @InProceedings{angeline:1995:mcc, author = "Peter J. Angeline", title = "Morphogenic Evolutionary Computations: Introduction, Issues and Examples", booktitle = "Evolutionary Programming IV: The Fourth Annual Conference on Evolutionary Programming", year = "1995", editor = "John Robert McDonnell and Robert G. Reynolds and David B. Fogel", pages = "387--401", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-13317-2", broken = "http://www.natural-selection.com/Library/1995/ep95-morph.ps.Z", broken = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=4397", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300850", DOI = "doi:10.7551/mitpress/2887.003.0037", size = "16 pages", abstract = "Morphogenic (or morphogenetic) evolutionary computations are evolutionary computations that distinguish between the representation that is evolved and the representation that is evaluated by the fitness function. A user defined development function provides the necessary mapping between these often very different structures. Such a separation affords important advantages for these evolutionary computations, not the least of which is modification of a relatively small structure that is expanded into a much larger one for evaluation by the fitness function. This paper provides a formal definition of morphogenic evolutionary computations along with a review and discussion of the relevant literature.", notes = "EP-95", } @InCollection{angeline:1995:asa, author = "Peter J. Angeline", title = "Adaptive and Self-Adaptive Evolutionary Computations", booktitle = "Computational Intelligence: A Dynamic Systems Perspective", publisher = "IEEE Press", year = "1995", editor = "Marimuthu Palaniswami and Yianni Attikiouzel and Robert J. {Marks, II} and David B. Fogel and Toshio Fukuda", pages = "152--163", keywords = "genetic algorithms, genetic programming", language = "English", ISBN = "0-7803-1182-5", broken = "http://www.natural-selection.com/Library/1995/icec95.ps.Z", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1007/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzicec95.pdf/angeline95adaptive.pdf", URL = "http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.493.2479&rank=6", URL = "https://www.amazon.com/Computational-Intelligence-Dynamic-System-Perspective/dp/0780311825/", URL = "https://research-repository.uwa.edu.au/en/publications/computational-intelligence-a-dynamic-system-perspective", size = "13 pages", abstract = "This paper reviews the various studies that have introduced adaptive and selfadaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in use. Previous studies are reviewed and placed into a categorisation that helps to illustrate their similarities and differences", } @Book{book:1996:aigp2, editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", title = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp2.html", URL = "http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267488", DOI = "doi:10.7551/mitpress/1109.001.0001", size = "538 pages", abstract = "Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field.", } @InCollection{intro:1996:aigp2, author = "Peter J. Angeline", title = "Genetic Programming's Continued Evolution", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "1--20", chapter = "1", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277539", DOI = "doi:10.7551/mitpress/1109.003.0004", size = "20 pages", abstract = "Genetic programming is a variant of genetic algorithms that evolves computer programs represented as tree structures. Genetic programming and genetic algorithms are but one technique in the larger collection of evolutionary computations that also include evolution strategies and evolutionary programming. This chapter begins with a description of the various evolutionary computations that highlights their respective differences across several important dimensions. Following this, an introduction to genetic programming and its relation to other evolutionary computations is provided. Research topics of current interest in the genetic programming field are then reviewed, demonstrating the present breadth and maturity of the field. The chapter ends with a description of the organisation of the remainder of this book and a brief synopsis of each chapter that appears.", } @InCollection{angeline:1996:aigp2, author = "Peter J. Angeline", title = "Two Self-Adaptive Crossover Operators for Genetic Programming", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "89--110", chapter = "5", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://www.natural-selection.com/Library/1996/aigp2.ps.Z", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277498", DOI = "doi:10.7551/mitpress/1109.003.0009", size = "21 pages", abstract = "Two self-adaptive crossover operations are studied in a nonstandard genetic program. It is shown that for three distinct problems the results obtained when using either of the self-adaptive crossover operations are equivalent or better than the results when using standard GP crossover. A postmortem analysis of the evolved values for the self-adaptive parameters suggests that certain heuristics commonly used in genetic programming may not be optimal.", notes = "These were called Selective Self-Adaptive Crossover and Self-adaptive Multi-Crossover.", } @InProceedings{angeline:1996:leaf, author = "Peter J. Angeline", title = "An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "21--29", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://www.natural-selection.com/Library/1996/gp96.zip", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap3.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "9 pages", abstract = "In genetic programming, crossover swaps randomly selected subtrees between parents. Typically, the probability of selecting a leaf as the subtree to be swapped is reduced, supposedly to allow larger structures on average. This paper reports on a study to determine the effect of modifying the leaf selection frequency for subtree crossover on the performance of a non-standard genetic program. Both a variety of constant values and dynamic update methods are investigated . It is shown that the performance of the genetic program is impacted by the manipulation of the leaf selection frequency and often can be improved using a random process rather than a constant value.", notes = "GP-96 multiple types of mutation Sunspot Numbers data from http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html", } @InProceedings{angeline:1996:efm, author = "Peter J. Angeline", title = "Evolving Fractal Movies", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Evolutionary Programming", pages = "503--511", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap84.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 EP paper", } @InProceedings{angeline:1997:tcbbe, author = "Peter J. Angeline", title = "Subtree Crossover: Building Block Engine or Macromutation?", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "9--17", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://ncra.ucd.ie/COMP41190/SubtreeXoverBuildingBlockorMacromutation_angeline_gp97.ps", size = "9 pages", abstract = "In genetic programming, crossover swaps randomly selected subtrees between parents. Recent work in genetic algorithms (Jones 1995) demonstrates that when one of the parents selected for crossover is replaced with a randomly generated parent, the algorithm performs as well or better than crossover for some problems. Terry Jones (ICGA 1995) termed this form of macromutation headless chicken crossover. The following paper investigates two forms of headless-chicken crossover for manipulating parse trees and shows that both types of macromutation perform as well or better than standard subtree crossover. It is argued that these experiments support the hypothesis that the building block hypothesis is not descriptive of the operation of subtree crossover and that sub-tree crossover is better modelled as a macromutation restricted by population content.", notes = "spirls, sun spots GP-97", } @InProceedings{Angeline:1997:aIMepesr, author = "Peter J. Angeline", title = "An Alternative to Indexed Memory for Evolving Programs with Explicit State Representations", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "evolutionary programming and evolution strategies", pages = "423--430", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{angeline:1997:txde, author = "Peter J. Angeline", title = "Tracking Extrema in Dynamic Environments", booktitle = "Proceedings of the 6th International Conference on Evolutionary Programming", year = "1997", editor = "P. J. Angeline and R. G. Reynolds and J. R. McDonnell and R. Eberhart", volume = "1213", series = "Lecture Notes in Computer Science", pages = "335--345", address = "Indianapolis, Indiana, USA", month = apr # " 13-16", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-62788-X", URL = "http://www.natural-selection.com/Library/1997/ep97b.pdf", DOI = "doi:10.1007/BFb0014823", size = "11 pages", abstract = "Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.", notes = "EP-97", } @InProceedings{angeline:1997:spie, author = "Peter J. Angeline and David B. Fogel", title = "An evolutionary program for the identification of dynamical systems", booktitle = "Application and Science of Artificial Neural Networks III", year = "1997", editor = "S. Rogers", volume = "3077", pages = "409--417", publisher_address = "Bellingham, WA, USA", organisation = "SPIE-The International Society for Optical Engineering", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, system identification, dynamical systems, optimization", URL = "http://www.natural-selection.com/Library/1997/spie97.pdf", DOI = "doi:10.1117/12.271503", size = "9 pages", abstract = "Various forms of neural networks have been applied to the identification of non-linear dynamical systems. In most of these methods, the network architecture is set prior to training. In this paper, a method that evolves a symbolic solution for plant models is described. This method uses an evolutionary program to manipulate collections of parse trees expressed in a task specific language. Experiments performed on two unknown plants show this method is competitive with those that train neural networks for similar problems", } @InCollection{Angeline:1997:HEC, author = "Peter J. Angeline", title = "Parse Trees", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C1.6", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "3 pages", abstract = "This section reviews parse tree representations, a popular representation for evolving executable structures. The field of genetic programming is based entirely on the flexibility of this representation. This section describes some of the history of parse trees in evolutionary computation, the form of the representation and some special properties.", } @InCollection{Angeline:1997:HECa, author = "Peter J. Angeline", title = "Mutation: Parse Trees", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C3.2.5", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "2 pages", abstract = "Genetics-based evolutionary computations typically discount the role of mutation operation in the induction of evolved structures. This is especially true in genetic programming where mutation operations for parse trees are often not used. Some practitioners of genetic programming believe that mutation has an important role in evolving fit parse trees. This section describes several mutation operations for parse trees used by some genetic programming enthusiasts.", notes = "grow, shrink,switch, cycle=point", } @InCollection{Angeline:1997:HECb, author = "Peter J. Angeline", title = "Crossover: parse trees", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C3.3.5", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "2 pages", abstract = "Described here is the standard crossover operation for parse tree representations most often used in genetic programming. Extensions to this operator for subtrees with multiple return types and genetic programs using automatically defined functions are also described.", } @InProceedings{angeline:1998:sccb, author = "Peter J. Angeline", title = "Subtree Crossover Causes Bloat", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "745--752", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, evolutionary programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/angeline_1998_sccb.pdf", size = "9 pages", notes = "GP-98, Even-5 parity, intertwined spirals, sunspot prediction. ", } @Article{angeline:1998:hpees, author = "Peter J. Angeline", title = "A Historical Perspective on the Evolution of Executable Structures", journal = "Fundamenta Informaticae", year = "1998", volume = "35", number = "1--4", pages = "179--195", month = aug, email = "angeline@natural-selection.com", keywords = "genetic algorithms, genetic programming", ISSN = "0169-2968", URL = "http://www.natural-selection.com/Library/1998/gphist.pdf", size = "16 pages", abstract = "Genetic programming (Koza 1992) is a method of inducing behaviors represented as executable programs. The generality of the approach has spawned a proliferation of work in the evolution of executable structures that is unmatched in the history of the subject. This paper describes the standard approach to genetic programming, as defined in Koza (1992), and then presents the significant studies that preceded its inception as well as the diversification of techniques evolving executable structures that is currently underway in the field.", notes = "Special volume: Evolutionary Computation Also published in book form, see \cite{angeline:1999:hpees}", } @Article{angeline:1998:mips3, author = "Peter J. Angeline", title = "Multiple Interacting Programs: A Representation for Evolving Complex Behaviors", journal = "Cybernetics and Systems", year = "1998", volume = "29", number = "8", pages = "779--803", month = nov, keywords = "genetic algorithms, genetic programming, mips", ISSN = "0196-9722", URL = "http://www.natural-selection.com/Library/1998/mips3.pdf", URL = "http://www.tandfonline.com/doi/abs/10.1080/019697298125407", DOI = "doi:10.1080/019697298125407", size = "25 pages", abstract = "This paper defines a representation for expressing complex behaviors, called multiple interacting programs (MIPs), and describes an evolutionary method for evolving solutions to difficult problems expressed as MIPs structures. The MIPs representation is a generalization of neural network architectures that can model any type of dynamic system. The evolutionary training method described is based on an evolutionary program originally used to evolve the architecture and weights of recurrent neural networks. Example experiments demonstrate the training method s ability to evolve appropriate MIPs solutions for difficult problems. An analysis of the evolved solutions shows their dynamics to be interesting and non-trivial.", notes = "Sun spots, Santa Fe trail Artifical Ant, 5-bit reverser, Tree, ANN", } @InProceedings{angeline:1998:spie, author = "Peter J. Angeline", title = "Evolving Predictors for Chaotic Time Series", booktitle = "Proceedings of SPIE: Application and Science of Computational Intelligence", year = "1998", editor = "S. Rogers and D. Fogel and J. Bezdek and B. Bosacchi", volume = "3390", pages = "170--80", publisher_address = "Bellingham, WA, USA", organisation = "SPIE", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, neural networks, chaotic time series prediction", URL = "http://www.natural-selection.com/Library/1998/spie98.pdf", DOI = "doi:10.1117/12.304803", size = "11 pages", abstract = "Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current methods performance is shown to compare favorably with using larger neural networks.", } @InCollection{angeline:1999:hpees, author = "Peter J. Angeline", title = "A Historical Perspective on the Evolution of Executable Structures", booktitle = "Evolutionary Computation", publisher = "Ohmsha", year = "1999", editor = "A. E. Eiben and A. Michalewicz", address = "Tokyo", keywords = "genetic algorithms, genetic programming", ISBN = "4-274-90269-2", URL = "http://www.ohmsha.co.jp/data/books/e_contents/4-274-90269-2.htm", notes = "This is the book edition of the journal, Fundamenta Informaticae, Volume 35, Nos. 1-4, 1998. See also \cite{angeline:1998:hpees} ", size = "pages", } @InCollection{angeline:2000:EC1, author = "Peter J. Angeline", title = "Parse trees", booktitle = "Evolutionary Computation 1 Basic Algorithms and Operators", publisher = "Institute of Physics Publishing", year = "2000", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "19", pages = "155--159", address = "Bristol", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0664-5", URL = "http://www.crcpress.com/product/isbn/9780750306645", size = "5 pages", } @Article{Angelis:2023:ACME, author = "Dimitrios Angelis and Filippos Sofos and Theodoros E. Karakasidis", title = "Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives", journal = "Archives of Computational Methods in Engineering", year = "2023", volume = "30", pages = "3845--3865", month = jul, keywords = "genetic algorithms, genetic programming", URL = "https://rdcu.be/dmkPm", DOI = "doi:10.1007/s11831-023-09922-z", size = "21 pages", abstract = "Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalisable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.", notes = "Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia 35100, Greece", } @Article{angelis:2023:Micromachines, author = "Dimitrios Angelis and Filippos Sofos and Konstantinos Papastamatiou and Theodoros E. Karakasidis", title = "Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods", journal = "Micromachines", year = "2023", volume = "14", number = "7", pages = "Article No. 1446", keywords = "genetic algorithms, genetic programming", ISSN = "2072-666X", URL = "https://www.mdpi.com/2072-666X/14/7/1446", DOI = "doi:10.3390/mi14071446", abstract = "In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic expressions. The outcome is a set of mathematical equations, with reduced complexity and increased accuracy, that adhere to existing domain knowledge and can be exploited in fluid property interpolation and extrapolation, bypassing timely simulations when possible.", notes = "also known as \cite{mi14071446}", } @InProceedings{Angelov:2008:GEFS, author = "Plamen Angelov and Arthur Kordon and Xiaowei Zhou", title = "Evolving fuzzy inferential sensors for process industry", booktitle = "3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008", year = "2008", month = "4-7 " # mar, address = "Witten-Boommerholz, Germany", pages = "41--46", keywords = "genetic algorithms, genetic programming, Dow Chemical Company, Takagi-Sugeno-fuzzy system, fuzzy inferential sensor, multi-objective genetic-programming-based optimization, on-line input selection techniques, on-line learning algorithm, process industry, self-tuning inferential soft sensor, chemical industry, fuzzy set theory, fuzzy systems, sensors", DOI = "doi:10.1109/GEFS.2008.4484565", abstract = "This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts aging, industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by the Dow Chemical Company, USA.", notes = "Also known as \cite{4484565}", } @Article{Anicic:2017:OLE, author = "Obrad Anicic and Srdan Jovic and Hivzo Skrijelj and Bogdan Nedic", title = "Prediction of laser cutting heat affected zone by extreme learning machine", journal = "Optics and Lasers in Engineering", volume = "88", pages = "1--4", year = "2017", ISSN = "0143-8166", DOI = "doi:10.1016/j.optlaseng.2016.07.005", URL = "http://www.sciencedirect.com/science/article/pii/S0143816616301385", abstract = "Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be used effectively in applications of HAZ forecasting.", keywords = "genetic algorithms, genetic programming, Extreme Learning Machine, Forecasting, HAZ, Laser cutting", } @InProceedings{Anjum:2019:EuroGP, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Ariadne: Evolving test data using Grammatical Evolution", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "3--18", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, SBSE, SBST", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_1", size = "16 pages", abstract = "Software testing is a key component in software quality assurance; it typically involves generating test data that exercises all instructions and tested conditions in a program and, due to its complexity, can consume as much as 50percent of overall software development budget. Some evolutionary computing techniques have been successfully applied to automate the process of test data generation but no existing techniques exploit variable interdependencies in the process of test data generation, even though several studies from the software testing literature suggest that the variables examined in the branching conditions of real life programs are often interdependent on each other, for example, if (x == y), etc. We propose the Ariadne system which uses Grammatical Evolution (GE) and a simple Attribute Grammar to exploit the variable interdependencies in the process of test data generation. Our results show that Ariadne dramatically improves both effectiveness and efficiency when compared with existing techniques based upon well-established criteria, attaining coverage (the standard software testing success metric for these sorts of problems) of 100percent on all benchmarks with far fewer program evaluations (often between a third and a tenth of other systems).", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Anjum:2019:SSCI, author = "Aftab Anjum and Mazharul Islam and Lin Wang", booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Gene Permutation: A new Probabilistic Genetic Operator for Improving Multi Expression Programming", year = "2019", pages = "3139--3146", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI44817.2019.9003048", abstract = "Multi-expression Programming (MEP) encodes multiple genes through linear representation and is a widely useful technique for tangible applications like classification, symbolic regression and digital circuit designing. MEP uses only two genetic operators (mutation, crossover) to explore the search space and exploit genetic materials. However, after going through multiple generations and due to its naturally inspired fitness-based selection procedure, MEP significantly reduces genetic diversity in the population and ultimately produces homogeneous individuals; hence, leading to poor convergence and an ultimate fall into the local minimum. Gene-permutation, the newly proposed Probabilistic Genetic Operator, breakouts the homogeneity by rearranging and inducing new genetic materials in the individuals which in turn maintains the healthy genetic diversity in the population. Moreover, it also assists other genetic operators to produce more effective chromosomes and fully explore the search space. The experiments point out that Gene-permutation improves training efficiency as well as reduces test errors on several well-known symbolic regression problems.", notes = "Also known as \cite{9003048}", } @InProceedings{Anjum:2020:EuroGP, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "18--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, SBSE, SBST, Automatic test case generation, Code coverage, Evolutionary Testing", isbn13 = "978-3-030-44093-0", video_url = "https://www.youtube.com/watch?v=e2T-NYqEvh8", DOI = "doi:10.1007/978-3-030-44094-7_2", abstract = "Software-based optimization techniques have been increasingly used to automate code coverage analysis since the nineties. Although several studies suggest that interdependencies can exist between condition constructs in branching conditions of real life programs e.g. (i<=100) or (i==j), etc., to date, only the Ariadne system, a Grammatical Evolution (GE)-based Search Based Software Testing (SBST) technique, exploits interdependencies between variables to efficiently automate code coverage analysis. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to very efficiently evolve highly complex test cases, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from the interdependencies involving constants e.g. (i<=100), which are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. ...", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Anjum:2020:GECCO, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Scalability Analysis of Grammatical Evolution Based Test Data Generation", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377930.3390167", DOI = "doi:10.1145/3377930.3390167", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1213--1221", size = "9 pages", keywords = "genetic algorithms, genetic programming, grammatical evolution, code coverage analysis, scalability, software testing, search based software testing, automatic test data generation, evolutionary testing, variable interdependencies", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "Heuristic-based search techniques have been increasingly used to automate different aspects of software testing. Several studies suggest that variable interdependencies may exist in branching conditions of real-life programs, and these dependencies result in the need for highly precise data values (such as of the form i=j=k) for code coverage analysis. This requirement makes it very difficult for Genetic Algorithm (GA)-based approach to successfully search for the required test data from vast search spaces of real-life programs. Ariadne is the only Grammatical Evolution (GE)-based test data generation system, proposed to date, that uses grammars to exploit variable interdependencies to improve code coverage. Ariadne has been compared favourably to other well-known test data generation techniques in the literature; however, its scalability has not yet been tested for increasingly complex programs. This paper presents the results of a rigorous analysis performed to examine Ariadne's scalability. We also designed and employed a large set of highly scalable 18 benchmark programs for our experiments. Our results suggest that Ariadne is highly scalable as it exhibited 100percent coverage across all the programs of increasing complexity with significantly smaller search costs than GA-based approaches, which failed even with huge search budgets.", notes = "Also known as \cite{10.1145/3377930.3390167} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{Anjum:2021:SNCS, author = "Muhammad Sheraz Anjum and Conor Ryan", title = "Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing", journal = "SN Computer Science", year = "2021", volume = "2", pages = "280", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Automatic test data generation, Code coverage, Evolutionary testing", DOI = "doi:10.1007/s42979-021-00631-7", size = "19 pages", abstract = "Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. ( i≤100 ) or ( i==j ), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relation-ship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of ( i==j ), and the value of variable k must be equal to 100 for the satisfaction of the condition of ( k==100 ). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form ( i≤j ) or ( i==j ), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. ( i≤100 ) or ( j==500 ), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach", } @Misc{DBLP:journals/corr/abs-1904-03368, author = "Aftab Anjum and Fengyang Sun and Lin Wang and Jeff Orchard", title = "A Novel Continuous Representation of Genetic Programmings using Recurrent Neural Networks for Symbolic Regression", howpublished = "arXiv", volume = "abs/1904.03368", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "http://arxiv.org/abs/1904.03368", archiveprefix = "arXiv", eprint = "1904.03368", timestamp = "Fri, 11 Oct 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/corr/abs-1904-03368.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{ANLAUF:2022:procs, author = "Stefan Anlauf and Andreas Haghofer and Karl Dirnberger and Stephan M. Winkler", title = "Using Heterogeneous Model Ensembles to Improve the Prediction of Yeast Contamination in Peppermint", journal = "Procedia Computer Science", volume = "200", pages = "1194--1200", year = "2022", note = "3rd International Conference on Industry 4.0 and Smart Manufacturing", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2022.01.319", URL = "https://www.sciencedirect.com/science/article/pii/S1877050922003283", keywords = "genetic algorithms, genetic programming, yeast contamination, herbs, machine learning, heterogeneous model ensembles", abstract = "In this paper, we present an heterogeneous ensemble modeling approach to learn predictors for yeast contamination in freshly harvested peppermint batches. Our research is based on data about numerous parameters of the harvesting process, such as planting, tillage, fertilization, harvesting, drying, as well as information about microbial contamination. We use several different machine learning methods, namely random forests, gradient boosting trees, symbolic regression by genetic programming, and support vector machines to learn models that predict contamination on the basis of available harvesting parameters. Using those models we form model ensembles in order to improve the accuracy as well as to reduce the false negative rate, i.e., to oversee as few contaminations as possible. As we summarize in this paper, ensemble modeling indeed helps to increase the prediction accuracy for our application, especially when using only the best models. The final prediction accuracy as well as other statistical indicators such as false negative rate and false positive rate depend on the choice of the discrimination threshold; in the optimal case, model ensembles are able to predict yeast contamination with 65.91percent accuracy and only 19.15percent of the samples are false negative, i.e., overseen contaminations", } @Article{AnnunziatoL2003:ICAE, author = "Mauro Annunziato and Carlo Bruni and Matteo Lucchetti and Stefano Pizzuti", title = "Artificial Life Approach for Continuous Optimisation of Non Stationary Dynamical Systems", journal = "Integrated Computer-Aided Engineering", year = "2003", volume = "10", number = "2", pages = "111--125", email = "lucchetti@dis.uniroma1.it", keywords = "genetic algorithms, genetic programming, artificial life", ISSN = "1069-2509", URL = "http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00140", DOI = "doi:10.3233/ICA-2003-10202", size = "15 pages", abstract = "In this paper, we develop an intelligent system to approach dynamical optimisation problems emerging in control of complex systems. In particular our proposal is to exploit the adaptivity of an artificial life (alife) environment in order to achieve 'not control rules but autonomous structures able to dynamically adapt and to generate optimised-control rules'. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to environmental changes). The suggested methodology has been tested on an energy regulation problem deriving from a classical testbed in dynamical systems experimentations: the Chua's circuit. We supposed not to know the system dynamics and to be able to act only on a subset of control parameters, letting the others vary in time in a random discrete way. We let the optimisation process searching for the new best value of performance, whenever a drop due to changes in fitness landscape occurred. We present the most important results showing the effectiveness of the proposed approach in adapting to environmental non-stationary changes by recovering the optimal value of process performance.", } @InProceedings{DBLP:conf/pricai/ArdehMZ19, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", editor = "Abhaya C. Nayak and Alok Sharma", title = "A Novel Genetic Programming Algorithm with Knowledge Transfer for Uncertain Capacitated Arc Routing Problem", booktitle = "{PRICAI} 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part {I}", series = "Lecture Notes in Computer Science", volume = "11670", pages = "196--200", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-29908-8_16", DOI = "doi:10.1007/978-3-030-29908-8_16", timestamp = "Mon, 15 Jun 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/pricai/ArdehMZ19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{AnsariArdeh:2019:CEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Transfer Learning in Genetic Programming Hyper-heuristic for Solving Uncertain Capacitated Arc Routing Problem", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "49--56", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8789920", size = "8 pages", abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimization problem that has many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Generalisation is an open issue in the field of UCARP and in this direction, an open challenge is the case of changes in number of vehicles which currently leads to new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. Since none of the existing GP transfer methods have been used as a hyper-heuristic in solving UCARP, we conduct a comprehensive study to investigate the behaviour of the existing GP transfer methods for evolving routing policy in UCARP,", notes = "also known as \cite{8789920} IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{AnsariArdeh:2019:GECCOcomp, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "334--335", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321988", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321988} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Ansel:2011:GECCO, author = "Jason Ansel and Maciej Pacula and Saman Amarasinghe and Una-May O'Reilly", title = "An efficient evolutionary algorithm for solving incrementally structured problems", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1699--1706", keywords = "genetic algorithms, genetic programming, SBSE, Real world applications", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001805", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn't appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.", notes = "Research compiler petabricks. GPEA. Aim: autotuning for computer when program when is actually installed on that computer. Looks at recursive sort and which chooses one of 4 types of sort (Insertion sort, quick sort, radix sort and a dummy) to use at each level of recursion. Noisy fitness evaluation (run for real, not simulation). uses T-test (trying to be too fair?). Examples: sort, matrix multiply (matmult) and eig (symmetric eigen problem). Also known as \cite{2001805} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{DBLP:conf/IEEEpact/AnselKVRBOA14, author = "Jason Ansel and Shoaib Kamil and Kalyan Veeramachaneni and Jonathan Ragan-Kelley and Jeffrey Bosboom and Una-May O'Reilly and Saman P. Amarasinghe", title = "{OpenTuner}: an extensible framework for program autotuning", booktitle = "International Conference on Parallel Architectures and Compilation, PACT '14", year = "2014", editor = "Jose Nelson Amaral and Josep Torrellas", pages = "303--316", address = "Edmonton, Canada", month = aug # " 24-27", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, program autotuning, multi-armed bandit problem", timestamp = "Thu, 24 Nov 2022 09:18:38 +0100", biburl = "https://dblp.org/rec/conf/IEEEpact/AnselKVRBOA14.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://doi.org/10.1145/2628071.2628092", DOI = "doi:10.1145/2628071.2628092", code_url = "http://opentuner.org/", size = "13 pages", abstract = "Program autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building auto-tuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8 fold with little programmer effort.", notes = "Not GP? GCC_FLAGS GCC_PARAMS. AUC Bandit Multiple simultaneous search. time, accuracy, energy, size, confidence. GCC/G++ Flags: fft.c, matrixmultiply.cpp, raytracer.cpp, tsp_ga.cpp Halide: Blur, Wavelet, Bilateral. Linpack PetaBricks: Poisson, Sort, Strassen, Tridiagonal Solver. Stencil, Laplacian, Stencil, Unitary Matrix, Mario", } @Misc{DBLP:journals/corr/abs-2305-16956, author = "Fabio Anselmi and Mauro Castelli and Alberto d'Onofrio and Luca Manzoni and Luca Mariot and Martina Saletta", title = "Local Search, Semantics, and Genetic Programming: a Global Analysis", howpublished = "arXiv", volume = "abs/2305.16956", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2305.16956", DOI = "doi:10.48550/ARXIV.2305.16956", eprinttype = "arXiv", eprint = "2305.16956", timestamp = "Mon, 05 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2305-16956.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Anthes:2009:ACM, author = "Gary Anthes", title = "Deep Data Dives Discover Natural Laws", journal = "Communications of the ACM", year = "2009", volume = "52", number = "11", pages = "13--14", month = nov, note = "News", keywords = "genetic algorithms, genetic programming", ISSN = "0001-0782", URL = "http://cacm.acm.org/magazines/2009/11/48443-deep-data-dives-discover-natural-laws/pdf", DOI = "doi:10.1145/1592761.1592768", size = "2 pages", abstract = "Computer scientists have found a way to bootstrap science, using evolutionary computation to find fundamental meaning in massive amounts of raw data. Mining scientific data for patterns and relationships has been a common practice for decades, and the use of self-mutating genetic algorithms is nothing new, either. But now a pair of computer scientists at Cornell University have pushed these techniques into an entirely new realm, one that could fundamentally transform the methods of science at the frontiers of research.", notes = "Report on \cite{Science09:Schmidt}", } @MastersThesis{hdl:1860/18, title = "Evolving board evaluation fuctions for a complex strategy game", author = "Lisa Patricia Anthony", year = "2002", month = dec # "~30", language = "en_US", school = "Drexel University", keywords = "genetic algorithms, genetic programming", URL = "http://dspace.library.drexel.edu/handle/1721.1/18", URL = "http://dspace.library.drexel.edu/bitstream/1860/18/1/anthony_thesis.pdf", size = "73 pages", abstract = "The development of board evaluation functions for complex strategy games has been approached in a variety of ways. The analysis of game interactions is recognized as a valid analogy to common real-world problems, which often present difficulty in designing algorithms to solve them. Genetic programming, as a branch of evolutionary computation, provides advantages over traditional algorithms in solving these complex real-world problems in speed, robustness and flexibility. This thesis attempts to address the problem of applying genetic programming techniques to the evolution of a strategy for evaluating potential moves in a one-step lookahead intelligent agent heuristic for a complex strategybased game. This is meant to continue the work in artificial intelligence which seeks to provide computer systems with the tools they need to learn how to operate within a domain, given only the basic building blocks. The issues surrounding this problem are formulated and techniques are presented within the realm of genetic programming which aim to contribute to the solution of this problem. The domain chosen is the strategy game known as Acquire, whose object is to amass wealth while investing stock in hotel chains and effecting mergers of these chains as they grow. The evolution of the board evaluation functions to be used by agent players of the game is accomplished via genetic programming. Implementation details are discussed, empirical results are presented, and the strategies of some of the best players are analyzed. Future improvements on these techniques within this domain are outlined, as well as implications for artificial intelligence and genetic programming.", notes = "format = 318461", } @Article{Anthony:2017/08/facebook, author = "Sebastian Anthony", title = "{Facebook}'s evolutionary search for crashing software bugs", journal = "ars technica UK", year = "2017", month = "22 " # aug # ", 07:52", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Facebook, Sapienz", URL = "https://arstechnica.co.uk/information-technology/2017/08/facebook-dynamic-analysis-software-sapienz/", video_url = "https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/", size = "2 pages", abstract = "Ars gets the first look at Facebook's fancy new dynamic analysis tool.", notes = "Is this GP? Cites Ke Mao, Mark Harman, Yue Jia. Sapienz: Multi-objective automated testing for Android applications. In International Symposium on Software Testing and Analysis (ISSTA, 2016). pp. 94-105 http://www.cs.ucl.ac.uk/staff/K.Mao/archive/p_issta16_sapienz.pdf facebook video https://developers.facebook.com/videos/f8-2018/friction-free-fault-finding-with-sapienz/", } @MastersThesis{antolik:mastersthesis, author = "Jan Antolik", title = "Evolutionary Tree Genetic Programming", school = "Department of Computing and Information Sciences, College of Arts and Sciences, Kansan State University", year = "2004", type = "Master of Science", address = "Manhattan, Kansas, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www.ms.mff.cuni.cz/~antoj9am/thesis.pdf", size = "49 pages", abstract = "We introduce an extension of a genetic programming (GP) algorithm we call Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree like pattern. We want to simulate similar behaviour in artificial evolutionary systems such as GP. In this thesis we provide multiple reasons why we believe simulation of this phenomenon can be beneficial for GP systems. We present various empirical results from test runs. As the test bed for our experiments two standard benchmark problems for GP systems are used, particularly the Artificial Ant problem and the Multiplexer problem. The performance of the ETGP algorithm is compared to the performance of GP system. Unfortunately no significant speedup is found. Some unexpected behaviors of our system are also identified, and a hypothesis is formulated that addresses the question of why we observe this strange behaviour and the lack of speedup. Suggestions on how to extend the ETGP system to overcome the problems identified by this hypothesis are then presented in the end of our concluding chapter.", notes = "Approved by: Major Professor William Hsu", } @InProceedings{1068312, author = "Jan Antolik and William H. Hsu", title = "Evolutionary tree genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1789--1790", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1789.pdf", DOI = "doi:10.1145/1068009.1068312", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster", abstract = "We introduce a clustering-based method of subpopulation management in genetic programming (GP) called Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree-like phylogenetic pattern. Our goal is to simulate similar behavior in artificial evolutionary systems such as GP. To test our model we use three common GP benchmarks: the Ant Algorithm, 11-Multiplexer, and Parity problems.The performance of the ETGP system is empirically compared to those of the GP system. Code size and variance are consistently reduced by a small but statistically significant percentage, resulting in a slight speedup in the Ant and 11-Multiplexer problems, while the same comparisons on the Parity problem are inconclusive.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Antonelli:2013:NAFIPS, author = "Michela Antonelli and Pietro Ducange and Francesco Marcelloni and Armando Segatori", title = "Evolutionary Fuzzy Classifiers for Imbalanced Datasets: An Experimental Comparison", booktitle = "Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS 2013)", year = "2013", month = "24-28 " # jun, pages = "13--18", address = "Edmonton, Canada", keywords = "genetic algorithms, genetic programming, database management systems, fuzzy set theory, learning (artificial intelligence), pattern classification, statistical testing, EFC, FRBC, ROC curve, complexity optimisation, embedded rule base generation, evolutionary data base learning, evolutionary fuzzy classifiers, genetic programming algorithm, genetic rule selection, hierarchical fuzzy rule-based classifier, imbalanced datasets, membership function parameters tuning, multiobjective evolutionary learning scheme, nonparametric statistical tests, rule base learning, sensitivity optimisation, specificity optimisation, Accuracy, Biological cells, Complexity theory, Genetics, Input variables, Training, Tuning, Fuzzy Rule-based Classifiers, Genetic and Evolutionary Fuzzy Systems, Imbalanced Datasets", isbn13 = "978-1-4799-0348-1", DOI = "doi:10.1109/IFSA-NAFIPS.2013.6608367", size = "6 pages", abstract = "In this paper, we compare three state-of-the-art evolutionary fuzzy classifiers (EFCs) for imbalanced datasets. The first EFC performs an evolutionary data base learning with an embedded rule base generation. The second EFC builds a hierarchical fuzzy rule-based classifier (FRBC): first, a genetic programming algorithm is used to learn the rule base and then a post-process, which includes a genetic rule selection and a membership function parameters tuning, is applied to the generated FRBC. The third EFC is an extension of a multi-objective evolutionary learning scheme we have recently proposed: the rule base and the membership function parameters of a set of FRBCs are concurrently learnt by optimising the sensitivity, the specificity and the complexity. By performing non-parametric statistical tests, we show that, without re-balancing the training set, the third EFC outperforms, in terms of area under the ROC curve, the other comparison approaches.", notes = "Also known as \cite{6608367}", } @InProceedings{conf/setn/AntoniouGTVL10, title = "A Gene Expression Programming Environment for Fatigue Modeling of Composite Materials", author = "Maria A. Antoniou and Efstratios F. Georgopoulos and Konstantinos A. Theofilatos and Anastasios P. Vassilopoulos and Spiridon D. Likothanassis", booktitle = "6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications (SETN 2010)", year = "2010", volume = "6040", editor = "Stasinos Konstantopoulos and Stavros J. Perantonis and Vangelis Karkaletsis and Constantine D. Spyropoulos and George A. Vouros", pages = "297--302", series = "Lecture Notes in Computer Science", address = "Athens, Greece", month = may # " 4-7", publisher = "Springer", isbn13 = "978-3-642-12841-7", keywords = "genetic algorithms, genetic programming", bibdate = "2010-05-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/setn/setn2010.html#AntoniouGTVL10", DOI = "doi:10.1007/978-3-642-12842-4", abstract = "In the current paper is presented the application of a Gene Expression Programming Environment in modeling the fatigue behavior of composite materials. The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques. In order to evaluate the performance of the presented environment, we tested it in fatigue modeling of composite materials.", } @InProceedings{Antoniou:2010:AIAI, author = "Maria Antoniou and Efstratios Georgopoulos and Konstantinos Theofilatos and Spiridon Likothanassis", title = "Forecasting Euro - United States Dollar Exchange Rate with Gene Expression Programming", booktitle = "6th IFIP Advances in Information and Communication Technology AIAI 2010", year = "2010", editor = "Harris Papadopoulos and Andreas Andreou and Max Bramer", volume = "339", series = "IFIP Advances in Information and Communication Technology", pages = "78--85", address = "Larnaca, Cyprus", month = oct # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1007/978-3-642-16239-8_13", abstract = "In the current paper we present the application of our Gene Expression Programming Environment in forecasting Euro-United States Dollar exchange rate. Specifically, using the GEP Environment we tried to forecast the value of the exchange rate using its previous values. The data for the EURO-USD exchange rate are online available from the European Central Bank (ECB). The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques.", affiliation = "Pattern Recognition Laboratory, Dept. of Computer Engineering & Informatics, University of Patras, 26500 Patras, Greece", notes = "http://www.cs.ucy.ac.cy/aiai2010/", } @InProceedings{foga90*193, author = "Hendrik James Antonisse", title = "A Grammar-Based Genetic Algorithm", pages = "193--204", ISBN = "1-55860-170-8", editor = "Gregory J. E. Rawlins", booktitle = "Foundations of Genetic Algorithms", month = "15--18 " # jul # " 1990", address = "Indiana University, Bloomington, USA", publisher = "Morgan Kaufmann", publisher_address = "San Mateo", keywords = "genetic algorithms, genetic programming, inductive bias, high-level representations, crossover", year = "1991", DOI = "doi:10.1016/B978-0-08-050684-5.50015-X", abstract = "High-level syntactically-based representations pose problems for applying the GA because it is hard to construct crossover operators that always result in legal offspring. This paper proposes a reformulation of the genetic algorithm that makes it appropriate to any representation that can be cast in a formal grammar. This reformulation is consistent with recent reinterpretations of GA foundations in set-theoretic terms, and concentrates on the modifications required to make the space of legal structures closed under the crossover operator. The analysis places no restriction on the form of the grammars.", notes = "FOGA-90 Published in 1991. cited by \cite{bruhn:2002:ECJ} grammar-based crossover, parity. K-armed bandit", } @InProceedings{antonov:2024:GECCO, author = "Kirill Antonov and Roman Kalkreuth and Kaifeng Yang and Thomas Baeck and Niki Stein and Anna Kononova", title = "A Functional Analysis Approach to Symbolic Regression", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "859--867", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, functional analysis, hilbert space optimization", isbn13 = "979-8-4007-0494-9", URL = "https://arxiv.org/abs/2402.06299", DOI = "doi:10.1145/3638529.3654079", size = "9 pages", abstract = "Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although traditional genetic programming (GP) algorithms have achieved success in various domains, they exhibit limited performance when tree-based representations are used for SR. To address these limitations, we introduce a novel SR approach called Fourier Tree Growing (FTG) that draws insights from functional analysis. This new perspective enables us to perform optimization directly in a different space, thus avoiding intricate symbolic expressions. Our proposed algorithm exhibits significant performance improvements over traditional GP methods on a range of classical one-dimensional benchmarking problems. To identify and explain the limiting factors of GP and FTG, we perform experiments on a large-scale polynomials benchmark with high-order polynomials up to degree 100. To the best of the authors' knowledge, this work represents the pioneering application of functional analysis in addressing SR problems. The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Antony:2023:ICCCNT, author = "Divya Antony and Naseer C", booktitle = "2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)", title = "Comparison of {CNN} and {YOLOv5} For Melanoma Detection", year = "2023", abstract = "MELANOMA is one of the most dangerous forms of skin cancer that results from melanocytes, which produce the brown pigment that gives skin its colour. Sometimes it forms multi-colours based on its stage. The survival of melanoma patients depends on the early identification of the disease. But in the early stage, it becomes very small and does not meet the dermoscopic standards for cancer detection such as irregular shape, network, colour of pigments and also it is difficult to differentiate lesion to benign and melanoma. So early detection of melanoma is difficult. Therefore, we need an accurate melanoma classifier that classifies lesions at begin of the stage. In this paper, a comprehensive analysis of cnn, Alexnet and yolov5 for melanoma detection is performed using Accuracy, Precision, Recall and F1 Score", keywords = "genetic algorithms, genetic programming, Multi tree genetic programming, Location awareness, Shape, Melanoma, Colour, Pigments, Skin, Lesions, You only look once, Regional convolutional neural network", DOI = "doi:10.1109/ICCCNT56998.2023.10307675", ISSN = "2473-7674", month = jul, notes = "Also known as \cite{10307675}", } @InProceedings{ICIP99_Vol1*529, author = "Shinya Aoki and Tomoharu Nagao", title = "Automatic construction of tree-structural image transformation using genetic programming", pages = "529--533", booktitle = "Proceedings of the 1999 International Conference on Image Processing ({ICIP}-99)", month = oct # " ~24--28", publisher = "IEEE", year = "1999", volume = "1", address = "Kobe", publisher_address = "Los Alamitos, CA, USA", keywords = "genetic algorithms, genetic programming, automatic construction, image filters, medical image processing, tree-structural image transformation, image coding, image processing", DOI = "doi:10.1109/ICIP.1999.821685", abstract = "We previously proposed an automatic construction method of image transformations. In this method, we approximated an unknown image transformation by a series of several known image filters, and a genetic algorithm optimizes their combination to meet the processing purpose presented by sets of original and target images. In this paper, we propose an extended method named {"}Automatic Construction of Tree-structural Image Transformations (ACTIT){"}. In this new method, a tree whose interior nodes are image filters and leaf ones are input images approximates the transformation. The structures of the trees are optimized using genetic programming. ACTIT finds practical filter combinations that are too complicated to be designed by hand. It can be applied to various kinds of image processing tasks. We show examples of its applications to document and medical image processing", notes = "Also known as \cite{821685}", } @InProceedings{aparicio:1999:PM, author = "Joaquim N. Aparicio and Luis Correia and Fernando Moura-Pires", title = "Populations are Multisets-PLATO", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1845--1850", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "methodology, pedagogy and philosophy", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-603.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/MP-603.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Applegate:2013:CEC, article_id = "1637", author = "Douglas Applegate and Blayne Mayfield", title = "An Analysis of Exchanging Fitness Cases with Population Size in Symbolic Regression Genetic Programming with Respect to the Computational Model", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "3111--3116", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557949", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{conf/lacci/AquinoRGBL17, author = "Nelson Marcelo Romero Aquino and Manasses Ribeiro and Matheus Gutoski and Cesar Manuel {Vargas Benitez} and Heitor Silverio Lopes", title = "A gene expression programming approach for evolving multi-class image classifiers", booktitle = "2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)", year = "2017", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-1-5386-3734-0", URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8275062", DOI = "doi:10.1109/LA-CCI.2017.8285696", size = "6 pages", abstract = "This paper presents a methodology to perform multi-class image classification using Gene Expression Programming(GEP) in both balanced and unbalanced datasets. Descriptors are extracted from images and then their dimensionality are reduced by applying Principal Component Analysis. The aspects extracted from images are texture, colour and shape that are, later, concatenated in a feature vector. Finally, GEP is used to evolve trees capable of performing as classifiers using the features as terminals. The quality of the solution evolved is evaluated by the introduced Cross-Entropy-Loss-based fitness function and compared with standard fitness function (both accuracy and product of sensibility and specificity). A novel GEP function linker Softmax-based is introduced. GEP performance is compared with the obtained by classifiers with tree structure, as C4.5 and Random Forest algorithms. Results show that GEP is capable of evolving classifiers able to achieve satisfactory results for image multi-class classification.", notes = "Also known as \cite{8285696}", } @InProceedings{Aquino:2021:HNICEM, author = "Heinrick L. Aquino and Ronnie S. Concepcion and Andres Philip Mayol and Argel A. Bandala and Alvin Culaba and Joel Cuello and Elmer P. Dadios and Aristotle T. Ubando and Jayne Lois G. {San Juan}", booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production", year = "2021", month = "28-30 " # nov, address = "Manila, Philippines", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-0168-5", DOI = "doi:10.1109/HNICEM54116.2021.9731926", abstract = "Moisture content is an imperative indicator of biofuel lipid content in microalgae. This paper developed a reliable, computationally cost-effective combination of artificial neurons and an optimization tool for moisture content concentration prediction using computational intelligence. A total of 83 data of microalgae var. Chlorella vulgaris moisture content parameter factors were used. Using feed-forward, recurrent, and deep neural networks as prediction models, their MSE and R2 values were analyzed. Genetic programming GPTIPSv2, a multigene symbolic regression genetic programming (MSRGP) tool, was used to create objective functions of the ANNs. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered to suggest the optimal quantity of neurons in each of the hidden layers in neural network architecture. The feed-forward artificial neural network with 22 neurons in its layer was recommended using the Levenberg-Marquardt training tool. The MSE (5.27e-6) and R2 (0.9999) results of this model surpassed the other neural networks models. Hence, it implies that the developed optimized Levenberg-Marquardt-based feed-forward neural network is an effective moisture content predictor as it provided highly accurate and sensitive results at a low cost.", notes = "Also known as \cite{9731926}", } @Article{vu37709, author = "Sachindra Dhanapala Arachchige and Khandakar Ahmed and S Shahid and B. J. C Perera", title = "Cautionary note on the use of genetic programming in statistical downscaling", journal = "International Journal of Climatology", year = "2018", volume = "38", number = "8", pages = "3449--3465", month = jun, note = "SHORT COMMUNICATION", keywords = "genetic algorithms, genetic programming, GP algorithm, Victoria, Pakistan, downscaling models, climate, predictor--predictand relationships, atmospheric domain", publisher = "Royal Meteorological Society", ISSN = "1097-0088", URL = "https://vuir.vu.edu.au/37709/", DOI = "doi:10.1002/joc.5508", abstract = "The selection of inputs (predictors) to downscaling models is an important task in any statistical downscaling exercise. The selection of an appropriate set of predictors to a downscaling model enhances its generalization skills as such set of predictors can reliably explain the catchment-scale hydroclimatic variable (predictand). Among the predictor selection procedures seen in the literature, the use of genetic programming (GP) can be regarded as a unique approach as it not only selects a set of predictors influential on the predictand but also simultaneously determines a linear or nonlinear regression relationship between the predictors and the predictand. In this short communication, the details of an investigation on the assessment of effectiveness of GP in identifying a unique optimum set of predictors influential on the predictand and its ability to generate a unique optimum predictor-predictand relationship are presented. In this investigation, downscaling models were evolved for relatively wet and dry precipitation stations pertaining to two study areas using two different sets of reanalysis data for each calendar month maintaining the same GP attributes. It was found that irrespective of the climate regime (i.e., wet and dry) and reanalysis data set used, the probability of identification of a unique optimum set of predictors influential on precipitation by GP is quite low. Therefore, it can be argued that the use of GP for the selection of a unique optimum set of predictors influential on a predictand is not effective. However, when run repetitively, GP algorithm selected certain predictors more frequently than others. Also, when run repetitively, the structure of the predictor-predictand relationships evolved by GP varied from one run to another, indicating that the physical interpretation of the predictor-predictand relationships evolved by GP in a downscaling exercise can be unreliable.", } @InProceedings{aragon-jurado:2024:CEC, author = "Jose Miguel Aragon-Jurado and Javier Jareno and Juan Carlos {de la Torre} and Patricia Ruiz and Bernabe Dorronsoro", title = "Two-Level Software Obfuscation with Cooperative Co-Evolutionary Algorithms", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Source coding, Plagiarism, Software algorithms, Evolutionary computation, Software, Security, Source code obfuscation, LLVM, Intermediate Representation, IR, Tigress, Cooperative coevolution", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612116", size = "8 pages", abstract = "Computing devices are ubiquitous nowadays and because of the rise of new paradigms as the Internet of Things, their presence is continuously growing. Software (SW) is highly exposed, and SW companies are forced to protect their products from attacks to prevent plagiarism and the detection of security flaws. Obfuscation is a widespread technique to protect SW. It consists in making the code unintelligible, so that it is very hard to learn how it works. There are numerous obfuscation techniques, but they often require expert hands. Therefore, there is a clear need for fully automatic obfuscation tools that can offer high quality outputs independently of the specific features of the considered SW. we define a novel combinatorial optimisation problem for a two-level obfuscation method that makes use of typical obfuscation transformations, those provided by Tigress framework, as well as classical optimisation ones, those from LLVM compilation framework. The problem is solved with a cooperative co-evolutionary cellular genetic algorithm, providing a tool for automatic SW obfuscation. Three different obfuscation metrics are considered as fitness function. The results show that the proposed methodology offers outstanding obfuscation results, outperforming the original programs by up to 6,152,547percent. Moreover, compared to approaches from the literature, these results are as much as 405 times better.", notes = "also known as \cite{10612116} WCCI 2024", } @Article{Arakawa:2006:CILS, author = "Masamoto Arakawa and Kiyoshi Hasegawa and Kimito Funatsu", title = "QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network", journal = "Chemometrics and Intelligent Laboratory Systems", year = "2006", volume = "83", number = "2", pages = "91--98", month = "15 " # sep, keywords = "genetic algorithms, genetic programming, Multi-objective optimisation, Variable selection, HEPT, quantitative structure activity relationship", DOI = "doi:10.1016/j.chemolab.2006.01.009", abstract = "Quantitative structure-activity relationship (QSAR) has been developed for a set of inhibitors of the human immunodeficiency virus 1 (HIV-1) reverse transcriptase, derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT). Structural descriptors used in this study are Hansch constants for each substituent and topological descriptors. We have applied the variable selection method based on multi-objective genetic programming (GP) to the HEPT data and constructed the nonlinear QSAR model using counter-propagation (CP) neural network with the selected variables. The obtained network is accurate and interpretable. Moreover in order to confirm a predictive ability of the model, a validation test was performed.", } @InProceedings{Aranha:2006:ASPGP, title = "The effect of using evolutionary algorithms on ant clustering techniques", author = "Claus Aranha and Hitoshi Iba", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "24--34", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/Aranha_2006_ASPGP.pdf", size = "11 pages", abstract = "Ant-based clustering is a biologically inspired data clustering technique. In this technique, multiple agents carry the information to be clustered, and make local comparisons. In this work we use genetic algorithms to improve the implementation and use of ant-clustering techniques.", notes = "broken march 2020 http://www.aspgp.org", } @InProceedings{Araseki:2012:SCIS, author = "Hitoshi Araseki", booktitle = "Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on", title = "Effectiveness of scale-free properties in genetic programming", year = "2012", pages = "285--289", address = "Kobe", month = "20-24 " # nov, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SCIS-ISIS.2012.6505204", abstract = "In this paper, we propose a new selection method, named scale-free selection, which is based on a scale-free network. Through study of the complex network, scale-free networks have been found in various fields. In recent years, it has been proposed that a scale-free property be applied to some optimisation problems. We investigate if the new selection method is an effective selection method to apply to genetic programming. Our experimental results on three benchmark problems show that performance of the scale-free selection model is similar to the usual selection methods in spite of different optimisations and may be able to resolve the bloating problem in genetic programming. Further, we show that the optimisation problem is relevant to complex network study.", notes = "Also known as \cite{6505204}", } @InProceedings{Araseki:2013:EVOLVE, author = "Hitoshi Araseki", title = "Genetic Programming with Scale-Free Dynamics", booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV", year = "2013", editor = "Michael Emmerich and Andre Deutz and Oliver Schuetze and Thomas Baeck and Emilia Tantar and Alexandru-Adrian and Pierre {Del Moral} and Pierrick Legrand and Pascal Bouvry and Carlos A. Coello", volume = "227", series = "Advances in Intelligent Systems and Computing", pages = "277--291", address = "Leiden, Holland", month = jul # " 10-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-01127-1", DOI = "doi:10.1007/978-3-319-01128-8_18", abstract = "This paper describe a new selection method, named SFSwT (Scale-Free Selection method with Tournament mechanism) which is based on a scale-free network study. A scale-free selection model was chosen in order to generate a scale-free structure. The proposed model reduces computational complexity and improves computational performance compared with a previous version of the model. Experimental results with various benchmark problems show that performance of the SFSwT is higher than with other selection methods. In various fields, scale-free structures are closely related to evolutionary computation. Further, it was found through the experiments that the distribution of node connectivity could be used as an index of search efficiency.", } @Article{agriculture13050935, author = "Adolfo Vicente Araujo and Caroline Mota and Sajid Siraj", title = "Using Genetic Programming to Identify Characteristics of Brazilian Regions in Relation to Rural Credit Allocation", journal = "Agriculture", year = "2023", volume = "13", number = "5", pages = "article--number 935", month = "24 " # apr, keywords = "genetic algorithms, genetic programming, rural credit, criteria analysis, family farming, machine learning", ISSN = "2077-0472", URL = "https://www.mdpi.com/2077-0472/13/5/935", DOI = "doi:10.3390/agriculture13050935", size = "14 pages", abstract = "Rural credit policies have a strong impact on food production and food security. The attribution of credit policies to agricultural production is one of the main problems preventing the guarantee of agricultural expansion. In this work, we conduct family typology analysis applied to a set of research data to characterize different regions. Through genetic programming, a model was developed using user-defined terms to identify the importance and priority of each criterion used for each region. Access to credit results in economic growth and provides greater income for family farmers, as observed by the results obtained in the model for the Sul region. The Nordeste region indicates that the cost criterion is relevant, and according to previous studies, the Nordeste region has the highest number of family farming households and is also the region with the lowest economic growth. An important aspect discovered by this research is that the allocation of rural credit is not ideal. Another important aspect of the research is the challenge of capturing the degree of diversity across different regions, and the typology is limited in its ability to accurately represent all variations. Therefore, it was possible to characterize how credit is distributed across the country and the main factors that can influence access to credit.", notes = "Department of Industrial Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil", } @InProceedings{Die99, author = "Dieferson L. A. Araujo and Heitor S. Lopes and Alex A. Freitas", title = "A parallel genetic algorithm for rule discovery in large databases", booktitle = "Proceedings of IEEE Systems, Man and Cybernetics Conference", year = "1999", volume = "III", pages = "940--945", note = "Tokyo, Japan, 12-15/october/1999", keywords = "genetic algorithms, data mining, parallel", URL = "http://www.cpgei.cefetpr.br/publicacoes/1999/ieeesmc99.zip", notes = "http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=823354&isnumber=17812&punumber=6569&k2dockey=823354@ieeecnfs&query=(freitas%20a.%20%20a.%3Cin%3Eau%20)&pos=7", } @InProceedings{araujo:2000:R, author = "Dieferson L. A. Araujo and Heitor S. Lopes and Alex A. Freitas", title = "Rule discovery with a parallel genetic algorithm", booktitle = "Data Mining with Evolutionary Algorithms", year = "2000", editor = "Alex A. Freitas and William Hart and Natalio Krasnogor and Jim Smith", pages = "89--94", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, data mining, parallel", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/gecco2000b.zip", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{araujo:2003:ICES, author = "Sergio G. Araujo and A. Mesquita and Aloysio C. P. Pedroza", title = "Using Genetic Programming and High Level Synthesis to Design Optimized Datapath", booktitle = "Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003", year = "2003", editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen", volume = "2606", series = "LNCS", pages = "434--445", address = "Trondheim, Norway", month = "17-20 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00730-X", DOI = "doi:10.1007/3-540-36553-2_39", abstract = "a methodology to design optimised electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level synthesis tools to automatically improve design structural quality (area measure). A two-stage, multiobjective optimization algorithm is used to search for circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation datapath design targeted to FPGA exemplifies the proposed methodology.", notes = "ICES-2003", } @InProceedings{semish2003meta007, title = "{S}{\'i}ntese de Circuitos Digitais Otimizados via Programa{\c c}{\~a}o Gen{\'e}tica", author = "Sergio Granato {de Araujo} and Antonio C. Mesquita and Aloysio C. P. Pedroza", year = "2003", abstract = "This paper presents a methodology for the design of optimized electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level synthesis tools to improve the design quality (area optimization). A two-stage, multiobjective optimization algorithm was used to search for circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation function design targeted to FPGA illustrates the methodology.", identifier = "semish2003article007", language = "por; eng", rights = "Sociedade Brasileira de Computa{\c c}{\~a}o", source = "semish2003", URL = "http://www.lbd.dcc.ufmg.br/bdbcomp/servlet/Trabalho?id=2490", URL = "http://www.gta.ufrj.br/ftp/gta/TechReports/AMP03d.pdf", booktitle = "XXX Semin{\'a}rio Integrado de Software e Hardware", volume = "III", pages = "273--285", address = "Unicamp, Campinas, SP, Brasil", month = "2-8 " # aug, keywords = "genetic algorithms, genetic programming", size = "13 pages", resumo = "Este trabalho apresenta uma metodologia para o projeto de circuitos eletronicos digitais otimizados a partir de descricoes feitas em alto nivel de abstracao. A metodologia utiliza Programacao Genetica e ferramentas de sintese de alto nivel para melhorar a qualidade do projeto (otimizacao de area). Foi utilizado um algoritmo de otimizacao com multiplos objetivos, composto de dois estagios, para buscar por circuitos com a funcionalidade desejada e com restricoes de area. Um experimento de projeto em FPGA de uma funcao que aproxima a raiz quadrada ilustra a metodologia.", notes = "XXIII Brazilian Symposium on Computation (SBC'03) http://www.sbc.org.br/sbc2003/semish.html In Portuguese Available as technical report GTA-03-50", } @InProceedings{araujo:2004:eurogp, author = "Lourdes Araujo", title = "Genetic Programming for Natural Language Parsing", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "230--239", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_21", abstract = "Our aim is to prove the effectiveness of the genetic programming approach in automatic parsing of sentences of real texts. Classical parsing methods are based on complete search techniques to find the different interpretations of a sentence. However, the size of the search space increases exponentially with the length of the sentence or text to be parsed and the size of the grammar, so that exhaustive search methods can fail to reach a solution in a reasonable time. This paper presents the implementation of a probabilistic bottom-up parser based on genetic programming which works with a population of partial parses, i.e. parses of sentence segments. The quality of the individuals is computed as a measure of its probability, which is obtained from the probability of the grammar rules and lexical tags involved in the parse. In the approach adopted herein, the size of the trees generated is limited by the length of the sentence. In this way, the size of the search space, determined by the size of the sentence to parse, the number of valid lexical tags for each words and specially by the size of the grammar, is also limited.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Araujo:PPSN:2006, author = "L. Araujo", title = "Multiobjective Genetic Programming for Natural Language Parsing and Tagging", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "433--442", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", URL = "http://ppsn2006.raunvis.hi.is/proceedings/055.pdf", DOI = "doi:10.1007/11844297_44", size = "10 pages", abstract = "Parsing and Tagging are very important tasks in Natural Language Processing. Parsing amounts to searching the correct combination of grammatical rules among those compatible with a given sentence. Tagging amounts to labelling each word in a sentence with its lexical category and, because many words belong to more than one lexical class, it turns out to be a disambiguation task. Because parsing and tagging are related tasks, its simultaneous resolution can improve the results of both of them. This work aims developing a multiobjective genetic program to perform simultaneously statistical parsing and tagging. It combines the statistical data about grammar rules and about tag sequences to guide the search of the best structure. Results show that any of the implemented multiobjective optimisation models improve on the results obtained in the resolution of each problem separately.", notes = "PPSN-IX", } @InProceedings{Araujo:2010:cec, author = "Lourdes Araujo and Jesus Santamaria", title = "Evolving natural language grammars without supervision", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Unsupervised grammar induction is one of the most difficult works of language processing. Its goal is to extract a grammar representing the language structure using texts without annotations of this structure. We have devised an evolutionary algorithm which for each sentence evolves a population of trees that represent different parse trees of that sentence. Each of these trees represent a part of a grammar. The evaluation function takes into account the contexts in which each sequence of Part-Of-Speech tags (POSseq) appears in the training corpus, as well as the frequencies of those POSseqs and contexts. The grammar for the whole training corpus is constructed in an incremental manner. The algorithm has been evaluated using a well known Annotated English corpus, though the annotation have only been used for evaluation purposes. Results indicate that the proposed algorithm is able to improve the results of a classical optimisation algorithm, such as EM (Expectation Maximisation), for short grammar constituents (right side of the grammar rules), and its precision is better in general.", DOI = "doi:10.1109/CEC.2010.5586291", notes = "WCCI 2010. Also known as \cite{5586291}", } @InProceedings{Araujo:2015:GECCOcomp, author = "Lourdes Araujo and Juan Martinez-Romo and Andres Duque", title = "Grammatical Evolution for Identifying Wikipedia Taxonomies", booktitle = "GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3488-4", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", pages = "1345--1346", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739482.2764629", DOI = "doi:10.1145/2739482.2764629", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This work applies Grammatical Evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a concept and is cross-linked by hyperlinks that connect related concepts. Hierarchical taxonomies and their generalization to ontologies are a highly useful resource for many applications by enabling semantic search and reasoning. We have developed a system which arranges a set of Wikipedia concepts into a taxonomy.", notes = "Also known as \cite{2764629} Distributed at GECCO-2015.", } @Article{AraujoMF18, author = "Lourdes Araujo and Juan Martinez-Romo and Andres Duque Fernandez", title = "Discovering taxonomies in {Wikipedia} by means of grammatical evolution", journal = "Soft Computing", year = "2018", volume = "22", number = "9", pages = "2907--2919", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "https://doi.org/10.1007/s00500-017-2544-4", timestamp = "Sat, 05 May 2018 23:05:31 +0200", biburl = "https://dblp.org/rec/bib/journals/soco/AraujoMF18", DOI = "doi:10.1007/s00500-017-2544-4", abstract = "This work applies grammatical evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a topic and is cross-linked by hyperlinks that connect related topics. Hierarchical taxonomies and their generalization to ontologies are a highly useful resource for many applications since they enable semantic search and reasoning. Thus, the automatic identification of taxonomies composed of concepts associated with linked Wikipedia pages has attracted much attention. We have developed a system which arranges a set of Wikipedia concepts into a taxonomy. This technique is based on the relationships among a set of features extracted from the contents of the Wikipedia pages. We have used a grammatical evolution algorithm to discover the best way of combining the considered features in an explicit function. Candidate functions are evaluated by applying a genetic algorithm to approximate the optimal taxonomy that the function can provide for a number of training cases. The fitness is computed as an average of the precision obtained by comparing, for the set of training cases, the taxonomy provided by the evaluated function with the reference one. Experimental results show that the proposal is able to provide valuable functions to find high-quality taxonomies.", } @Article{Araujo:GPEM20, author = "Lourdes Araujo", title = "Genetic programming for natural language processing", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "11--32", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Grammatical evolution, NLP, Natural language processing, Applications, Challenges", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09361-5", size = "22 pages", abstract = "This work takes us through the literature on applications of genetic programming to problems of natural language processing. The purpose of natural language processing is to allow us to communicate with computers in natural language. Among the problems addressed in the area is, for example, the extraction of information, which draws relevant data from unstructured texts written in natural language. There are also domains of application of particular relevance because of the difficulty in dealing with the corresponding documents, such as opinion mining in social networks, or because of the need for high precision in the information extracted, such as the biomedical domain. There have been proposals to apply genetic programming techniques in several of these areas. This tour allows us to observe the potential (not yet fully exploited) of such applications. We also review some cases in which genetic programming can provide information that is absent from other approaches, revealing its ability to provide easy to interpret results, in form of programs or functions. Finally, we identify some important challenges in the area.", } @Article{Arbuckle:2014:IJCISTUDIES, author = "Tom Arbuckle and Damien Hogan and Conor Ryan", title = "Learning predictors for flash memory endurance: a comparative study of alternative classification methods", journal = "International Journal of Computational Intelligence Studies", year = "2014", volume = "3", number = "1", pages = "18--39", month = jan # "~14", keywords = "genetic algorithms, genetic programming, flash memory endurance, performance prediction, linear programming, support vector machines, SVMs, learning predictors, classification methods, timing data, erasure, programming, modelling", publisher = "Inderscience Publishers", language = "eng", ISSN = "1755-4985", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=58644", DOI = "doi:10.1504/IJCISTUDIES.2014.058644", abstract = "Flash memory's ability to be programmed multiple times is called its endurance. Beyond being able to give more accurate chip specifications, more precise knowledge of endurance would permit manufacturers to use flash chips more effectively. Rather than physical testing to determine chip endurance, which is impractical because it takes days and destroys an area of the chip under test, this research seeks to predict whether chips will meet chosen endurance criteria. Timing data relating to erasure and programming operations is gathered as the basis for modelling. The purpose of this paper is to determine which methods can be used on this data to accurately and efficiently predict endurance. Traditional statistical classification methods, support vector machines and genetic programming are compared. Cross-validating on common datasets, the classification methods are evaluated for applicability, accuracy and efficiency and their respective advantages and disadvantages are quantified.", } @InProceedings{Arcanjo:2011:GECCO, author = "Filipe {de Lima Arcanjo} and Gisele Lobo Pappa and Paulo Viana Bicalho and {Wagner Meira, Jr.} and Altigran Soares {da Silva}", title = "Semi-supervised genetic programming for classification", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1259--1266", keywords = "genetic algorithms, genetic programming, Genetics based machine learning", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001746", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available. Semi-supervised learning, which builds models from a small set of labeled examples and a potential large set of unlabeled examples, is a paradigm that may effectively use those unlabeled data. Here we propose KGP, a semi-supervised transductive genetic programming algorithm for classification. Apart from being one of the first semi-supervised algorithms, it is transductive (instead of inductive), i.e., it requires only a training dataset with labeled and unlabeled examples, which should represent the complete data domain. The algorithm relies on the three main assumptions on which semi-supervised algorithms are built, and performs both global search on labeled instances and local search on unlabeled instances. Periodically, unlabeled examples are moved to the labeled set after a weighted voting process performed by a committee. Results on eight UCI datasets were compared with Self-Training and KNN, and showed KGP as a promising method for semi-supervised learning.", notes = "Also known as \cite{2001746} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{Archanjo:2011:IRI, author = "Gabriel A. Archanjo and Fernando J. {Von Zuben}", title = "Induction of linear genetic programs for relational database manipulation", booktitle = "IEEE International Conference on Information Reuse and Integration (IRI 2011)", year = "2011", month = "3-5 " # aug, pages = "347--352", address = "Las Vegas, USA", size = "6 pages", abstract = "In virtually all fields of human activity, softwares are used to manage processes and manipulate information, usually stored in computer databases. In fields like Knowledge Discovery and Data Mining (KDD), different approaches have been used to extract patterns or more meaningful information from datasets, including genetic programming. Nevertheless, the induction of programs that not only query data, but also manipulate it, has not been widely explored. This work presents Linear Genetic Programming for Databases (LGPDB), a tool to induce programs manipulating entities stored in a relational database. It combines a Linear Genetic Programming (LGP) induction environment and a simple relational database management system (RDBMS). A hypothetical library system is used to show LGPDB in action. Programs were induced to provide a set of selected features for this system and results indicate that genetic programming can be used to model processes that query, delete, insert and update records in a relational database.", keywords = "genetic algorithms, genetic programming, KDD, Knowledge Discovery and Data Mining, LGPDB, Linear Genetic Programming for Databases, hypothetical library system, relational database management system, data mining, relational databases", DOI = "doi:10.1109/IRI.2011.6009572", notes = "Also known as \cite{6009572}", } @Article{Archanjo:2012:ASE, author = "Gabriel A. Archanjo and Fernando J. {Von Zuben}", title = "Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems", journal = "Advances in Software Engineering", year = "2012", pages = "Article ID 893701", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, SQL", ISSN = "16878655", URL = "http://www.hindawi.com/journals/ase/2012/893701/", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=16878655\&date=2012\&volume=2012\&issue=\&spage=", DOI = "doi:10.1155/2012/893701", size = "14 pages", abstract = "Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks.", notes = "Article ID 893701", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:1999bf5251ee08492e4a5bfbdfe2af9a", source = "Advances in Software Engineering", notes = "library books. agile software development, model view controller MVC, in memory, rollback, weakly typed but STGP, elitist. Fitness based on internal state of database rather than results of queries. 14 tables. pop=1000? Is function set complete? 'toward automatic relevant and basic stages of [information technology] IT system development'. Laboratory of Bioinformatics and Bioinspired Computing, School of Electrical and Computer Engineering, University of Campinas (Unicamp), 13083-970 Campinas, SP, Brazil", } @InProceedings{1144042, author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi", title = "Genetic programming for human oral bioavailability of drugs", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "255--262", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p255.pdf", DOI = "doi:10.1145/1143997.1144042", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Biological Applications, bioavailability, bioinformatics, complexity measures, molecular descriptors, performance measures, SVM, ANN, LLSR, CFS, PCA, AIC, feature selection, SMILES", size = "8 pages", abstract = "Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of view of the accurateness of the solutions proposed, of the generalisation capabilities and of the correlation between predicted data and correct ones. Our work represents a first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of molecules into classes with similar bioavailability. Categories and Subject Descriptors", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060 Winner best paper.", } @InProceedings{Archetti:2007:evobio, author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi", title = "Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs", booktitle = "EvoBIO 2007, Proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics", year = "2007", editor = "Elena Marchiori and Jason H. Moore and Jagath C. Rajapakse", volume = "4447", series = "Lecture Notes in Computer Science", pages = "11--23", address = "Valencia, Spain", publisher_address = "Berlin Heidelberg NewYork", month = apr # " 11-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "0302-9743", ISBN = "3-540-71782-X", ISBN-13 = "978-3-540-71782-9", DOI = "doi:10.1007/978-3-540-71783-6_2", abstract = "Computational methods allowing reliable pharmacokinetics predictions for newly synthesised compounds are critically relevant for drug discovery and development. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterise the harmful effects and the distribution into human body of a drug, their accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting pharmacokinetics parameters, both from the point of view of the accurateness and of the generalisation ability.", notes = "EvoBIO2007", } @Article{Archetti:2007:GPEM, author = "Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi", title = "Genetic programming for computational pharmacokinetics in drug discovery and development", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "413--432", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Computational pharmacokinetics, Drug discovery, QSAR, SMILES", ISSN = "1389-2576", URL = "https://rdcu.be/cYj4W", DOI = "doi:10.1007/s10710-007-9040-z", size = "20 pages", abstract = "The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesised compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalisation ability.", notes = "GP, LS2-GP, LS2-C-GP, DF-GP, AIC, Weka ANN, MLP, SVM, Linear regression, PCFS, CorrFS p429 'We have tried to investigate the reasons why GP ...'", } @InProceedings{Archetti:2008:wivace, author = "Francesco Archetti and Mauro Castelli and Ilaria Giordani and Leonardo Vanneschi", title = "Classification of colon tumor tissues using genetic programming", booktitle = "Artificial Life and Evolutionary Computation: Proceedings of Wivace 2008", year = "2008", editor = "J. Roberto Serra and Marco Villani and Irene Poli", pages = "49--58", address = "Venice, Italy", month = "8-10 " # sep, publisher = "World Scientific Publishing Co.", keywords = "genetic algorithms, genetic programming", isbn13 = "9789814287456", URL = "ftp://ftp.ce.unipr.it/pub/cagnoni/WIV08/paper%202.pdf", broken = "http://ebooks.worldscinet.com/ISBN/9789814287456/9789814287456_0004.html", size = "10 pages", abstract = "A Genetic Programming (GP) framework for classification is presented in this paper and applied to a publicly available biomedical microarray dataset representing a collection of expression measurements from colon biopsy experiments [3]. We report experimental results obtained using two different well known fitness criteria: the area under the receiving operating curve (ROC) and the percentage of correctly classified instances (CCI). These results, and their comparison with the ones obtained by three non-evolutionary Machine Learning methods (Support Vector Machines, Voted Perceptron and Random Forests) on the same data, seem to hint that GP is a promising technique for this kind of classification both from the viewpoint of the accuracy of the proposed solutions and of the generalisation ability. These results are encouraging and should pave the way to a deeper study of GP for classification applied to biomedical microarray data sets.", notes = "Workshop Italiano di Vita Artificiale e Computazione Evolutiva http://wivace.unimore.it/ Dept. of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy http://microarray.princeton.edu/oncology/affydata/index.html", } @Article{Archetti2010170, title = "Genetic programming for {QSAR} investigation of docking energy", author = "Francesco Archetti and Ilaria Giordani and Leonardo Vanneschi", journal = "Applied Soft Computing", volume = "10", number = "1", pages = "170--182", year = "2010", month = jan, keywords = "genetic algorithms, genetic programming, Machine learning, Regression, Docking energy, Computational biology, Drug design, QSAR", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2009.06.013", broken = "http://www.sciencedirect.com/science/article/B6W86-4WP47KG-3/2/20419bfc47761543f509e96265d88e5d", abstract = "Statistical methods, and in particular Machine Learning, have been increasingly used in the drug development workflow to accelerate the discovery phase and to eliminate possible failures early during clinical developments. In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced toxicity and (ii) evaluation of the target drug chemical interaction based on chemical descriptors. Among the numerous existing Machine Learning methods and their application to drug development (see for instance [F. Yoshida, J.G. Topliss, QSAR model for drug human oral bioavailability, Journal of Medicinal Chemistry 43 (2000) 2575-2585; Frohlich, J. Wegner, F. Sieker, A. Zell, Kernel functions for attributed molecular graphs - a new similarity based approach to ADME prediction in classification and regression, QSAR and Combinatorial Science, 38(4) (2003) 427-431; C.W. Andrews, L. Bennett, L.X. Yu, Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship, Pharmacological Research 17 (2000) 639-644; J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan, S.S. Young, Predictive toxicology: benchmarking molecular descriptors and statistical methods, Journal of Chemical Information Computer Science 43 (2003) 1463-1470; T.M. Martin, D.M. Young, Prediction of the acute toxicity (96-h LC50) of organic compounds to the fat head minnow (Pimephales promelas) using a group contribution method, Chemical Research in Toxicology 14(10) (2001) 1378-1385; G. Colmenarejo, A. Alvarez-Pedraglio, J.L. Lavandera, Chemoinformatic models to predict binding affinities to human serum albumin, Journal of Medicinal Chemistry 44 (2001) 4370-4378; J. Zupan, P. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, 2nd edition, Wiley, 1999]), we have been specifically concerned with Genetic Programming. A first paper [F. Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development, Genetic Programming and Evolvable Machines 8(4) (2007) 17-26 \cite{Archetti:2007:GPEM}] has been devoted to problem (i). The present contribution aims at developing a Genetic Programming based framework on which to build specific strategies which are then shown to be a valuable tool for problem (ii). In this paper, we use target estrogen receptor molecules and genistein based drug compounds. Being able to precisely and efficiently predict their mutual interaction energy is a very important task: for example, it may have an immediate relationship with the efficacy of genistein based drugs in menopause therapy and also as a natural prevention of some tumours. We compare the experimental results obtained by Genetic Programming with the ones of a set of non-evolutionary Machine Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression. Experimental results confirm that Genetic Programming is a promising technique from the viewpoint of the accuracy of the proposed solutions, of the generalization ability and of the correlation between predicted data and correct ones.", } @Article{Archetti20101395, author = "Francesco Archetti and Ilaria Giordani and Leonardo Vanneschi", title = "Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset", journal = "Computers \& Operations Research", volume = "37", number = "8", pages = "1395--1405", year = "2010", note = "Operations Research and Data Mining in Biological Systems", ISSN = "0305-0548", DOI = "doi:10.1016/j.cor.2009.02.015", URL = "http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0", keywords = "genetic algorithms, genetic programming, Machine learning, Regression, Microarray data, Anticancer therapy, NCI-60", abstract = "Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results, and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the 'best' solutions found by genetic programming are presented.", } @InProceedings{Arcuri:2007:ASE, author = "Andrea Arcuri and Xin Yao", title = "Coevolving Programs and Unit Tests from their Specification", booktitle = "IEEE International Conference on Automated Software Engineering (ASE)", year = "2007", address = "Atlanta, Georgia, USA", month = nov # " 5-9", organisation = "IEEE", keywords = "genetic algorithms, genetic programming, Automatic Programming, Coevolution, Software Testing, Formal Specification, Sorting, SBSE", DOI = "doi:10.1145/1321631.1321693", abstract = "Writing a formal specification before implementing a program helps to find problems with the system requirements. The requirements might be for example incomplete and ambiguous. Fixing these types of errors is very difficult and expensive during the implementation phase of the software development cycle. Although writing a formal specification is usually easier than implementing the actual code, writing a specification requires time, and often it is preferred, instead, to use this time on the implementation. In this paper we introduce for the first time a framework that might evolve any possible generic program from its specification. We use the Genetic Programming to evolve the programs, and at the same time we exploit the specifications to coevolve sets of unit tests. Programs are rewarded on how many tests they do not fail, whereas the unit tests are rewarded on how many programs they make fail. We present and analyse four different problems on which this novel technique is successfully applied.", notes = "http://www.cse.msu.edu/ase2007/", } @InProceedings{Arcuri:2008:ICSEphd, author = "Andrea Arcuri", title = "On the automation of fixing software bugs", booktitle = "ICSE Companion '08: Companion of the 30th international conference on Software engineering", year = "2008", pages = "1003--1006", address = "Leipzig, Germany", publisher_address = "New York, NY, USA", publisher = "ACM", note = "Doctoral symposium session", keywords = "genetic algorithms, genetic programming, co-evolution, SuA, SBSE", isbn13 = "978-1-60558-079-1", URL = "http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf", DOI = "doi:10.1145/1370175.1370223", size = "4 pages", abstract = "Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. Techniques to help the software developers for locating bugs exist though, and they take name of Automated Debugging. However, to our best knowledge, there has been only little attempt in the past to completely automate the actual changing of the software for fixing the bugs. Therefore, in this paper we propose an evolutionary approach to automate the task of fixing bugs. The basic idea is to evolve the programs (e.g., by using Genetic Programming) with a fitness function that is based on how many unit tests they are able to pass. If a formal specification of the buggy software is given, more sophisticated fitness functions can be designed. Moreover, by using the formal specification as an oracle, we can generate as many unit tests as we want. Hence, a co-evolution between programs and unit tests might take place to give even better results. It is important to know that, to fix the bugs in a program with this novel approach, a user needs only to provide either a formal specification or a set of unit tests. No other information is required.", notes = "p1006 {"}We are building our prototype on top of our previous system for AP{"} \cite{Arcuri:2007:ASE} also known as \cite{1370223} Doctoral Symposium of the IEEE International Conference in Software Engineering", } @InProceedings{Arcuri:2008:cec, author = "Andrea Arcuri and Xin Yao", title = "A Novel Co-Evolutionary Approach to Automatic Software Bug Fixing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "162--168", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0063.pdf", DOI = "doi:10.1109/CEC.2008.4630793", abstract = "Many tasks in Software Engineering are very expensive, and that has led the investigation to how to automate them. In particular, Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. In this paper we propose an evolutionary approach to automate the task of fixing bugs. This novel evolutionary approach is based on Co-evolution, in which programs and test cases co-evolve, influencing each other with the aim of fixing the bugs of the programs. This competitive co-evolution is similar to what happens in nature for predators and prey. The user needs only to provide a buggy program and a formal specification of it. No other information is required. Hence, the approach may work for any implementable software. We show some preliminary experiments in which bugs in an implementation of a sorting algorithm are automatically fixed.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{ArcuriWCY08, author = "Andrea Arcuri and David Robert White and John Clark and Xin Yao", title = "Multi-Objective Improvement of Software using Co-evolution and Smart Seeding", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "61--70", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_7", size = "10 pages", abstract = "Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program's semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner.", notes = "Also known as \cite{DBLP:conf/seal/ArcuriWCY08}", bibsource = "DBLP, http://dblp.uni-trier.de", } @TechReport{Arcuri09, author = "Andrea Arcuri", title = "Evolutionary Repair of Faulty Software", institution = "University of Birmingham, School of Computer Science", year = "2009", type = "Technical Report", number = "CSR-09-02", address = "B15 2TT, UK", month = apr, keywords = "genetic algorithms, genetic programming, SBSE", URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2009/CSR-09-02.pdf", size = "34 pages", abstract = "Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed, the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current limits can be addressed in the future. This task is extremely challenging and mainly unexplored in literature. Hence, this paper only covers an initial investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.", notes = "cited by \cite{Ackling:2011:GECCO}", } @InProceedings{Arcuri:2009:SSBSE, author = "Andrea Arcuri", title = "On Search Based Software Evolution", booktitle = "Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009", year = "2009", editor = "Massimiliano {Di Penta} and Simon Poulding", pages = "39--42", address = "Windsor, UK", month = "13-15 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, program coevolution, program test case, search algorithm, software engineering problem, software evolution, program testing, search problems, software engineering", isbn13 = "978-0-7695-3675-0", DOI = "doi:10.1109/SSBSE.2009.12", abstract = "Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems.This framework can be used to tackle software engineering tasks such as automatic refinement, fault correction,improving non-functional criteria and reverse engineering.While the programs evolve to accomplish one of these tasks, test cases are co-evolved at the the same time to find new faults in the evolving programs.", notes = "order number P3675 http://www.ssbse.info/ Also known as \cite{5033178}", } @PhdThesis{Arcuri:thesis, author = "Andrea Arcuri", title = "Automatic software generation and improvement through search based techniques", school = "School of Computer Science, University of Birmingham", year = "2009", address = "UK", month = aug, keywords = "genetic algorithms, genetic programming, SBSE", URL = "http://etheses.bham.ac.uk/400/1/Arcuri09PhD.pdf", URL = "http://etheses.bham.ac.uk/400/", size = "234 pages", abstract = "Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis we also give the important contribution of defining a first theoretical foundation.", } @Article{Arcuri2010, author = "Andrea Arcuri and Xin Yao", title = "Co-evolutionary automatic programming for software development", journal = "Information Sciences", year = "2014", volume = "259", pages = "412--432", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2009.12.019", URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34WFM-2/2/6700572128cf209a061759f28c5b7020", keywords = "genetic algorithms, genetic programming, SBSE, STGP, Automatic programming, Automatic refinement, Co-evolution, Software testing", size = "21 pages", abstract = "Since the 1970s the goal of generating programs in an automatic way (i.e., Automatic Programming) has been sought. A user would just define what he expects from the program (i.e., the requirements), and it should be automatically generated by the computer without the help of any programmer. Unfortunately, this task is much harder than expected. Although transformation methods are usually employed to address this problem, they cannot be employed if the gap between the specification and the actual implementation is too wide. In this paper we introduce a novel conceptual framework for evolving programs from their specification. We use genetic programming to evolve the programs, and at the same time we exploit the specification to co-evolve sets of unit tests. Programs are rewarded by how many tests they do not fail, whereas the unit tests are rewarded by how many programs they make to fail. We present and analyse seven different problems on which this novel technique is successfully applied.", notes = "competitive co-evolution. MaxValue, AllEqual, Triangle Classification, Swap, Order, Sorting, Median. One variable called result write_result read_result. Simple first order logic specification. Java, ECJ. Random sampling SSP, ensemble N-version programming.", } @Article{Arcuri20113494, author = "Andrea Arcuri", title = "Evolutionary repair of faulty software", journal = "Applied Soft Computing", volume = "11", number = "4", pages = "3494--3514", year = "2011", ISSN = "1568-4946", URL = "http://crest.cs.ucl.ac.uk/fileadmin/crest/sebasepaper/Arcurid09d.pdf", DOI = "doi:10.1016/j.asoc.2011.01.023", URL = "http://www.sciencedirect.com/science/article/B6W86-5223XWX-1/2/5d81be4fc12644887723df167e134516", keywords = "genetic algorithms, genetic programming, Repair, Fault localisation, Automated debugging, Search Based Software Engineering, Coevolution", abstract = "Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed, the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current limitations can be addressed in the future. This task is extremely challenging and mainly unexplored in the literature. Hence, this paper only covers an initial investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.", } @InProceedings{Ardeh:2020:SSCI, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "A GPHH with Surrogate-assisted Knowledge Transfer for Uncertain Capacitated Arc Routing Problem", year = "2020", pages = "2786--2793", abstract = "The Uncertain Capacited Arc Routing Problem is an important and challenging problem that has many real-world applications. Genetic Programming is used to evolve routing policies for vehicles to make real-time decisions and handle uncertain environments efficiently. However, when the problem scenario changes (e.g. a new vehicle is bought or an existing vehicle breaks down), the previously trained routing policy becomes ineffective and a new routing policy needs to be retrained. The retraining process is time-consuming. On the other hand, by extraction and transfer of some knowledge learned from the previous similar problems, the efficiency and effectiveness of the retraining process can be improved. Previous studies have found that the lack of diversity in the transferred materials (e.g. sub-trees) could hurt the effectiveness of transfer learning. As a result, instead of using the genetic materials from a source domain directly, in this work, we use the knowledge from the source domain to create a surrogate model. This surrogate is used on a large number of randomly generated individuals by GP in the target domain to select the promising initial individuals. This way, the diversity of the initial population can be maintained by randomly generated individuals, but also guided by the transferred surrogate model. Our experiments demonstrate that the proposed surrogate-assisted transfer learning method is superior to existing methods and can improve training efficiency and final performance of GP in the target domain.", keywords = "genetic algorithms, genetic programming, Routing, Task analysis, Statistics, Sociology, Learning systems, Knowledge transfer, Training", DOI = "doi:10.1109/SSCI47803.2020.9308398", month = dec, notes = "Also known as \cite{9308398}", } @InProceedings{Ardeh:2020:CEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24067", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185714", abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem with extensive real-world applications. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, whenever a UCARP scenario changes, e.g. when a new vehicle is bought, the previously trained routing policy may no longer work effectively, and one has to retrain a new policy. Retraining a new policy from scratch can be time-consuming but the transfer of knowledge gained from solving the previous similar scenarios may help improve the efficiency of the retraining process. In this paper, we propose a novel transfer learning method by learning the probability distribution of good solutions from source domains and modeling it as a probabilistic prototype tree. We demonstrate that this approach is capable of capturing more information about the source domain compared to transfer learning based on (sub-)tree transfers and even create good trees that are not seen in source domains. Our experimental results showed that our method made the retraining process more efficient and one can obtain an initial state for solving difficult problems that is significantly better than existing methods. The final performance of all algorithms, were comparable, implying that there was no negative transfer.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185714}", } @InProceedings{ardeh:2020:AI, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "A Parametric Framework for Genetic Programming with Transfer Learning for Uncertain Capacitated Arc Routing Problem", booktitle = "AI 2020: Advances in Artificial Intelligence", year = "2020", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-64984-5_12", DOI = "doi:10.1007/978-3-030-64984-5_12", } @InProceedings{Ardeh:2021:CEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)", title = "Surrogate-Assisted Genetic Programming with Diverse Transfer for the Uncertain Capacitated Arc Routing Problem", year = "2021", editor = "Yew-Soon Ong", pages = "628--635", address = "Krakow, Poland", month = "28 " # jun # "-1 " # jul, isbn13 = "978-1-7281-8393-0", abstract = "The Uncertain Capacited Arc Routing Problem (UCARP) is an important routing problem that can model uncertainties of real-world scenarios. Genetic Programming (GP) is a powerful method for evolving routing policies for vehicles to enable them make real-time decisions and handle environmental uncertainties. When facing various problem domains, knowledge transfer can improve the effectiveness of the GP training. Previous studies have demonstrated that due to the existence of duplicated GP individuals in the source domain, the existing transfer learning methods do not perform satisfactorily for UCARP. To address this issue, in this work, we propose a method for detecting duplicates in the source domain and initialising the GP population in the target domain with phenotypically unique individuals. Additionally, since the presence of duplicates can limit the number of good GP individuals, we propose a surrogate-assisted initialisation approach that is able to generate much more diversely distributed initial individuals in the target domain. Our experiments demonstrate that our proposed transfer learning method can significantly improve the effectiveness of GP for training new UCARP routing policies. Compared with the state-of-the-art GP with knowledge transfer, the proposed approach can obtain significantly better solutions on a wide range of UCRP instances, in terms of both initial and final quality.", keywords = "genetic algorithms, genetic programming, Training, Adaptation models, Uncertainty, Transfer learning, Sociology, Routing", DOI = "doi:10.1109/CEC45853.2021.9504817", notes = "Also known as \cite{9504817}", } @InProceedings{Ardeh:2021:GECCO, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "A Novel Multi-Task Genetic Programming Approach to Uncertain Capacitated Arc Routing Problem", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "759--767", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Uncertain Capacitated Arc Routing Problem, Hyper Heuristics, Multi-task Optimisation", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459322", size = "9 pages", abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is an NP-hard optimisation problem with many applications in logistics domains. Genetic Programming (GP) is capable of evolving routing policies to handle the uncertain environment of UCARP. There are many different but related UCARP domains in the real world to be solved (e.g. winter gritting and waste collection for different cities). Instead of training a routing policy for each of them, we can use the multi-task learning paradigm to improve the training effectiveness by sharing the common knowledge among the related UCARP domains. Previous studies showed that GP population for solving UCARP loses diversity during its evolution, which decreases the effectiveness of knowledge sharing. we propose a novel multi-task GP approach that takes the uniqueness of transferable knowledge, as well as its quality, into consideration. Additionally, the transferred knowledge in a manner that improves diversity. We investigated the performance of the proposed method with several experimental studies and demonstrated that the designed knowledge transfer mechanism can significantly improve the performance of GP for solving UCARP", notes = "Victoria University of Wellington GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @PhdThesis{Ansari_Ardeh2022, author = "Mazhar {Ansari Ardeh}", title = "{Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem}", school = "Computer Science, Victoria University of Wellington", year = "2022", address = "New Zealand", month = "15 " # jul, keywords = "genetic algorithms, genetic programming, UCARP, Evolutionary computation, Fuzzy computation, Transfer Optimisation, Uncertain Capacitated Arc Routing Problem", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/36279690", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Transfer_Optimisation_in_Genetic_Programming_for_Solving_Uncertain_Capacitated_Arc_Routing_Problem/20311185", DOI = "doi:10.26686/wgtn.20311185", size = "286 pages", abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimisation problem with many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Reusability is an open issue in the field of UCARP and in this direction, an open challenge is the case of scenario changes, e.g. change in the number of vehicles and probability distributions of random demands, which typically requires new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem-solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. The overall goal of this thesis is to develop novel knowledge transfer algorithms for GP for solving UCARP to handle environment changes more effectively and efficiently. To fulfill this goal, a plethora of machine learning techniques, i.e. surrogate models, feature selection, searching and specialised genetic operators, are used in this thesis. First, this thesis explores the effectiveness of the existing transfer optimisation methods for solving UCARP. Accordingly, one of the main directions of this thesis is towards identifying the nature of transferable knowledge, which can impact the quality of knowledge transfer for GP to solve UCARP. For this purpose, a collection of the state-of-the-art transfer optimisation GP algorithms are evaluated for UCARP. After identifying some potential gaps in the literature, a number of preliminary transfer optimisation algorithms are proposed that supplement the literature. To evaluate the algorithms, a large set of knowledge transfer scenarios with various source and target problems were designed based on real-world datasets. According to the results, none of the methods showed significant improvement in the effectiveness of the trained UCARP routing policies. These results revealed the need for more effective transfer optimisation methods specifically designed for UCARP. Furthermore, our investigations revealed that the presence of duplicates in knowledge sources is one of the main challenges for effective transfer optimisation in solving UCARP. Second, we propose approaches to handling the presence of duplicates in the transferred knowledge. The first approach increases population diversity after knowledge transfer to counteract the loss of diversity that is introduced by the presence of duplicates in the transferred knowledge. In the second approach, the duplicates are removed from the transferred knowledge. Then, the transferred knowledge is used to create a diverse initial GP population of high-quality individuals. Both approaches are investigated through detailed experimental studies. The results indicate that, while the first approach did not perform better than GP with knowledge transfer, the second can improve the effectiveness of training routing policies with GP significantly. Third, this thesis proposes a novel algorithm that transfers the phenotypic characteristics of the routing policies for solving the source problem. In the new algorithm, the most fit and unique source routing policies are used for initialising GP for solving the target problem. Then, a tabu list is placed on the source routing policies and the GP process is prohibited from recreating any of the source routing policies. The motivation for this approach is that, due to the existence of similarity between the source and target problems, source routing policies are unlikely to have a good performance for the target problem. Our experimental studies confirmed that by prohibiting GP from recreating source policies, and the computational resources will be spent on searching and evaluating new regions of the search space, which can lead to discovering better solutions. Fourth, this thesis proposes a novel knowledge transfer algorithm based on the idea of maintaining the transferred knowledge as an auxiliary population. In this approach, first, the best individuals of the duplicate-free knowledge source are used to initialise GP. Additionally, these transferred individuals are also maintained as an auxiliary population and are evolved alongside the main population. To save the computational cost, the auxiliary population is evolved with a surrogate method. Additionally, an elaborate knowledge exchange mechanism between the two populations is devised that emphasises transferring high-quality and unique individuals, the transfer of which can improve the diversity of the receiving population. This allows GP to overcome the problem of losing its population diversity during the evolutionary process. Our detailed experimental results confirmed the superior performance of the proposed algorithm and confirmed that the proposed method improved the phenotypic diversity of GP population.", notes = "Supervisors: Yi Mei and Mengjie Zhang", } @Article{Ardeh:2022:ieeeTEC, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang", title = "Genetic Programming With Knowledge Transfer and Guided Search for Uncertain Capacitated Arc Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "4", pages = "765--779", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3129278", abstract = "The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming (GP) is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this article, we propose a novel GP method that removes phenotypic duplicates from the source individuals to initialize the target individuals. Furthermore, assuming that the transferred knowledge used in initialization already includes all the important knowledge ex", notes = "also known as \cite{9621218}", } @Article{Ardeh:ieeeTEC2, author = "Mazhar {Ansari Ardeh} and Yi Mei and Mengjie Zhang and Xin Yao", title = "Knowledge Transfer Genetic Programming with Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "2", pages = "311--325", month = apr, keywords = "genetic algorithms, genetic programming, Arc routing, GP, hyper-heuristics, transfer optimization", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3169289", size = "15 pages", abstract = "The uncertain capacitated arc routing problem is an NP-hard combinatorial optimisation problem with a wide range of applications in logistics domains. Genetic programming hyper-heuristic has been successfully applied to evolve routing policies to effectively handle the uncertain environment in this problem. The real world usually encounters different but related instances due to events like season change and vehicle breakdowns, and it is desirable to transfer knowledge gained from solving one instance to help solve another related one. However, the solutions found by the genetic programming process can lack diversity, and the existing methods use the transferred knowledge mainly during initialisation. Thus, they cannot sufficiently handle the change from the source to the target instance. To address this issue, we develop a novel knowledge transfer genetic programming with an auxiliary population. In addition to the main population for the target instance, we initialise a", notes = "also known as \cite{9761253}", } @InCollection{ardell:1994:TOPE, author = "David H. Ardell", title = "TOPE and Magic Squares: A Simple GA Approach to Combinatorial Optimization", booktitle = "Genetic Algorithms at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "1--6", address = "Stanford, California, 94305-3079 USA", month = dec, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-187263-3", notes = "Uses Genesis This volume contains 20 papers written and submitted by students describing their term projects for the course {"}Genetic Algorithms and Genetic Programming{"} (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html", } @Article{DBLP:journals/rcs/ArellanoR19, author = "Humberto Velasco Arellano and Martin Montes Rivera", title = "Forward Kinematics for 2 {DOF} Planar Robot using Linear Genetic Programming", journal = "Research in Computing Science", volume = "148", number = "6", pages = "123--133", year = "2019", keywords = "genetic algorithms, genetic programming, forward kinematics, automatic robot modeling, linear genetic programming", ISSN = "1870-4069", URL = "https://www.rcs.cic.ipn.mx/2019_148_6/Forward%20Kinematics%20for%202%20DOF%20Planar%20Robot%20using%20Linear%20Genetic%20Programming.pdf", timestamp = "Wed, 17 Feb 2021 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/rcs/ArellanoR19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "11 pages", abstract = "In the field of robotics, forward kinematics is an activity that allows finding a mathematical model for the resulting position in the final effector based on the robot joints position, a popular alternative for determining this model is defined by the Denavit Hartenberg convention, nevertheless, this method requires knowledge about linear algebra and three-dimensional spatial kinematics. Machine learning uses specific computational methodologies to solving similar problems in several areas, so it could be a viable answer for automatic determining of forwarding kinematics. we propose the use of genetic programming as a machine learning algorithm for finding the forward kinematics of a 2 degrees of freedom robot, getting a satisfactory outcome obtaining a satisfactory result with blocks that describe the expected solution, validating the capacity of the genetic programming in order to validate this algorithm for later work with more complex robots.", notes = "https://www.rcs.cic.ipn.mx/", } @Article{Arganis:2009:AiCE, title = "Genetic Programming and Standardization in Water Temperature Modelling", author = "Maritza Arganis and Rafael Val and Jordi Prats and Katya Rodriguez and Ramon Dominguez and Josep Dolz", journal = "Advances in Civil Engineering", year = "2009", volume = "2009", publisher = "Hindawi Publishing Corporation", keywords = "genetic algorithms, genetic programming", ISSN = "16878086", URL = "http://downloads.hindawi.com/journals/ace/2009/353960.pdf", DOI = "doi:10.1155/2009/353960", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:c20c6fc231b4ba552b81c9f42d58f35f", abstract = "An application of Genetic Programming (an evolutionary computational tool) without and with standardization data is presented with the aim of modeling the behavior of the water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their applicability to forecasting purposes.", notes = "Article ID 353960", } @InCollection{Arganis:2012:GPnew, author = "M. L. Arganis and R. Val and R. Dominguez and K. Rodriguez and Josep Dolz and J. M. Eaton", title = "Comparison Between Equations Obtained by Means of Multiple Linear Regression and Genetic Programming to Approach Measured Climatic Data in a River", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "11", pages = "239--254", keywords = "genetic algorithms, genetic programming", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/50556", size = "16 pages", notes = "Asco Nuclear Power Station, Ebro River, Spain. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @Article{Arganis:2015:REDIN, author = "Maritza Liliana {Arganis Juarez} and Margarita {Preciado-Jimenez} and Katya {Rodriguez-Vazquez}", title = "Daily rainfall interpolation models obtained by means of genetic programming", journal = "Revista Facultad de Ingenieria Universidad de Antioquia", year = "2015", number = "75", pages = "189--201", month = may, keywords = "genetic algorithms, genetic programming, daily rainfall, genetic programming, interpolation models, isohyet, geographic coordinates, Co Kriging, missing data, Matlab, 29 July 2006", ISSN = "0120-6230", URL = "https://revistas.udea.edu.co/index.php/ingenieria/article/view/21564/18766", DOI = "doi:10.17533/udea.redin.n75a18", size = "13 pages", abstract = "The evolutionary computing algorithm of genetic programming was applied to obtain mathematical daily rainfall interpolation models in one climatologic station, using the measured data in nearby stations in Cutzamala River basin in Mexico. The obtained models take into account both the geographical coordinates of the climatologic station and also its elevation; the answer of these models was compared against those obtained by means of multiple linear regression and a nonlinear model with parameters obtained with genetic algorithms; genetic programming models gave the best performance. Isohyets maps were then obtained to compare the spatial shapes between measured and calculated rainfall data in Cutzamala River Basin, for a maximum historic storm recorded in 2006 year, showing an adequate agreement of the results in case of rainfalls greater than 23 mm. Genetic programming represent a useful practical tool for approaching mathematical models of variables applied in engineering problems and new models could be obtained in several basins by applying these algorithms.", resumen = "Se aplico el algoritmo de computo evolutivo de programacion genetica (PG) para obtener modelos matematicos de interpolacion de precipitacion diaria en una estacion climatologica, utilizando datos medidos en las estaciones cercanas a la cuenca del Rio Cutzamala en Mexico. Los modelos obtenidos toman en cuenta tanto las coordenadas geograficas de las estaciones climatologicas como su elevacion; la respuesta de los modelos se comparo contra los resultados obtenidos con ayuda de regresiones lineales multiples, presentando un mejor desempeno programacion genetica. Adicionalmente, se construyeron mapas de isoyetas para comparar las formas espaciales entre los datos de precipitacion medidos y calculados en la cuenca del Rio Cutzamala para una tormenta maxima historica registrada en el ano 2006, observandose concordancia en los resultados en el caso de precipitaciones mayores de 23 mm. La programacion genetica representa una herramienta de utilidad practica para aproximar modelos matematicos de variables aplicadas en problemas de ingenieria y se pueden obtener nuevos modelos en distintas cuencas al aplicar estos algoritmos.", notes = "UNAM, Instituto de Ingenieria, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico p199 'Using the GP model (eq 3) also gave higher correlations than those used with the simple regression model'", } @Article{ARGANISJUAREZ:2022:jksus, author = "Maritza Liliana {Arganis Juarez} and Maria Fernanda {Hernandez Ignacio} and Sandra Lizbeth {Rosales Silvestre} and Javier {Osnaya Romero} and Eliseo {Carrizosa Elizondo}", title = "Evaluation of the capacity of {PET} bottles, water aeration, and water recirculation to reduce evaporation in containers of water", journal = "Journal of King Saud University - Science", volume = "34", number = "4", pages = "102046", year = "2022", ISSN = "1018-3647", DOI = "doi:10.1016/j.jksus.2022.102046", URL = "https://www.sciencedirect.com/science/article/pii/S1018364722002270", keywords = "genetic algorithms, genetic programming, Water evaporation, Evaporation reduction, PET plastic bottles, Principal component analysis", abstract = "Objective: To evaluate the effectiveness of an evaporation reduction method in which the greater part of the water surface was covered with PET-type plastic bottles. The capacity of this method to diminish natural evaporation was compared to water aeration, water recirculation, and the control (without intervention). With evolutionary computation, including genetic algorithms and genetic programming, equations for calculating evaporation were developed based on meteorological factors. Methods Four containers of water were placed on a flat roof in Mexico City (thus exposed to the factors of weather), and evaporation was measured daily with an evaporimeter. Each container was assigned to one of the evaporation reduction methods (PET bottles, water aeration, or water recirculation) or to the control (without intervention). Evaporation-related variables were selected according to previous reports and principal component analysis, and their values were acquired from a nearby meteorological station. The study was conducted from April of 2020 to February of 2021. Results Covering the water surface with PET bottles avoided 38.61percent (a total of 139mm) of natural evaporation, which is represented by the control. The water aeration and water recirculation methods diminished evaporation by 7.22percent (26mm) and 2.22percent (8mm), respectively. The best equations for estimating evaporation were obtained with genetic programming for the control container and a genetic algorithm for the container with PET bottles. Conclusions The PET bottle method of evaporation reduction was 7 and 17 times more effective than water aeration and water recirculation, respectively. The 38.61percent decrease in evaporation achieved by covering the water surface with PET bottles constitutes a substantial savings in water. Hence, the implementation of such a method should be considered to contribute to water conservation in reservoirs. The use of PET bottles is a practical and inexpensive method requiring only a few cleaning maneuvers to prevent the proliferation of unwanted aquatic fauna.", } @Article{Argyri2012, author = "Anthoula A. Argyri and Roger M. Jarvis and David Wedge and Yun Xu and Efstathios Z. Panagou and Royston Goodacre and George-John E. Nychas", title = "A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage", journal = "Food Control", volume = "29", number = "2", pages = "461--470", year = "2013", note = "Predictive Modelling of Food Quality and Safety", ISSN = "0956-7135", DOI = "doi:10.1016/j.foodcont.2012.05.040", URL = "http://www.sciencedirect.com/science/article/pii/S0956713512002745", keywords = "genetic algorithms, genetic programming, Meat spoilage, Raman spectroscopy, FT-IR, Multivariate analysis, Evolutionary computing", abstract = "In this study, time series spectroscopic, microbiological and sensory analysis data were obtained from minced beef samples stored under different packaging conditions (aerobic and modified atmosphere packaging) at 5 C. These data were analysed using machine learning and evolutionary computing methods, including partial least square regression (PLS-R), genetic programming (GP), genetic algorithm (GA), artificial neural networks (ANNs) and support vector machines regression (SVR) including different kernel functions [i.e. linear (SVRL), polynomial (SVRP), radial basis (RBF) (SVRR) and sigmoid functions (SVRS)]. Models predictive of the microbiological load and sensory assessment were calculated using these methods and the relative performance compared. In general, it was observed that for both FT-IR and Raman calibration models, better predictions were obtained for TVC, LAB and Enterobacteriaceae, whilst the FT-IR models performed in general slightly better in predicting the microbial counts compared to the Raman models. Additionally, regarding the predictions of the microbial counts the multivariate methods (SVM, PLS) that had similar performances gave better predictions compared to the evolutionary ones (GA-GP, GA-ANN, GP). On the other hand, the GA-GP model performed better from the others in predicting the sensory scores using the FT-IR data, whilst the GA-ANN model performed better in predicting the sensory scores using the Raman data. The results of this study demonstrate for the first time that Raman spectroscopy as well as FT-IR spectroscopy can be used reliably and accurately for the rapid assessment of meat spoilage.", } @Article{Ari:2021:SAUJS, author = "Davut Ari and Baris Baykant Alagoz", title = "A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications", journal = "Sakarya University Journal of Science", year = "2021", volume = "25", number = "2", pages = "397--416", month = apr, keywords = "genetic algorithms, genetic programming, gp types, gp applications, gp software", publisher = "Sakarya University", ISSN = "2147-835X", URL = "http://www.saujs.sakarya.edu.tr/en/pub/issue/60672/793333", URL = "https://dergipark.org.tr/en/download/article-file/1284370", DOI = "doi:10.16984/saufenbilder.793333", size = "20 pages", abstract = "Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.", notes = "also known as \cite{article_793333} SAUJS http://www.saujs.sakarya.edu.tr/en/", } @InProceedings{Ari:2021:ICIT, author = "Davut Ari and Baris Baykant Alagoz", title = "A Genetic Programming Based Pollutant Concentration Predictor Design for Urban Pollution Monitoring Based on Multi-Sensor Electronic Nose", booktitle = "2021 International Conference on Information Technology (ICIT)", year = "2021", pages = "168--172", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICIT52682.2021.9491122", month = jul, abstract = "An important part of air pollution control is the pollution monitoring. Since industrial spectrometers are expensive equipment, the number of observation points to monitor air pollution over an urban area can be limited. The low-cost multi-sensors network can spread over areas and form a wide-area electronic nose to estimate pollutant concentration distributions. However, the collected multisensor data should be analysed to correctly estimate pollutant concentrations. This study demonstrates implementation of genetic programming (GP) to obtain prediction models that can estimate CO and NO2 concentrations from multisensor electronic nose data. For this purpose, to function as an electronic nose, a regression model from a training data set is obtained by using a tree-based GP algorithm. In order to improve performance of the GP based prediction models, data normalization is performed and prediction performance enhancements are demonstrated via statistical performance analyses on a test data set.", notes = "Also known as \cite{9491122}", } @InProceedings{conf/cit/AriA21a, author = "Davut Ari and Baris Baykant Alagoz", title = "Modeling Daily Financial Market Data by Using Tree-Based Genetic Programming", booktitle = "2021 International Conference on Information Technology, ICIT", year = "2021", pages = "382--386", address = "Amman, Jordan", month = "14-15 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-6654-2870-5", bibdate = "2021-10-14", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cit/cit2021.html#AriA21a", DOI = "doi:10.1109/ICIT52682.2021.9491652", abstract = "A behavioural modelling of financial markets based on daily data is not an easy problem for machine learning algorithms. The social and physiological factors can take effect on market data and result in significant uncertainty in data. This study demonstrates an implementation of tree-based genetic programming (GP) to develop a mathematical model of stock market from the daily stock data of other stock markets to observe relations between global market trends and to consider this effect in market prediction problems. To obtain a prediction model of Istanbul Stock Exchange 100 Index (ISE100), numerical data from ISE100 and seven other international stock market indices are used to produce GP models that can estimate daily price changes in ISE100 according to daily change in other international stock market indices. To reduce negative effects of the data uncertainty on the GP modelling, ensemble average GP modelling performances are investigated and the results are reported for future research direction suggestions.", } @Article{ari:2022:NCaA, author = "Davut Ari and Baris Baykant Alagoz", title = "An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application", journal = "Neural Computing and Applications", year = "2022", volume = "34", number = "15", keywords = "genetic algorithms, genetic programming, ANN", URL = "http://link.springer.com/article/10.1007/s00521-022-07129-0", DOI = "doi:10.1007/s00521-022-07129-0", } @PhdThesis{DBLP:phd/tr/Ari23, author = "Davut Ari", title = "The genetic programming and its applications in engineering", title_tr = "Genetik programlama ve m{\"{u}}hendislikte uygulamalar{\i}", school = "{\.{I}}n{\"{o}}n{\"{u}} University, Turkey", year = "2023", month = jan, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Hava kirliligi = Air pollution, Veri analizi = Data analysis", URL = "https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=5cvay5p_jJQNEtvmnO5fww&no=HNOMlxzz-uJmirw8rmMHEQ", timestamp = "Sun, 29 Oct 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/phd/tr/Ari23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "123 pages", abstract = "Genetic Programming (GP) is an evolutionary computational method that can generate symbolic and mathematical models. In addition to being a type of evolutionary computing, the GP is also frequently used in solving symbolic regression problems in machine learning applications. Since its first appearance, it has become one of the popular evolutionary calculation methods as a result of being successfully applied for solution of modeling problems appeared in many different disciplines. Within the scope of this thesis, research studies have been carried out for development of data driven prediction models and their engineering applications by using the classical GP and its a variant, Gene Expression Programming (GEP). In order to increase the effectiveness of these GP methods in practice, data normalization, ensemble learning, hybrid model development and hyperparameter optimization techniques are studied. In addition, the chromosome structure of the GEP method has been modified and an optimal solution to the constant value determination problem has been proposed. Then, the modified GEP method was combined with popular metaheuristic optimization methods, and thus a metaheuristic optimization based GEP (MetaSezGEP) approach was developed. Contributions of these improvements to some engineering applications have been investigated.", notes = "in Turkish Tez No 774886. 36183619043 Supvervisor: Baris Baykant ALAGOZ", } @Article{ari:2023:SC, author = "Davut Ari and Baris Baykant Alagoz", title = "{DEHypGpOls:} a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction", journal = "Soft Computing", year = "2023", volume = "27", number = "5", pages = "2553--2574", month = mar, keywords = "genetic algorithms, genetic programming, Stock market prediction, Stock price, Hyperparameter optimization, Trend prediction", ISSN = "1432-7643", URL = "https://rdcu.be/daFKI", URL = "http://link.springer.com/article/10.1007/s00500-022-07571-1", DOI = "doi:10.1007/s00500-022-07571-1", size = "22 pages", abstract = "Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87 percent average accuracy in buy-sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8 percent more average income compared to the average income of a long-term investment strategy.", } @Article{Ari:2023:ASOC, author = "Davut Ari and Baris Baykant Alagoz", title = "A differential evolutionary chromosomal gene expression programming technique for electronic nose applications", journal = "Applied Soft Computing", year = "2023", volume = "136", pages = "110093", month = mar, keywords = "genetic algorithms, genetic programming, Gene expression programming, Air quality electronic nose, Differential evolution, Sensor calibration", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623001114", DOI = "doi:10.1016/j.asoc.2023.110093", abstract = "The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.", notes = "also known as \cite{ARI2023110093}", } @InProceedings{Arif:2017:ICTAI, author = "Muhammad Hassan Arif and Jianxin Li and Muhammad Iqbal", booktitle = "2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)", title = "Solving Social Media Text Classification Problems Using Code Fragment-Based XCSR", year = "2017", pages = "485--492", abstract = "Sentiment analysis and spam detection of social media text messages are two challenging data analysis tasks due to sparse and high-dimensional feature vectors. Learning classifier systems (LCS) are rule-based evolutionary computing systems and have limited capabilities to handle real valued sparse high-dimensional big data sets. LCS techniques use interval based representations to handle real valued feature vectors. In the work presented here, interval based representation is replaced by genetic programming based tree like structures to classify high-dimensional real valued text feature vectors. Multiple experiments are conducted on different social media text data sets, i.e. tweets, film reviews, Amazon and yelp reviews, SMS and Email spam message to evaluate the proposed scheme. Real valued feature vectors are generated from these data sets using term frequency inverse document frequency and/or sentiment lexicons-based features. Results depicts the supremacy of the new encoding scheme over interval based representations in both small and large social media text data sets.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICTAI.2017.00080", ISSN = "2375-0197", month = nov, notes = "The Institute of Advanced Computing Technology, Beihang University, Beijing, China Also known as \cite{8371983}", } @InProceedings{Aristotle-De-Leon:2022:HNICEM, author = "Joseph {Aristotle De Leon} and Mike {Louie Enriquez} and Ronnie Concepcion and Ira Valenzuela and Ryan {Rhay Vicerra} and Homer Co and Argel Bandala and Elmer Dadios", booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)", title = "Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming", year = "2022", abstract = "The inversion process of Vertical Electrical Sounding (VES) is an important step in 1-D subsurface surface surveys to determine the true resistivities and heights of different layers of soil or rocks underground which is beneficial in geological and hydrological applications like locating potential areas for aquifers. Machine learning based algorithms is currently a trend in the inversion of vertical electrical sounding (VES) data to address the issues of the conventional methods. However, most models trained are being limited to one electrode half spacing configuration, and not being able to explain the underlying relationships of the model. Hence, the present study seeks to address these by obtaining VES inversion models for four-layer earth models through genetic programming and a synthetic dataset. The synthetic dataset covering different electrode half spacing configurations and VES curve types was generated and used to train the genetic programming model through GPTIPS software. By testing the best models on the synthetic dataset, it offered good metrics on the true resistivities of each layer, but performed poorly on estimating the layers' heights. Regardless, the models obtained can be symbolically expressed and be interpreted which has not been done in other machine learning inversion models for VES. While this study's implementation of genetic programming is not yet satisfactory, obtaining the symbolic expressions can allow future works to systematically improve the worst performing models.", keywords = "genetic algorithms, genetic programming, Measurement, Electrodes, Earth, Machine learning, Conductivity, Soil, Vertical Electrical Sounding, Inversion, Underground Imaging, Resistivity Imaging", DOI = "doi:10.1109/HNICEM57413.2022.10109565", ISSN = "2770-0682", month = dec, notes = "Also known as \cite{10109565}", } @InProceedings{Arita:1997:hamilton, author = "Masanori Arita and Akira Suyama and Masami Hagiya", title = "A Heuristic Approach for Hamiltonian Path Problem with Molecules", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "DNA Computing", pages = "457--462", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{Arkoudas:2008:AAAIf, author = "Konstantine Arkoudas", title = "Automatically Discovering Euler's Identity via Genetic Programming", booktitle = "AAAI Fall Symposium", year = "2008", editor = "Selmer Bringsjord and Andrew Shilliday", pages = "1--7", address = "Arlington, Virginia, USA", publisher_address = "Menlo Park, California, USA", month = nov # " 7-9", publisher = "AAAI", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-57735-395-9", URL = "http://www.aaai.org/Papers/Symposia/Fall/2008/FS-08-03/FS08-03-001.pdf", size = "7 pages", abstract = "We show that by using machine learning techniques (genetic programming, in particular), Euler's famous identity (V - E + F = 2) can be automatically discovered from a limited amount of data indicating the values of V , E, and F for a small number of polyhedra the five platonic solids. This result suggests that mechanized inductive techniques have an important role to play in the process of doing creative mathematics, and that large amounts of data are not necessary for the extraction of important regularities. Genetic programming was implemented from scratch in SML-NJ.", notes = "Technical Report FS-08-03. Published by The AAAI Press http://www.aaai.org/Press/Reports/Symposia/Fall/fs-08-03.php Cube, triangular prism, pentagonal prism, square pyramid, triangular pyramid, pentagonal pyramid, octahedron, tower, truncated cube. SML. The source code can be downloaded from www.rpi.edu/~arkouk/euler/", } @Article{Arkov:2000:ARC, author = "V. Arkov and C. Evans and P. J. Fleming and D. C. Hill and J. P. Norton and I. Pratt and D. Rees and K. Rodriguez-Vazquez", title = "System Identification Strategies Applied to Aircraft Gas Turbine Engines", journal = "Annual Reviews in Control", volume = "24", pages = "67--81", year = "2000", number = "1", keywords = "genetic algorithms, genetic programming, gas turbines, system identification, frequency domain, multisine signals least-squares estimation, time-varying systems, structure selection", ISSN = "1367-5788", broken = "http://www.sciencedirect.com/science/article/B6V0H-482MDPD-8/2/dd470648e2228c84efe7e14ca3841b7e", DOI = "doi:10.1016/S1367-5788(00)90015-4", size = "15 pages", abstract = "A variety of system identification techniques are applied to the derivation of models of aircraft gas turbine dynamics. The motivation behind the study is to improve the efficiency and cost-effectiveness of system identification techniques currently used in the industry. Four system identification approaches are outlined in this paper. They are based upon: identification using ambient noise only data, multisine testing and frequency-domain identification, time-varying models estimated using extended least squares with optimal smoothing, and multiobjective genetic programming to select model structure.", notes = "Also known as \cite{Arkov200067}", } @Article{journals/ewc/ArmaghaniFMFT18, author = "Danial Jahed Armaghani and Roohollah Shirani Faradonbeh and Ehsan Momeni and Ahmad Fahimifar and Mahmood M. D. Tahir", title = "Performance prediction of tunnel boring machine through developing a gene expression programming equation", journal = "Engineering with Computers", year = "2018", number = "1", volume = "34", pages = "129--141", month = jan, keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "0177-0667", bibdate = "2018-01-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ewc/ewc34.html#ArmaghaniFMFT18", DOI = "doi:10.1007/s00366-017-0526-x", abstract = "The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang-Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R 2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.", } @Article{armaghani:2018:NCaA, author = "Danial Jahed Armaghani and Roohollah Shirani Faradonbeh and Hossein Rezaei and Ahmad Safuan A. Rashid and Hassan Bakhshandeh Amnieh", title = "Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming", journal = "Neural Computing and Applications", year = "2018", volume = "29", number = "11", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s00521-016-2618-8", DOI = "doi:10.1007/s00521-016-2618-8", } @InProceedings{Armani_2010, author = "Umberto Armani and Vassili V. Toropov and Andrey Polynkin and Osvaldo M. Querin and Luis Alvarez", title = "Enhancements to a hybrid genetic programming technique applied to symbolic regression", booktitle = "Proceedings of the 8th ASMO UK / ISSMO conference on Engineering Design Optimization Product and Process Improvement", year = "2010", editor = "Fabian Duddeck and Osvaldo M. Querin and Johann Sienz and Vassili V. Toropov and M. Hasan Shaheed", address = "Queen Mary University of London, UK", month = jul # " 8-9", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-85316-292-6", URL = "http://www.asmo-uk.com/8th-asmo-uk/html/menu_page.html", URL = "http://www.asmo-uk.com/8th-asmo-uk/presentations/session1_presentation5.pdf", abstract = "A major problem in genetic programming techniques is premature convergence, which emerges during evolution as a progressive loss of variability among individuals in the population. Moreover, the mechanisms according to which individuals are created, recombined and evaluated have of course strong influence on the chances of success. Increasing variability of the population and expressivity of the genotype are then major issues for genetic programming techniques. The aim of this paper is to investigate if a hybrid, tree-based GP implementation written for symbolic regression purposes can be improved in terms of reliability and precision of the results both by several modifications of the standard GP components and by pre-processing the input data set. In order to increase variability, the effect of a simple archive updating strategy and of a periodical killing of a large part of the population (with the insertion of new and composed individuals) is assessed. As a promising measure to preserve variation among individuals, a MinMax approach in the definition of the fitness function is also proposed and tested as an alternative to the plain aggregating approach. With regard to expressivity, a simple solution consisting in the definition of a unary function that introduces a translation in the argument of the function itself is put forward. Other experiments are performed to assess if the redefinition of the fitness function using a normalised error can have beneficial effects on the evolution, as an alternative to the common root mean square error. Finally, the splitting of the input data set in two different subsets, respectively for parameter tuning and fitness evaluation, is investigated.", notes = "SQP Rosenbrock KILLandFILL Kotanchek RatPol2D Hock Branin-Hoo Salustowicz session1_presentation5.pdf is slides School of Civil Engineering, University of Leeds, LS2 9JT, UK", } @InProceedings{Armani_2011_1, author = "Umberto Armani and Dirk Jan Boon and Vassili V. Toropov and Andrey Polynkin and Leslie J. Clark and Mary B. Stowe", title = "Generation of models related to aluminium surface treatment using genetic programming", booktitle = "Proceedings of the 9th world congress on structural and multidisciplinary optimization (WCSMO9),", year = "2011", address = "Shizuoka, Japan", month = jun # " 13-17", keywords = "genetic algorithms, genetic programming, hybrid genetic programming, corrosion, model", broken = "http://pbl.eng.kagawa-u.ac.jp/kani/index4.php", URL = "http://pbl.eng.kagawa-u.ac.jp/kani/p/paper246_1.pdf", size = "9 pages", abstract = "Surface treatment in aerospace industry is of paramount importance for protection of metallic structures against corrosion, in particular aluminium alloys. One of the common techniques consists of the generation of a surface coating (through chemical conversion or anodising) followed by the application of a primer paint containing a water soluble chromate salt, such as barium chromate BaCrO4 or strontium chromate SrCrO4. Such treatment allows for corrosion protection of the aluminium alloy in the presence of moisture even in the case of damage to the protective coating, through chemical and mass transfer processes involving the primer, water, the exposed alloy and the chromate salts. The availability of empirical models describing the quantity of chromate dissolving into the aqueous medium is therefore important for understanding the corrosion protection process and it could lead to improvements in the development and qualification of new corrosion protection systems. The main aim of this paper is to provide improved models to describe the quantity of dissolved chromate in water for three different chromate-based primers, considering as independent variables the time treated aluminium alloy samples are left in an aqueous solution and the acidity of the solution. To produce the models a hybrid genetic programming technique is used. Its role is to generate models through symbolic regression on experimental data provided by industry. Being a non-parametric regression technique, genetic programming is successful in finding a range of models whose mathematical structure is different from existing ones", } @InProceedings{Armani_2011_2, author = "U. Armani and Z. Khatir and Amirul Khan and V. V. Toropov and A. Polynkin and H. Thompson and N. Kapur and C. J. Noakes", title = "Control of Physical Consistency in Metamodel Building by Genetic Programming", booktitle = "Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering (CSC2011)", year = "2011", editor = "Y. Tsompanakis and B. H. V. Topping", pages = "Paper 43", address = "Chania, Greece", publisher_address = "Stirlingshire, UK", publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, high-fidelity design optimisation, metamodel, mathematical structure, non-linear system, analytical expression, engineering applications", URL = "http://www.ctresources.info/ccp/paper.html?id=6631", DOI = "doi:10.4203/ccp.97.43", size = "18 pages", abstract = "Soft computing has grown in importance in recent years, allowing engineers to handle more and more complex problems. Computer power has made different classes of computationally intensive techniques viable and successful alternatives to other established methods. Algorithms based on machine learning, data mining and genetically inspired methods are in some cases the only choice when the knowledge of the problem is scarce. Genetic programming (GP) [1] can be considered one of the latest techniques to have appeared in the range of soft computing tools. It is a genetically-inspired method able to generate from a data set global metamodels describing the relationship between a system's input and output data. Typically, genetic operators are used to recombine parts of mathematical expressions in a randomised but directed way until a high quality metamodel (i.e. a model of a model) is found. The major strength of genetic programming lies in its ability to provide explicit metamodels, making possible the use of traditional analytical methods for the subsequent analysis and optimisation. A problem arises that the stochastic nature of GP reduces the possibility of controlling the consistency of the generated metamodels. It is not uncommon in a conventional GP experiment to obtain expressions that despite showing low errors cannot be used in an application as their response is not consistent with the assumptions imposed by the problem's nature. In this paper it is described how control of the physical consistency of the generated metamodels can be improved using some basic knowledge regarding the problem at hand by imposing constraints in the problem formulation. The benefits of the new strategy are shown through a benchmark problem. Two case studies where genetic programming has been successfully applied to optimise the ventilation design of an industrial bread baking oven and of a hospital ward are also presented. In both cases data provided by computational fluid dynamics (CFD) simulations were used to generate a metamodel and genetic algorithm techniques were used to find the optimum of the modelled response. Validation of the optimal point performed using data generated by additional CFD simulations confirmed the high quality of the metamodels. In a case study the optimum found by genetic programming matches the optimum found by another metamodelling technique.", } @InProceedings{Armani_2012, author = "U. Armani and S. Coggon and V. V. Toropov", title = "Derivation of Deterministic Design Data from Stochastic Analysis in the Aircraft Design Process", booktitle = "Proceedings of the Eleventh International Conference on Computational Structures Technology (CST2012)", year = "2012", editor = "B. H. V. Topping", pages = "Paper 216", address = "Dubrovnik, Croatia", publisher_address = "Stirlingshire, UK", month = "4-7 " # sep, publisher = "Civil-Comp Press", keywords = "genetic algorithms, genetic programming, industrial optimisation, metamodel, polynomial chaos expansion, sensitivity analysis, particle swarm optimisation, dimensionality reduction", URL = "http://webapp.tudelft.nl/proceedings/cst2012/html/summary/armani.htm", URL = "http://webapp.tudelft.nl/proceedings/cst2012/pdf/armani.pdf", DOI = "doi:10.4203/ccp.99.216", size = "16 pages", abstract = "The application of uncertainty management techniques to the aircraft design process is currently a high profile research area and of key strategic interest within aerospace industry. Within the aircraft design process there is always a difficult balance between non specific and specific design steps for configuration and design maturity versus the overall project lead time. This leads to either an immature design that causes delays of the entry into service or significant re-design loops within the aircraft development project again resulting in a significant cost penalty. The ability to quantify uncertainties in the design enables the application of more robust optimisation approaches to balance the quantitative risks of design evolution against the aircraft performance implications (e.g. aircraft weight) and specific design lead time. Although the application of stochastic analysis is a powerful way of making informed design decisions, its integration into the standard design process requires the generation of deterministic design data which achieve the design targets from an uncertainty approach. In this paper the problem of retrieving deterministic design data from a collection of responses provided by aircraft structural computer models is addressed. Firstly, a framework that enables metamodel generation and dimensionality reduction is presented. The framework relies on polynomial chaos expansion (PCE) for metamodel generation [1]. The technique was chosen for its ability to ease the sensitivity analysis process, as sensitivity information in the form of Sobol indices can be extracted analytically from the PCE metamodels. Secondly, a search algorithm that can be used to explore the metamodels generated by PCE is presented. The algorithm, based on the particle swarm optimisation (PSO) paradigm [2], was developed specifically to be used in constrained search problems: it performs a search of the design configurations that produces a specified target response level. Constraints can also be defined using additional metamodels. The framework and the search algorithm have been validated on an aircraft structural analysis problem. The accuracy of the results and the reduced computational cost of the entire process make the presented methodology a valuable tool for uncertainty and sensitivity analysis in the aerospace industry.", notes = "PSO rather than GP? http://webapp.tudelft.nl/proceedings/cst2012/html/home.htm", } @PhdThesis{Armani_PhD_thesis, author = "Umberto Armani", title = "Development of a hybrid genetic programming technique for computationally expensive optimisation problems", school = "School of Civil Engineering, University of Leeds", year = "2014", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/7281/1/Armani_PhD_thesis_resubmission_grerrors_corrected.pdf", URL = "http://etheses.whiterose.ac.uk/7281/", URL = "http://ethos.bl.uk/OrderDetails.do?did=50&uin=uk.bl.ethos.631392", size = "406 pages", abstract = "The increasing computational power of modern computers has contributed to the advance of nature-inspired algorithms in the fields of optimisation and metamodelling. Genetic programming (GP) is a genetically-inspired technique that can be used for meta modelling purposes. GP main strength is in the ability to infer the mathematical structure of the best model fitting a given data set, relying exclusively on input data and on a set of mathematical functions given by the user. Model inference is based on an iterative or evolutionary process, which returns the model as a symbolic expression (text expression). As a result, model evaluation is inexpensive and the generated expressions can be easily deployed to other users. Despite genetic programming has been used in many different branches of engineering, its diffusion on industrial scale is still limited. The aims of this thesis are to investigate the intrinsic limitations of genetic programming, to provide a comprehensive review of how researchers have tackled genetic programming main weaknesses and to improve genetic programming ability to extract accurate models from data. In particular, research has followed three main directions. The first has been the development of regularisation techniques to improve the generalisation ability of a model of a given mathematical structure, based on the use of a specific tuning algorithm in case sinusoidal functions are among the functions the model is composed of. The second has been the analysis of the influence that prior knowledge regarding the function to approximate may have on genetic programming inference process. The study has led to the introduction of a strategy that allows to use prior knowledge to improve model accuracy. Thirdly, the mathematical structure of the models returned by genetic programming has been systematically analysed and has led to the conclusion that the linear combination is the structure that is mostly returned by genetic programming runs. A strategy has been formulated to reduce the evolutionary advantage of linear combinations and to protect more complex classes of individuals throughout the evolution. The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/~cnua/mypage.html.", notes = "HyGP C.4 Hock function C.5 Branin-Hoo function C.6 Rosenbrock function (PCE comparison) C.7 Kotanchek function (PCE comparison) C.8 10-bar truss optimisation C.9 Hospital ward ventilation optimisation C.10 Chromate diffusion model C.11 Jet pump model C.12 Bread baking oven design optimisation C.13 Aerodynamic optimisation of NASA rotor 37 compressor rotor blade uk.bl.ethos.631392", } @InProceedings{arnaldo:2014:EuroGP, author = "Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly", title = "Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "13--24", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, GPU", isbn13 = "978-3-662-44302-6", DOI = "DOI:10.1007/978-3-662-44303-3_2", abstract = "The Flash system runs ensemble-based Genetic Programming (GP) symbolic regression on a shared memory desktop. To significantly reduce the high time cost of the extensive model predictions required by symbolic regression, its fitness evaluations are tasked to the desktop's GPU. Successive GP {"}instances{"} are run on different data subsets and randomly chosen objective functions. Best models are collected after a fixed number of generations and then fused with an adaptive, output-space method. New instance launches are halted once learning is complete. We demonstrate that Flash's ensemble strategy not only makes GP more robust, but it also provides an informed online means of halting the learning process. Flash enables GP to learn from a dataset composed of 370K exemplars and 90 features, evolving a population of 1000 individuals over 100 generations in as few as 50 seconds.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @InProceedings{Arnaldo:2014:GECCO, author = "Ignacio Arnaldo and Krzysztof Krawiec and Una-May O'Reilly", title = "Multiple regression genetic programming", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "879--886", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, MRGP, Multiple Regression", URL = "http://doi.acm.org/10.1145/2576768.2598291", DOI = "doi:10.1145/2576768.2598291", code_url = "https://flexgp.github.io/gp-learners/mrgp.html", size = "8 pages", abstract = "We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fitness cases, that we assess for fitness, rather than the program's execution output. MRGP can be used to improve the fitness of a final evolved solution. On our experimental suite, MRGP consistently generated solutions fitter than the result of competent GP or multiple regression. When integrated into GP, inline MRGP, on the basis of equivalent computational budget, outperforms competent GP while also besting post-run MRGP. Thus MRGP's output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP.", notes = "Also known as \cite{2598291} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Arnaldo:2015:GECCO, author = "Ignacio Arnaldo and Una-May O'Reilly and Kalyan Veeramachaneni", title = "Building Predictive Models via Feature Synthesis", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "983--990", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754693", DOI = "doi:10.1145/2739480.2754693", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We introduce Evolutionary Feature Synthesis (EFS), a regression method that generates readable, nonlinear models of small to medium size datasets in seconds. EFS is, to the best of our knowledge, the fastest regression tool based on evolutionary computation reported to date. The feature search involved in the proposed method is composed of two main steps: feature composition and feature subset selection. EFS adopts a bottom-up feature composition strategy that eliminates the need for a symbolic representation of the features and exploits the variable selection process involved in pathwise regularized linear regression to perform the feature subset selection step. The result is a regression method that is competitive against neural networks, and outperforms both linear methods and Multiple Regression Genetic Programming, up to now the best regression tool based on evolutionary computation.", notes = "Also known as \cite{2754693} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Proceedings{Arnold:2014:GECCOcomp, title = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Dirk Arnold and Mengjie Zhang and Ryan Urbanowicz and Muhammad Iqbal and Kamran Shafi and Forrest Stonedahl and William Rand and Tea Tusar and Boris Naujoks and David Walker and Richard Everson and Jonathan Fieldsend and Stefan Wagner and Michael Affenzeller and Zhun Fan and Yaochu Jin and Hod Lipson and Erik Goodman and Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman and Kent McClymont and Kevin Sim and Gabriela Ochoa and Ed Keedwell and Anna I Esparcia-Alcazar and Frank W. Moore and Jaume Bacardit and Ignacio Arnaldo and Kalyan Veeramachaneni and Una-May O'Reilly and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Steven Gustafson and Ekaterina Vladislavleva and John Woodward and Jerry Swan and Earl Barr and Krzysztof Krawiec and Chris Simons and John Clark and Dirk Sudholt and Anna Esparcia and Aniko Ekart and Carola Doerr and Anne Auger", address = "Vancouver, BC, Canada", publisher_address = "New York, NY, USA", month = "12-16 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Keynotes and invited talk, ant colony optimization and swarm intelligence, artificial immune systems, artificial life, robotics, and evolvable hardware, biological and biomedical applications, digital entertainment technologies and arts, estimation of distribution algorithms, evolution strategies and evolutionary programming, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, generative and developmental systems, integrative genetic and evolutionary computation, parallel evolutionary systems, real world applications, search based software engineering, self-* search, theory, Introductory tutorials, Advanced tutorials, Specialized tutorials, 17th annual international workshop on learning classifier systems, Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS), student workshop, VizGEC: Workshop on visualisation in genetic and evolutionary computation, Workshop on Evolutionary Computation Software Systems (EvoSoft), evolutionary synthesis of dynamical systems, Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC), Workshop on Problem Understanding and Real-world Optimisation (PURO), Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef), Workshop on Evolutionary Computation for Big Data and Big Learning, Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), Workshop on Symbolic Regression and Modelling, 4th workshop on evolutionary computation for the automated design of algorithms, Workshop on Metaheuristic Design Patterns (MetaDeeP), Late breaking abstracts workshop, Women@GECCO 2014", isbn13 = "978-1-4503-2881-4", URL = "http://dl.acm.org/citation.cfm?id=2598394", DOI = "doi:10.1145/2598394", abstract = "It is my pleasure to welcome you to Philadelphia for the 2012 Genetic and Evolutionary Computation Conference (GECCO-2012). This is the first time GECCO has been held in Philly. We very much you hope you enjoy this historic American city and all it has to offer. This will be my 14th year attending GECCO. I have contributed a number of papers and have enjoyed many thought-provoking presentations over the years. GECCO has played a very important role in my research program and in the training of many of my students and postdocs. I agreed to serve as General Chair of GECCO-2012 because it was time to give back to the community I have enjoyed being a part of since 1999. Terence Soule served as the editor-in-chief this year and did a very skillful job maintaining the high quality of the conference. GECCO-2012 accepted 172 full papers for oral presentation out of a total of 467 submitted. This is an acceptance rate of less than 37percent. I am very thankful to Terry, Anne Auger, our Proceedings Chair, and all the track chairs for their hard work managing the review, selection and scheduling process for the scientific papers. One of the highlights of every GECCO is the free tutorials and the free workshops held during the first two days of the conference. I found these to be incredibly helpful when I was still learning about the field.", notes = "Distributed at GECCO-2014.", } @Article{Arora:2012:IJCA, title = "Optimization of Decision Rules in Fuzzy Classification", author = "Renuka Arora and Sudesh Kumar", journal = "International Journal of Computer Applications", year = "2012", volume = "51", number = "3", pages = "13--17", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "09758887", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:2c58ebd9b3af7344384924c400738db2", URL = "http://research.ijcaonline.org/volume51/number3/pxc3880505.pdf", DOI = "doi:10.5120/8021-0505", publisher = "Foundation of Computer Science (FCS)", size = "5 pages", abstract = "There are various advances in data collection that can intelligently and automatically analyse and mine knowledge from large amounts of data. World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. The Knowledge Discovery in Databases (KDD) is the process of extracting the knowledge from huge data collection. Data mining is a step of KDD in which patterns or models are extracted from data by using some automated techniques. Discovering knowledge in the form of classification rules is one of the most important tasks of data mining. Discovery of comprehensible, concise and effective rules helps us to make right decisions. Therefore, several Machine Learning techniques are applied for discovery of classification rules. Recently there have been several applications of genetic algorithms for effective rules with high predictive accuracy.", notes = "Balloon, Poker", } @InProceedings{Arora:2010:NAACL, author = "Shilpa Arora and Elijah Mayfield and Carolyn Penstein-Rose and Eric Nyberg", title = "Sentiment Classification Using Automatically Extracted Subgraph Features", booktitle = "Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text", series = "CAAGET '10", year = "2010", address = "Los Angeles, California", pages = "131--139", month = jun, keywords = "genetic algorithms, genetic programming, GP", URL = "http://dl.acm.org/citation.cfm?id=1860631.1860647", acmid = "1860647", oai = "oai:CiteSeerX.psu:10.1.1.207.7440", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.7440", URL = "http://www.cs.cmu.edu/%7Eemayfiel/AroraMayfieldRoseNybergNAACL2010.pdf", publisher = "Association for Computational Linguistics", publisher_address = "Stroudsburg, PA, USA", size = "9 pages", abstract = "In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.", } @InProceedings{Arpaia:2009:I2MTC, author = "Pasquale Arpaia and Fabrizio Clemente and Carlo Manna and Giuseppe Montenero", title = "Automatic modeling based on cultural programming for osseointegration diagnosis", booktitle = "IEEE Instrumentation and Measurement Technology Conference, I2MTC '09", year = "2009", pages = "1274--1277", address = "Singapore", month = "5-7 " # may, keywords = "genetic algorithms, genetic programming, gene expression programming, EIS data, artificial intelligence, automatic modeling, bone implant, cultural programming, electrical impedance spectroscopy, evolutionary programming approach, metallic implant, osseointegration diagnosis, prosthesis, artificial intelligence, biomedical measurement, bone, electric impedance measurement, equivalent circuits, evolutionary computation, genetics, medical computing, orthopaedics, prosthetics", isbn13 = "978-1-4244-3352-0", ISSN = "1091-5281", DOI = "doi:10.1109/IMTC.2009.5168651", size = "4 pages", abstract = "The problem of modelling equivalent circuits for interpreting Electrical Impedance Spectroscopy (EIS) data in monitoring osseointegration level of metallic implants in bone is faced by means of an evolutionary programming approach based on cultural algorithms. With respect to state-of-the-art gene expression programming, the information on search advance acquired by most promising individuals during the evolution is shared with the entire population of potential solutions and stored also for next generations. Experimental results of the application such cultural programming-based analytical modelling to in-vitro EIS measurements of bone in-growth around metallic implants during prosthesis osseointegration are presented.", notes = "Also known as \cite{5168651}", } @Article{Arroba:2015:grid, title = "Enhancing regression models for complex systems using evolutionary techniques for feature engineering", author = "Patricia Arroba and Jose Luis Risco-Martin and Marina Zapater and Jose Manuel Moya and Jose Luis Ayala", journal = "Journal of Grid Computing", year = "2015", volume = "13", number = "3", pages = "409--423", publisher = "Springer", month = sep # "~27", keywords = "genetic algorithms, genetic programming", URL = "http://eprints.ucm.es/30960/", URL = "http://eprints.ucm.es/30960/1/JGridComputing2014.pdf", URL = "http://link.springer.com/article/10.1007%2Fs10723-014-9313-8", ISSN = "1572-9184", DOI = "doi:10.1007/s10723-014-9313-8", abstract = "This work proposes an automatic methodology for modelling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimisation policies, but accurate and fast power modelling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimises error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98percent. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.", bibsource = "OAI-PMH server at eprints.ucm.es", language = "en", oai = "oai:www.ucm.es:30960", relation = "10.1007/s10723-014-9313-8; TIN2008-00508", rights = "info:eu-repo/semantics/openAccess", type = "PeerReviewed", } @Article{Arsalan:2017:ASC, author = "Muhammad Arsalan and Aqsa Saeed Qureshi and Asifullah Khan and Muttukrishnan Rajarajan", title = "Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique", journal = "Applied Soft Computing", volume = "51", pages = "168--179", year = "2017", month = feb, keywords = "genetic algorithms, genetic programming, Health care, Integer Wavelet Transform, Reversible watermarking, Medical images", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.11.044", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616306135", abstract = "This work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique IRW-Med, the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.", } @InProceedings{conf/cibb/ArseneAB13, author = "Corneliu T. C. Arsene and Denisa Ardevan and Paul Bulzu", title = "Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expression Analysis and Other Statistical Methods", publisher = "Springer", year = "2013", volume = "8452", keywords = "genetic algorithms, genetic programming", bibdate = "2014-07-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cibb/cibb2013.html#ArseneAB13", booktitle = "CIBB", editor = "Enrico Formenti and Roberto Tagliaferri and Ernst Wit", isbn13 = "978-3-319-09041-2", pages = "161--177", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-319-09042-9", } @InProceedings{Arshad:2014:FIT, author = "Junaid Arshad and Aneela Zameer and Asifullah Khan", booktitle = "12th International Conference on Frontiers of Information Technology (FIT)", title = "Wind Power Prediction Using Genetic Programming Based Ensemble of Artificial Neural Networks ({GPeANN})", year = "2014", pages = "257--262", abstract = "Over the past couple of years, the share of wind power in electrical power system has increased considerably. Because of the irregular characteristics of wind, the power generated by the wind turbines fluctuates continuously. The unstable nature of the wind power thus poses a serious challenge in power distribution systems. For reliable power distribution, wind power prediction system has become an essential component in power distribution systems. In this Paper, a wind power forecasting strategy composed of Artificial Neural Networks (ANN) and Genetic Programming (GP) is proposed. Five neural networks each having different structure and different learning algorithm were used as base regressors. Then the prediction of these neural networks along with the original data is used as input for GP based ensemble predictor. The proposed wind power forecasting strategy is applied to the data from five wind farms located in same region of Europe. Numerical results and comparison with existing wind power forecasting strategies demonstrates the efficiency of the proposed strategy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/FIT.2014.55", month = dec, notes = "Also known as \cite{7118409}", } @InProceedings{Arshad:2014:IJCNN, author = "R. Arshad and G. M. Khan and S. A. Mahmud", booktitle = "International Joint Conference on Neural Networks (IJCNN 2014)", title = "Smart bandwidth management using a recurrent Neuro-Evolutionary technique", year = "2014", month = jul, pages = "2240--2247", abstract = "The requirement for correct bandwidth allocation and management in a multitude of different communication mediums has generated some exceedingly tedious challenges that need to be addressed both intelligently and with innovative solutions. Current advances in high speed broadband technologies have manifold increased the amount of bandwidth required during successful multimedia streaming. The progressive growth of Neuro-Evolutionary techniques have presented themselves as worthy options to address many of the challenges faced during multimedia streaming. In this paper a Neuro-Evolutionary technique called the Recurrent Cartesian Genetic Programming Evolved Artificial Neural Network(RCGPANN) is presented for prediction of future frame sizes. The proposed technique takes into account the traffic size trend of the historically transmitted data for future frame size prediction. The predicted frame size forms the basis for estimation of the amount of bandwidth necessary for transmission of future frame. Different linear regression and probabilistic approaches are employed to estimate the allocated bandwidth, while using the predicted frame size. Our proposed intelligent traffic size prediction along with bandwidth estimation and management results in a 98percent increased efficiency.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/IJCNN.2014.6889727", notes = "Also known as \cite{6889727}", } @InProceedings{Arslan:2017:BIYOMUT, author = "Sibel Arslan and Celal Ozturk", booktitle = "2017 21st National Biomedical Engineering Meeting (BIYOMUT)", title = "Feature Selected Cancer Data Classification with Genetic Programming", year = "2017", pages = "i--iv", abstract = "Classification is used to distribute data to classes defined on the dataset. Classification algorithms determine the classes in which the data in the test set is to be included by learning the distribution of classes in the training set. It is directly dependent on the choice of which properties to use in the classification. The most prominent features of cancer data in this work are selection and classification using genetic programming method. It has been seen that very successful classification results are obtained with Genetic Programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BIYOMUT.2017.8478885", month = nov, notes = "Also known as \cite{8478885}", } @Article{ARSLAN:2019:ASC, author = "Sibel Arslan and Celal Ozturk", title = "Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection", journal = "Applied Soft Computing", volume = "78", pages = "515--527", year = "2019", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.03.014", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619301322", keywords = "genetic algorithms, genetic programming, Feature selection, Artificial bee colony programming, Multi hive artificial bee colony programming, High dimension data", abstract = "Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods", } @Article{arslan:2019:AS, author = "Sibel Arslan and Celal Ozturk", title = "Artificial Bee Colony Programming Descriptor for {Multi-Class} Texture Classification", journal = "Applied Sciences", year = "2019", volume = "9", number = "9", note = "Special Issue Machine Learning and Compressed Sensing in Image Reconstruction", keywords = "genetic algorithms, genetic programming, Texture classification, artificial bee colony programming-descriptor, image descriptor, local binary pattern, genetic programming-descriptor", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/9/9/1930", URL = "https://www.mdpi.com/2076-3417/9/9/1930.pdf", DOI = "doi:10.3390/app9091930", size = "18 page", abstract = "Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behaviour of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.", notes = "also known as \cite{app9091930}", } @InProceedings{Arslan:2022:ASYU, author = "Sibel Arslan and Nursah Kutuk", booktitle = "2022 Innovations in Intelligent Systems and Applications Conference (ASYU)", title = "Titan Yellow Biosorption of Hemp Waste in Acidic Medium and Modeling of Experimental Conditions by Multi Gene Genetic Programming", year = "2022", abstract = "In recent years, pollutants such as dyes, drugs and heavy metals in wastewater have caused serious environmental pollution. In this study, biosorption of titan yellow (TY) using hemp waste was studied. In the biosorption of TY dye to hemp waste, 88percent biosorption was achieved with an initial dye concentration of 10 mg/L and a biosorbent ratio of 2 g/L in acidic medium. When Langmuir and Freundlich isotherms were examined, R-squared values were obtained as 0.92 and 0.95, respectively. Its maximum biosorption capacity has been calculated as 51.8 mg/g. It has also been observed that the biosorption process adapts to the pseudo second order reaction R-squared = 0.99) kinetics. We have also developed more accurate and reliable correlation models using Multi-Gene Genetic Programming (MGGP), a powerful method based on evolutionary computation. The performance of the developed models was examined using three statistical criteria. A comparison of the criteria reveals that MGGP is effective in simulating the biosorption process in the real world.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ASYU56188.2022.9925394", ISSN = "2770-7946", month = sep, notes = "Also known as \cite{9925394}", } @Article{ARSLAN:2024:asoc, author = "Sibel Arslan", title = "Immune Plasma Programming: A new evolutionary computation-based automatic programming method", journal = "Applied Soft Computing", volume = "152", pages = "111204", year = "2024", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.111204", URL = "https://www.sciencedirect.com/science/article/pii/S156849462301222X", keywords = "genetic algorithms, genetic programming, Automatic programming, Immune plasma programming, Immune plasma algorithm, Symbolic regression", abstract = "Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation-based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems", } @Article{ARSLAN:2023:asoc, author = "Sibel Arslan and Celal Ozturk", title = "A comprehensive review of automatic programming methods", journal = "Applied Soft Computing", volume = "143", pages = "110427", year = "2023", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2023.110427", URL = "https://www.sciencedirect.com/science/article/pii/S1568494623004453", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Evolutionary computation, Automatic programming", abstract = "Automatic programming (AP) is one of the most attractive branches of artificial intelligence because it provides effective solutions to problems with limited knowledge in many different application areas. AP methods can be used to determine the effects of a system's inputs on its outputs. Although there is increasing interest in solving many problems using these methods for a variety of applications, there is a lack of reviews that address the methods. Therefore, the goal of this paper is to provide a comprehensive literature review of AP methods. At the same time, we mention the main characteristics of the methods by grouping them according to how they represent solutions. We also try to give an outlook on the future of the field by highlighting possible bottlenecks and perspectives for the benefit of the researchers involved", } @Article{ARSLAN:2023:eswa, author = "Sibel Arslan and Nursah Kutuk", title = "Symbolic regression with feature selection of dye biosorption from an aqueous solution using pumpkin seed husk using evolutionary computation-based automatic programming methods", journal = "Expert Systems with Applications", volume = "231", pages = "120676", year = "2023", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2023.120676", URL = "https://www.sciencedirect.com/science/article/pii/S0957417423011788", keywords = "genetic algorithms, genetic programming, Pumpkin seed husk, Biosorption, Titan yellow, System modeling, Artificial bee colony programming", abstract = "Industrial waste pollution is a serious and systematic problem that harms the environment and people. The development of cheap, simple, and efficient techniques to solve this problem is important for sustainability. In this study, both experimental and evolutionary computation (EC)-based automatic programming (AP) methods were used to investigate the biosorption process for water treatment. In the experiments, titan yellow (TY), an anionic dye, was biosorbed from an aqueous solution containing pumpkin seed husk (PSH). The structure of PSH was examined using a Fourier transform infrared spectroscopy (FTIR) and a scanning electron microscope (SEM). The result of the experimental studies was that TY biosorption of PSH reached a biosorption efficiency of 95percent after 120 min of contact time. The maximum biosorption capacity (qmax) was calculated to be 181.8 mg/g. It was found that the biosorption of TY better followed the Dubinin-Radushkevich isotherm (R2=0.98) and pseudo second-order reaction kinetics (R2=0.99). Based on the thermodynamic data, the biosorption process was exothermic and spontaneous. After the experiments, the process was modeled using pH, biosorbent concentration, initial dye concentration, contact time, and temperature as inputs and biosorption efficiency (percent) as output for the methods. Moreover, the success of these AP methods was compared with a newly proposed evolutionary method. The simulation results indicate that AP methods generate best models (Rtrain2=0.99 and Rtest2=0.97). At the same time, the most important parameter of the process in the feature analysis is contact time. This study shows that EC-based AP methods can effectively model applications such as the biosorption process", } @Article{ARSLAN:2023:engappai, author = "Sibel Arslan and Kemal Koca", title = "Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade", journal = "Engineering Applications of Artificial Intelligence", volume = "123", pages = "106210", year = "2023", ISSN = "0952-1976", DOI = "doi:10.1016/j.engappai.2023.106210", URL = "https://www.sciencedirect.com/science/article/pii/S0952197623003949", keywords = "genetic algorithms, genetic programming, Automatic programming, Artificial bee colony programming, Aerodynamic coefficients, Power efficiency, Wind turbine blade", abstract = "Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are used for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time", } @Article{arslan:2005:GPEM, author = "Tughrul Arslan", title = "Book Review: Evolvable Components--From Theory to Hardware Implementations", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "4", pages = "461--462", month = dec, keywords = "genetic algorithms, evolvable hardware", ISSN = "1389-2576", URL = "https://rdcu.be/dR8dB", DOI = "doi:10.1007/s10710-005-3718-x", size = "2 pages", abstract = "Book Review: Evolvable Components--From Theory to Hardware Implementations by Lukas Sekanina Springer, 2003, ISBN 3-540-40377-9", notes = "review of \cite{sekanina:2003:book}", } @InProceedings{Arvaneh:2009:ICBPE, author = "M. Arvaneh and H. Ahmadi and A. Azemi and M. Shajiee and Z. S. Dastgheib", title = "Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the {PR} interval of {ECG}", booktitle = "International Conference on Biomedical and Pharmaceutical Engineering, ICBPE '09", year = "2009", month = "2-4 " # dec, pages = "1--5", keywords = "genetic algorithms, genetic programming, ECG signal, PR interval, Paroxysmal Atrial Fibrillation, electrocardiography, neural networks, ANN, electrocardiography, neural nets", DOI = "doi:10.1109/ICBPE.2009.5384063", size = "5 pages", abstract = "In this work, we propose a new method for prediction of Paroxysmal Atrial Fibrillation (PAF) by only using the PR interval of ECG signal. We first obtain a nonlinear structure and parameters of PR interval by a Genetic Programming (GP) based algorithm. Next, we use the neural networks for prediction of PAF. The inputs of the neural networks are the parameters of nonlinear model of the PR intervals. For the modeling and prediction we have limited ourselves to only 30 seconds of an ECG signal, which is one of the advantages of our proposed approach. For comparison purposes, we have modeled 30 seconds of ECG signals by time based modeling method and have compared prediction results of them.", notes = "Also known as \cite{5384063}", } @Article{aryafar:2019:EES, author = "Ahmad Aryafar and Vahid Khosravi and Hosniyeh Zarepourfard and Reza Rooki", title = "Evolving genetic programming and other {AI-based} models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran", journal = "Environmental Earth Sciences", year = "2019", volume = "78", number = "3", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s12665-019-8092-8", DOI = "doi:10.1007/s12665-019-8092-8", } @Article{Asadi:2010:ASC, author = "Mojtaba Asadi and Mehdi Eftekhari and Mohammad Hossein Bagheripour", title = "Evaluating the strength of intact rocks through genetic programming", journal = "Applied Soft Computing", year = "2011", volume = "11", number = "2", pages = "1932--1937", month = mar, keywords = "genetic algorithms, genetic programming, Information criterion, Intact rock, Failure criteria", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/B6W86-50CVPW4-2/2/863c13a5a1c7be6da7b1ea6592b11bd3", DOI = "doi:10.1016/j.asoc.2010.06.009", size = "13 pages", abstract = "Good prediction of the strength of rocks has many theoretical and practical applications. Analysis, design and construction of underground openings and tunnels, open pit mines and rock-based foundations are some examples of applications in which prediction of the strength of rocks is of great importance. The prediction might be done using mathematical expressions called failure criteria. In most cases, failure criteria of jointed rocks contain the value of strength of intact rock, i.e. the rock without joints and cracks. Therefore, the strength of intact rock can be used directly in applications and indirectly to predict the strength of jointed rock masses. On the other part, genetic programming method is one of the most powerful methods in machine learning field and could be used for non-linear regression problems. The derivation of an appropriate equation for evaluating the strength of intact rock is the common objective of many researchers in civil and mining engineering; therefore, mathematical expressions were derived in this paper to predict the strength of the rock using a genetic programming approach. The data of 51 rock types were used and the efficiency of equations obtained was illustrated graphically through figures.", notes = "a Sirjan engineering college, Department of Civil Engineering, Iran b Shahid Bahonar University of Kerman, Department of Computer Engineering, Iran c Shahid Bahonar University of Kerman, Department of Civil Engineering, Iran", } @Article{AsadiTashvigh:2015:Calphad, author = "Akbar Asadi Tashvigh and Farzin Zokaee Ashtiani and Mohammad Karimi and Ahmad Okhovat", title = "A novel approach for estimation of solvent activity in polymer solutions using genetic programming", journal = "Calphad", volume = "51", pages = "35--41", year = "2015", ISSN = "0364-5916", DOI = "doi:10.1016/j.calphad.2015.07.005", URL = "http://www.sciencedirect.com/science/article/pii/S0364591615300080", abstract = "In this paper, genetic programming (GP) as a novel approach for the explicit modelling the phase equilibria of polymer solutions is presented. The objective of this study is to develop robust model based on experimental data for prediction of solvent activity in polymer/solvent mixtures. Molecular weight, density, chemical structures of polymer and solvent, and concentration of polymer solution were considered as input parameters of the model. Activity of solvent is considered as output parameter of the model. Some statistical parameters were calculated in order to investigate the reliability of model. The results showed very well agreement with the experimental data with an average error of less than 3percent.", keywords = "genetic algorithms, genetic programming, Solvent activity, Polymer solution, Phase equilibria", } @Article{ASADZADEH:2021:AES, author = "Mohammad Zhian Asadzadeh and Hans-Peter Ganser and Manfred Mucke", title = "Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process", journal = "Applications in Engineering Science", volume = "6", pages = "100049", year = "2021", ISSN = "2666-4968", DOI = "doi:10.1016/j.apples.2021.100049", URL = "https://www.sciencedirect.com/science/article/pii/S2666496821000157", keywords = "genetic algorithms, genetic programming, Hybrid modelling, Symbolic regression, Knowledge discovery, Metal sheet bending", abstract = "Hybrid semiparametric models integrate physics-based ({"}white-box{"}, parametric) and data-driven ({"}black-box{"}, non-parametric) submodels. Black-box models are often implemented using artificial neural networks (ANNs). In this work, we investigate the fitness of symbolic regression (SR) for black-box modelling. The main advantage of this approach is that a trained hybrid model can be expressed in closed form as an algebraic equation. We examine and test the idea on a simple example, namely the v-shape bending of a metal sheet, where an analytical solution for the stamping force is readily available. We explore unconstrained and hybrid symbolic regression modelling to show that hybrid SR models, where the regression tree is partly fixed according to a-priori knowledge, perform much better than purely data-driven SR models based on unconstrained regression trees. Furthermore, the generation of algebraic equations by this method is much more repeatable, which makes the approach applicable to process knowledge discovery", } @InCollection{ashcraft:2003:NEBCEGA, author = "Kenneth Ashcraft", title = "Nark: Evolving Bug-Finding Compiler Extensions with Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2003", year = "2003", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = "4 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2003/Ashcraft.pdf", notes = "part of \cite{koza:2003:gagp}", } @Article{Ashiru:1998:MM, author = "I. Ashiru and C. A. Czarnecki", title = "Evolving communicating controllers for multiple mobile robot systems", journal = "Microprocessors and Microsystems", year = "1998", volume = "21", pages = "393--402", number = "6", keywords = "genetic algorithms, genetic programming, Mobile robots, Communication", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V0X-3TB0788-6/2/445577f1e7cd0c0d531457835edf327e", ISSN = "0141-9331", DOI = "doi:10.1016/S0141-9331(98)00054-4", abstract = "Multiple mobile robot systems working together to achieve a task have many advantages over single robot systems. However, the planning and execution of a task which is to be undertaken by multiple robots is extremely difficult. To date no tools exist which allow such systems to be engineered. One of the key questions that arises when developing such systems is: does communication between the robots aid the completion of the task, and if so what information should be communicated? This paper presents the results of an investigation undertaken to address the above question. The approach adopted is to use genetic programming (GP) with the aim of evolving a controller, and letting the evolution process determine what information should be communicated and how best to use this information. A number of experiments were performed with the aim of determining the communication requirements. The results of these experiments are presented in this paper. It is shown that the GP system evolved controllers whose performance benefitted as a result of the communication process.", } @InProceedings{ashlock:1997:GPdd, author = "Dan Ashlock", title = "GP-Automata for Dividing the Dollar", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "18--26", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/ashlock_1997_GPdd.pdf", size = "9 pages", notes = "GP-97", } @InProceedings{ashlock:1997:spbs, author = "Dan Ashlock and Charles Richter", title = "The Effect of Splitting Populations on Bidding Strategies", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "27--34", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://dakotarichter.com/papers/AshlockRichterSplittingPopulationsGP97.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/ashlock_1997_spbs.pdf", size = "8 pages", abstract = "In this paper we explore the effects of splitting a single population of artificial agents engaging in a simple double auction game into two competing populations by modifying experiments reported in [Ashlock, 1997]. The original paper used a new genetic programming tool, termed GP-Automata, to induce bidding strategies with a genetic algorithm for Nash's game divide the dollar. The motivation for performing the research is the biological notion of inclusive fitness and kinship theory. The a priori hypothesis of the authors was that behaviour of the agents in the simulated market would change substantially when they were no longer forced to be similar to one another by the genetic mechanism used to induce new bidding strategies. While breeding takes place only within each population, all bidding is between agents from different populations. The agents in the original (single population) paper strongly favoured {"}fair{"} Nash equilibria of the divide the dollar game, at odds with the economic theory for egoistic agents. When controls for kinship effects are implemented by splitting the population a substantial effect is observed. When agents doing the bidding are not close genetic kin to one another the 'unfair' Nash equilbria regain a great deal of their former prominence. This result is of importance to any sort of evolutionary algorithm creating artificial agents, as kinship theory can confound game-theoretic predictions that assume egoistic agents. The current research also arguably increases the level of realism in the simulation of a double auction market.", notes = "GP-97", } @InProceedings{ashlock:1998:fctsGP, author = "Dan Ashlock and James I. Lathrop", title = "A Fully Characterized Test Suite for Genetic Programming", booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming", year = "1998", editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben", volume = "1447", series = "LNCS", pages = "537--546", address = "Mission Valley Marriott, San Diego, California, USA", publisher_address = "Berlin", month = "25-27 " # mar, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64891-7", URL = "https://rdcu.be/cTHTU", URL = "https://link.springer.com/chapter/10.1007/BFb0040805", DOI = "doi:10.1007/BFb0040753", size = "10 pages", abstract = "We present a family of related test problems for genetic programming. These test problems form a very simple test environment that nevertheless possesses some degree of algorithmic subtlety. We term this genetic programming environment plus-one-recall-store (PORS). This genetic programming environment has only a pair of terminals, 1 and recall, and a pair of operations, plus and store, together with a single memory location. We present an extensive mathematical characterization of the PORS environment and report experiments testing the benefits of incorporating expert knowledge into the initial population and into the operation of crossover. The experiments indicate that, in the test environment, expert knowledge is best incorporated only in the initial population. This is a welcome result as this is the computationally inexpensive choice of the two methods of incorporating expert knowledge tested.", notes = "EP-98. Iowa State University.", } @InProceedings{ashlock:1998:ISAc, author = "Dan Ashlock and Mark Joenks", title = "{ISAc} Lists, A Different Representation for Program Induction", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "3--10", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/ashlock_1998_ISAc.pdf", size = "9 pages", abstract = "The traditional method of using a genetic algorithm to perform program induction is genetic programming which operates upon parse trees. In this papers we introduce a simpler data structure for program induction, the If-Statement-Action ISAc table. We test this data structure on the Tartarus problem of Astro Teller and compare its performance with simple string genes for Tartarus, Teller's own work, and with GP-Automata. In addition to the main result we present a new baseline study for the Tartarus problem. These baseline results suggest state information alone, without reactive ability, can provide relatively high fitness of the Tartarus problem.", notes = "Tartarus GP-98", } @InProceedings{ashlock:2003:taaaogptve, author = "Daniel A. Ashlock and Kenneth M. Bryden", title = "Thermal agents: An application of genetic programming to virtual engineering", booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003", editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon", pages = "1340--1347", year = "2003", publisher = "IEEE Press", address = "Canberra", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "8-12 " # dec, organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", keywords = "genetic algorithms, genetic programming, Boundary conditions, Genetic engineering, Geometry, Impedance, Iterative methods, System testing, Systems engineering and theory, Temperature, Thermal engineering, iterative methods, mechanical engineering computing, temperature distribution, thermal engineering, cellular decomposition, exploratory analysis, iterative method, iterative thermal solver, thermal agents, thermal boundary condition, virtual engineering", ISBN = "0-7803-7804-0", DOI = "doi:10.1109/CEC.2003.1299824", size = "8 pages", abstract = "The temperature profile across an object is easy to compute by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex configuration is an impediment to exploratory analysis of engineering systems. A rapidly computed initial guess can speed convergence for an iterative thermal solver. We describe and test a system for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object geometry but general across different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on one or more sets of boundary conditions. In use, thermal agents transform boundary conditions into a rapidly converged set of initial values on a cellular decomposition of an object.", notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.", } @InProceedings{Ashlock:2004:OToECP, title = "On Taxonomy of Evolutionary Computation Problems", author = "Daniel Ashlock and Kenneth M. Bryden and Steven Corns", pages = "1713--1719", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", volume = "2", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, data visualisation, evolutionary computation, graph theory, pattern classification, pattern clustering, tree data structures, tree searching cladogram, classification technique, evolutionary computation problems, graph based evolutionary algorithms, hierarchical clustering, standard taxonomic technique, taxonomy, Theory of evolutionary algorithms, Combinatorial \& numerical optimization", URL = "https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1072.3780&rep=rep1&type=pdf", DOI = "doi:10.1109/CEC.2004.1331102", size = "7 pages", abstract = "Taxonomy is the practice of classifying members of a group based on their measurable characteristics. In evolutionary computation the problem of telling when two problems are similar is both challenging and important. An accurate classification technique would yield large benefits by permitting a researcher to rationally chose algorithm and parameter setting based on past experience. This study uses a standard taxonomic technique, hierarchical clustering, on a set of taxonomic characters derived from a comparative study using graph based evolutionary algorithms.", notes = "Also known as \cite{1331102}. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Ashlock:2004:CaT, title = "Coevolution and Tartarus", author = "Daniel Ashlock and Stephen Willson and Nicole Leahy", pages = "1618--1624", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Coevolution \& collective behavior, Evolutionary intelligent agents", URL = "http://orion.math.iastate.edu/danwell/eprints/TartarusCE.pdf", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01331089", DOI = "doi:10.1109/CEC.2004.1331089", abstract = "This study applies coevolution to the Tartarus task. If the coevolving test cases are viewed as a form of parasite the question of virulence becomes an important feature of the algorithm. This study compares two types of parasites. The impact of coevolution in this study is at odds with intuition and statistically significant. Analysis suggests that disruptive crossover has a key effect. In the presence of disruptive crossover, coevolution may need to be modified to be effective. The key method of dealing with disruptive crossover is tracking the age of the Tartarus agents. Using only older agents to drive coevolution of test cases substantially enhances the performance of one of the two type of coevolution studied.", notes = "GP-automata. Disruptive effect of crossover and mutation important in co-evolution studies. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{ashlock:2005:CECd, author = "Daniel A. Ashlock and Kenneth M. Bryden and Wendy Ashlock and Stephen P. Gent", title = "Rapid Training of Thermal Agents with Single Parent Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "3", pages = "2122--2129", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554957", size = "8 pages", abstract = "The temperature profile across an object can be computed by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex configuration is an impediment to exploratory analysis of engineering systems. A high-quality rapidly computed initial guess can speed convergence for an iterative algorithm. A system is described and tested for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object but general across different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on several sets of boundary conditions. In use, thermal agents transform boundary conditions into rapidly-converged initial values on a cellular decomposition of an object. the impact of using single parent genetic programming on thermal agents is tested. Single parent genetic programming replaces the usual sub-tree crossover in genetic programming with crossover with members of an unchanging ancestor set. The use of this ancestor set permits the incorporation of expert knowledge into the system as well as permitting the re-use of solutions derived on one object to speed training of thermal agents for another object. For three types of experiments, incorporating expert knowledge; re-using evolved solutions; and transferring knowledge between distinct configurations statistically significant improvements are obtained with single parent techniques.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{Ashlock:2006:CECtax, author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven Corns and Justin Schonfeld", title = "An Updated Taxonomy of Evolutionary Computation Problems using Graph-based Evolutionary Algorithms", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "403--410", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688295", size = "8 pages", abstract = "Graph based evolutionary algorithms use combinatorial graphs to impose a topology or geographic structure on an evolving population. It has been demonstrated that, for a fixed problem, time to solution varies substantially with the choice of graph. This variation is not simple with very different graphs yielding faster solution times for different problems. Normalised time to solution for many graphs thus forms an objective character that can be used for classifying the type of a problem, separate from its hardness measured with average time to solution. This study uses fifteen combinatorial graphs to classify 40 evolutionary computation problems. The resulting classification is done using neighbour joining, and the results are also displayed using non-linear projection. The different methods of grouping evolutionary computation problems into similar types exhibit substantial agreement. Numerical optimisation problems form a close grouping while some other groups of problems scatter across the taxonomy. This paper updates an earlier taxonomy of 23 problems and introduces new classification techniques.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 96--103", } @Book{Ashlock:2006:book, author = "Daniel Ashlock", title = "Evolutionary Computation for Modeling and Optimization", publisher = "Springer", year = "2006", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-22196-0", DOI = "doi:10.1007/0-387-31909-3", size = "approx 571 pages", abstract = "Evolutionary Computation for Optimisation and Modelling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modelling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool. Written for: Undergraduate and graduate students", notes = "GP in Chapter 12 ISAc, Chapter 13 graph based EA, Chapter 14 cellular encoding", size = "572 pages. 2010 available in paper back. ISBN-13: 978-1441919694", } @InProceedings{Ashlock:2006:ANNIE, author = "Daniel Ashlock and Kenneth M. Bryden and Nathan G. Johnson", title = "Evolvable Threaded Controllers for a Multi-Agent Grid Robot Task", booktitle = "ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks", year = "2006", editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy", volume = "16", chapter = "22", address = "St. Louis, MO, USA", month = nov # " 5-8", keywords = "genetic algorithms, genetic programming", isbn13 = "0791802566", DOI = "doi:10.1115/1.802566.paper22", abstract = "If skip action (ISAc) lists are a linear genetic programming data structure that can be used as an evolvable grid robot controller. In this study ISAc lists are modified to run multiple control threads so that a single ISAc list can control multiple grid robots. The threaded ISAc lists are tested by evolving them to control 20--25 grid robots that all must exit a virtual room through a single door. The evolutionary algorithm used rapidly locates a variety of controllers that permit the room to be cleared efficiently.", } @InProceedings{Ashlock:2006:ANNIEa, author = "Daniel Ashlock and Kenneth M. Bryden", title = "Function Stacks, {GBEAs}, and Crossover for the Parity Problem", booktitle = "ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks", year = "2006", editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy", volume = "16", chapter = "18", address = "St. Louis, MO, USA", month = nov # " 5-8", note = "Part I: Evolutionary Computation", keywords = "genetic algorithms, genetic programming", isbn13 = "0791802566", DOI = "doi:10.1115/1.802566.paper18", abstract = "Function stacks are a directed acyclic graph representation for genetic programming that subsumes the need for automatically defined functions, substantially reduces the number of operations required to solve a problem, and permits the use of a conservative crossover operator. Function stacks are a generalisation of Cartesian genetic programming. Graph based evolutionary algorithms are a method for improving evolutionary algorithm performance by imposing a connection topology on an evolutionary population to strike an efficient balance between exploration and exploration. In this study the parity problems using function stacks for parity on 3, 4, 5, and 6 variables are tested on fifteen graphical connection topologies with and without crossover. Choosing the correct graph is found to have a statistically significant impact on time to solution. The conservative crossover operator for function stacks, new in this study, is found to improve time to solution by 4 to 9 fold with more improvement in harder instances of the parity problem.", } @InProceedings{Ashlock:2008:cec, author = "Daniel Ashlock and Taika {von Konigslow}", title = "Evolution of Artificial Ring Species", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "653--659", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0169.pdf", DOI = "doi:10.1109/CEC.2008.4630865", size = "7 pages", abstract = "Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with self-organised and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent. Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with the Tartarus task. A comparison is made with another study that uses hybridisation to achieve record breaking Tartarus fitness.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ashlock3:2008:cec, author = "Daniel Ashlock and Elizabeth Warner", title = "The Geometry of Tartarus Fitness Cases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "1309--1316", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0339.pdf", DOI = "doi:10.1109/CEC.2008.4630965", size = "8 pages", abstract = "Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320320 fitness cases for the standard Tartarus task of which 297040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ashlock5:2008:cec, author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven Corns", title = "Small Population Effects and Hybridization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "2637--2643", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0599.pdf", DOI = "doi:10.1109/CEC.2008.4631152", size = "7 pages", abstract = "This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow. The first is hybridisation; the second is using small population effects. Hybridisation consists of restarting evolutionary algorithms with copies of bestof- population individuals drawn from many populations. Small population effects occur when an evolutionary algorithm's performance, either speed or probability of premature convergence, is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridisation of many small populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used and from varying the frequency with which hybridisation is performed. The major effect results from changing the frequency of hybridization; the impact of population size is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed. Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous hybridization experiments that motivated its development.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Ashlock:2009:ANNIEa, author = "Daniel Ashlock and Adam J. Shuttleworth and Kenneth M. Bryden", title = "Induction of Virtual Sensors with Function Stacks", booktitle = "ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks", year = "2009", editor = "Cihan H. Dagli and K. Mark Bryden and Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer", volume = "19", address = "St. Louis, MO, USA", note = "Part I", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791802953", DOI = "doi:10.1115/1.802953.paper4", abstract = "Virtual sensors are mathematical models that predict the readings of a sensor in a location currently without an operational sensor. Virtual sensors can be used to compensate for a failed sensor or as a framework for supporting mathematical decomposition of a model of a complex system. This study applies a novel genetic programming representation called a function stack to the problem of virtual sensor induction in a simple thermal system. Real-valued function stacks are introduced in this study. The thermal system modelled is a heat exchanger. Function stacks are found to be able to efficiently find compact and accurate models for each often sensors using the data from the other sensors. This study serves as proof-of-concept for using function stacks as a modeling technology for virtual sensors.", } @InProceedings{Ashlock:2009:ANNIE, author = "Daniel Ashlock and Douglas McCorkle and Kenneth M. Bryden", title = "Logic Function Induction with the Blender Algorithm Using Function Stacks", booktitle = "ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks", year = "2009", editor = "Cihan H. Dagli and K. Mark Bryden and Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer", volume = "19", pages = "189--196", chapter = "24", address = "St. Louis, MO, USA", note = "Part III Evolutionary Computation", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791802953", DOI = "doi:10.1115/1.802953.paper24", abstract = "This paper applies two techniques, hybridisation and small population effects, to the problem of logic function induction. It also uses an efficient representation for genetic programming called a function stack. Function stacks are a directed acyclic graph representation used in place of the more common tree-structured representation. This study is the second exploring an algorithm for evolutionary computation called the blender algorithm which performs hybridization of many small populations. The blender algorithm is tested on the 3 and 4 variable parity problems. Confirming and sharpening earlier results on the use of small population sizes for the parity problem, it is demonstrated that subpopulation size and intervals between population mixing steps are critical parameters. The blender algorithm is found to perform well on the parity problem.", } @InProceedings{Ashlock:2010:cec, author = "Daniel Ashlock and Justin Schonfeld", title = "Evolution for automatic assessment of the difficulty of {Sokoban} boards", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586239", size = "8 pages", abstract = "Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the Sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialised population of ISAc lists while the other initialises populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.", notes = "ISAc lists. bulldozer. http://sokoban.dk/science/ WCCI 2010. Also known as \cite{5586239}", } @InProceedings{Ashlock:2015:CEC, author = "Daniel Ashlock and Jeffrey Tsang", title = "Evolving Fractal Art with a Directed Acyclic Graph Genetic Programming Representation", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "2137--2144", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", URL = "http://eldar.mathstat.uoguelph.ca/dashlock/eprints/RFSfrac.pdf", DOI = "doi:10.1109/CEC.2015.7257148", abstract = "A class of fractals called orbit capture fractals are generated by iterating a function on a point until the point's trajectory enters a capture zone. This study uses a digraph based representation for genetic programming to evolve functions used to generate orbit capture fractals. Three variations on the genetic programming system are examined using two fitness functions. The first fitness function maximizes the entropy of the distribution of capture numbers, while the second places a geometric constraint on the distribution of capture numbers. Some combinations of representation and fitness function generate fractals often, while others yield interesting non-fractal images most of the time.", notes = "0950 hrs 15492 CEC2015", } @InProceedings{Ashlock:2016:CEC, author = "Daniel A. Ashlock and Joseph Alexander Brown", title = "Evolutionary Partitioning Regression with Function Stacks", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew Song Ong", pages = "1469--1476", address = "Vancouver", month = "25-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743963", size = "8 pages", abstract = "Partitioning regression is the simultaneous fitting of multiple models to a set of data and partitioning of that data into easily modelled classes. The key to partitioning regression with evolution is minimum error assignment during fitness evaluation. Assigning a point to the model for which it has the least error while using evolution to minimize total model error encourages the evolution of models that cleanly partition data. This study demonstrates the efficacy of partitioning regression with two or three models on simple bivariate data sets. Possible generalizations to the general case of clustering are outlined.", notes = "CEC2016 WCCI2016", } @InProceedings{Ashlock:2016:CECa, author = "Daniel Ashlock and Garrison Greenwood", title = "Generalized Divide the Dollar", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "343--350", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, FSM", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7743814", size = "8 pages", abstract = "Divide the dollar is a two-player simultaneous derived from a game invented by John Nash because its strategy space has an entire subspace of Nash equilibria. This study describes and explores a family of generalizations of divide the dollar with easily controlled properties. If we view divide the dollar as modelling the process of making a bargain, then the generalized game makes it easy to model the impact of external subsidies on bargaining. Classical divide the dollar is compared to four generalizations representing a simple subsidy in three different amounts and a more complex type of subsidy. The distribution of simple strategies that arise under replicator dynamics is compared to the bids that arise in populations of evolving, adaptive agents. Agents are encoded using a finite state representation that conditions its transitions on the result of bargains. These results fall into three categories, the first player obtains a higher amount, the second one does, or the agents fail to make a deal. The replicator dynamic results are compared to obtain the naive degree of distortion caused by the subsidies. The results for evolving agents are then examined to figure out the degree to which adaptation compensated for or amplifies this distortion.", notes = "WCCI2016", } @InProceedings{ashlock:2005:CECw, author = "Wendy Ashlock and Dan Ashlock", title = "Single Parent Genetic Programming", booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation", year = "2005", editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer", volume = "2", pages = "1172--1179", address = "Edinburgh, UK", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "2-5 " # sep, organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9363-5", DOI = "doi:10.1109/CEC.2005.1554823", size = "8 pages", abstract = "The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover operator is its potential to induce defensive metaselection for large parse trees, a process usually termed 'bloat'. Single parent genetic programming is a form of genetic programming in which bloat is reduced by doing subtree crossover with a fixed population of ancestor trees. Analysis of mean tree size growth demonstrates that this fixed and limited set of crossover partners provides implicit, automatic control on tree size in the evolving population, reducing the need for additionally disruptive trimming of large trees. The choice of ancestor trees can also incorporate expert knowledge into the genetic programming system. The system is tested on four problems: plus-one-recall-store (PORS), odd parity, plus-times-half (PTH) and a bioinformatic model fitting problem (NIPs). The effectiveness of the technique varies with the problem and choice of ancestor set. At the extremes, improvements in time to solution in excess of 4700-fold were observed for the PORS problem, and no significant improvements for the PTH problem were observed.", notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.", } @InProceedings{ashlock:2006:cecW, author = "Wendy Ashlock", title = "Using Very Small Population Sizes in Genetic Programming", booktitle = "2006 IEEE World Congress on Computational Intelligence, 2006 IEEE Congress on Evolutionary Computation", year = "2006", pages = "1023--1030", address = "Vancouver", month = "16-21 " # jul, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2006.1688325", size = "8 pages", abstract = "This paper examines the use of very small (4-7) population sizes in genetic programming. When using exploitive operators, this results in hillclimbing; when using exploratory operators this results in genetic drift. The end result is a different way of searching the space which gives insight into the fitness landscape and the nature of the variation operators used. This study compares the use of very small population sizes with the use of population sizes up to 1000 for three genetic programming problems: 4-parity using parse trees, Tartarus using ISAc lists, and several versions of plus-onerecall- store (PORS) using parse trees. For 4-parity and Tartarus with 60 ISAc nodes, algorithms with very small population sizes found more solutions faster. For PORS, the effect was less pronounced: more solutions were found, but the algorithm was faster only than when using slightly larger populations. For Tartarus with 30 ISAc nodes, no effect was detected.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = 319--326,", } @InProceedings{Ashlock:2006:ANNIEw, author = "Wendy Ashlock", title = "Mutation vs. Crossover with Genetic Programming", booktitle = "ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks", year = "2006", editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy", volume = "16", address = "St. Louis, MO, USA", month = nov # " 5-8", note = "Part I: Evolutionary Computation", keywords = "genetic algorithms, genetic programming", isbn13 = "0791802566", DOI = "doi:10.1115/1.802566.paper2", abstract = "Understanding how variation operators work leads to a better understanding both of the search space and of the problem being solved. This study examines the behaviour of mutation and crossover operators in genetic programming using parse trees to find solutions to 3-parity and 4-parity. The standard subtree crossover and subtree mutation operators are studied along with two new operators, fold mutation and fusion crossover. They are studied in terms of how often and how fast they solve the problem; how much they change the fitness on average; and what proportion of variations are neutral, harmful, and helpful. It is found that operators behave differently when used alone than when used together with another operator and that some operators behave differently when solving 3-parity and when solving 4-parity.", } @InProceedings{Ashlock:2011:CIBCB, author = "Wendy Ashlock and Daniel Ashlock", title = "Designing artificial organisms for use in biological simulations", booktitle = "IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2011)", year = "2011", month = "11-15 " # apr, address = "Paris", keywords = "genetic algorithms, genetic programming, Smith-Waterman crossover, artificial genes, artificial organisms, biological simulations, context free grammar, gene interaction, genetic programming maximum problem, genomic level, gridwalkers, horizontal gene transfer, plus-one-recall-store, rugged multimodal fitness landscapes, self-avoiding walk problem, size-neutral crossover, variable length strings, biology computing, context-free grammars, genetics", DOI = "doi:10.1109/CIBCB.2011.5948463", size = "8 pages", abstract = "In this paper we investigate two types of artificial organism which have the potential to be useful in biological simulations at the genomic level, such as simulations of speciation or gene interaction. Biological problems of this type are usually studied either with simulations using artificial genes that are merely evolving strings with no phenotype, ignoring the possibly crucial contribution of natural selection, or with real biological data involving so much complexity that it is difficult to sort out the important factors. This research provides a middle ground. The artificial organisms are: gridwalkers (GWs), a variation on the self-avoiding walk problem, and plus-one-recall-store (PORS), a simple genetic programming maximum problem implemented with a context free grammar. Both are known to have rugged multimodal fitness landscapes. We define a new variation operator, a kind of aligned crossover for variable length strings, which we call Smith-Waterman crossover. The problems, using Smith-Waterman crossover, size-neutral crossover (a kind of non-aligned crossover defined in), mutation only, and horizontal gene transfer (such as occurs in biology with retroviruses) are explored. We define a measure called fitness preservation to quantify the differences in their fitness landscapes and to provide guidance to researchers in determining which problem/variation operator set is best for their simulation.", notes = "Also known as \cite{5948463}", } @InProceedings{Ashlock:2019:CIBCB, author = "Daniel Ashlock and Wendy Ashlock and James Montgomery", title = "Implementing Phenotypic Plasticity with an Adaptive Generative Representation", booktitle = "2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", year = "2019", address = "Siena, Italy", month = "9-11 " # jul, keywords = "genetic algorithms, genetic programming, SAW, AGR, agent", isbn13 = "978-1-7281-1463-7", URL = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8791496", DOI = "doi:10.1109/CIBCB.2019.8791496", size = "8 pages", abstract = "This study compares an adaptive and a non-adaptive representation for finding long walks on obstructed grids. This process models adaptation of a simple plant to an environment where the plant's ability to grow is impeded by obstructions such as resource poor areas like bare rock. The intent of the adaptive representation is to model the biological phenomenon of phenotypic plasticity in which gene regulation is at least partially in response to environmental cues, in this case the obstructions. The adaptive representation is found to have a substantial advantage, with the greatest level of advantage at intermediate levels of obstruction. Agents are asked to solve multiple problem instances simultaneously (i.e. using the same chromosome). The advantage of the adaptive representation is also found to be higher when more boards are used in fitness evaluation.", notes = "Is this GP? cites Ashlock:2016:CEC Also known as \cite{8791496}", } @Article{ashofteh:2019:EMaA, author = "Parisa-Sadat Ashofteh and Omid Bozorg-Haddad and Hugo A. Loaiciga", title = "Logical genetic programming {(LGP)} application to water resources management", journal = "Environmental Monitoring and Assessment", year = "2019", volume = "192", number = "1", pages = "Article number: 34", keywords = "genetic algorithms, genetic programming, GP algorithm, LGP approach, Standard operating procedure (SOP) rule, Logical operators, Logical functions, Multi-conditional mathematical problem", URL = "http://link.springer.com/article/10.1007/s10661-019-8014-y", DOI = "doi:10.1007/s10661-019-8014-y", size = "11 pages", abstract = "Genetic programming (GP) is a variant of evolutionary algorithms (EA). EAs are general-purpose search algorithms. Yet, GP does not solve multi-conditional problems satisfactorily. This study improves the GP predictive skill by development and integration of mathematical logical operators and functions to it. The proposed improvement is herein named logical genetic programming (LGP) whose performance is compared with that of GP using examples from the fields of mathematics and water resources. The results of the examples show the LGP superior performance in both examples, with LGP producing improvements of 74 and 42 percent in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP results. The objective functions minimize the mean absolute error (MAE). The comparison of the LGP and GP results with alternative performance criteria demonstrate a better capability of the former algorithm in solving multi-conditional problems.", } @Article{Ashour:2003:CS, author = "A. F. Ashour and L. F. Alvarez and V. V. Toropov", title = "Empirical modelling of shear strength of {RC} deep beams by genetic programming", journal = "Computers and Structures", year = "2003", volume = "81", number = "5", pages = "331--338", month = mar, keywords = "genetic algorithms, genetic programming, Reinforced concrete deep beams, Empirical model building", broken = "http://www.sciencedirect.com/science/article/B6V28-47S6J5M-5/2/03211d57903fd1d7c48ac56fb32d1d36", DOI = "doi:10.1016/S0045-7949(02)00437-6", abstract = "This paper investigates the feasibility of using previous termgeneticnext term programming (GP) to create an empirical model for the complicated non-linear relationship between various input parameters associated with reinforced concrete (RC) deep beams and their ultimate shear strength. GP is a relatively new form of artificial intelligence, and is based on the ideas of Darwinian theory of evolution and previous termgenetics.next term The size and structural complexity of the empirical model are not specified in advance, but these characteristics evolve as part of the prediction. The engineering knowledge on RC deep beams is also included in the search process through the use of appropriate mathematical functions. The model produced by GP is constructed directly from a set of experimental results available in the literature. The validity of the obtained model is examined by comparing its response with the shear strength of the training and other additional datasets. The developed model is then used to study the relationships between the shear strength and different influencing parameters. The predictions obtained from GP agree well with experimental observations.", } @Article{Ashrafian:2020:Measurement, author = "Ali Ashrafian and Amir H. Gandomi and Mohammad Rezaie-Balf and Mohammad Emadi", title = "An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement", journal = "Measurement", year = "2020", volume = "152", pages = "107309", month = feb, keywords = "genetic algorithms, genetic programming, Gene expression programming, Evolutionary approach, Roller compacted concrete pavement, Compressive strength, Prediction", ISSN = "0263-2241", URL = "http://www.sciencedirect.com/science/article/pii/S026322411931173X", DOI = "doi:10.1016/j.measurement.2019.107309", abstract = "The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to use, and provide consequently new formulations for fc of RCCP.", notes = "also known as \cite{ASHRAFIAN2020107309}", } @Article{ASIF:2024:cscm, author = "Usama Asif and Muhammad Faisal Javed and Maher Abuhussain and Mujahid Ali and Waseem Akhtar Khan and Abdullah Mohamed", title = "Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners", journal = "Case Studies in Construction Materials", volume = "20", pages = "e03135", year = "2024", ISSN = "2214-5095", DOI = "doi:10.1016/j.cscm.2024.e03135", URL = "https://www.sciencedirect.com/science/article/pii/S2214509524002869", keywords = "genetic algorithms, genetic programming, Plastic concrete, Machine learning, Compressive strength, Flexural strength, Sustainability, Ensemble learning algorithms, Gene expression programming", abstract = "This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model's development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables' relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete", } @Article{ASIM:2018:SDEE, author = "Khawaja M. Asim and Adnan Idris and Talat Iqbal and Francisco Martinez-Alvarez", title = "Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification", journal = "Soil Dynamics and Earthquake Engineering", volume = "111", pages = "1--7", year = "2018", keywords = "genetic algorithms, genetic programming, Earthquake predictor system, Seismic indicators, AdaBoost, Earthquake prediction", ISSN = "0267-7261", URL = "http://www.sciencedirect.com/science/article/pii/S0267726118301349", URL = "https://iranarze.ir/wp-content/uploads/2018/09/E9269-IranArze.pdf", DOI = "doi:10.1016/j.soildyn.2018.04.020", size = "7 pages", abstract = "In this study an earthquake predictor system is proposed by combining seismic indicators along with Genetic Programming (GP) and AdaBoost (GP-AdaBoost) based ensemble method. Seismic indicators are computed through a novel methodology in which, the indicators are computed to obtain maximum information regarding seismic state of the region. The computed seismic indicators are used with GP-AdaBoost algorithm to develop an Earthquake Predictor system (EP-GPBoost). The setup has been arranged to provide predictions for earthquakes of magnitude 5.0 and above, fifteen days prior to the earthquake. The regions of Hindukush, Chile and Southern California are considered for experimentation. The EP-GPBoost has produced noticeable improvement in earthquake prediction due to collaboration of strong searching and boosting capabilities of GP and AdaBoost, respectively. The earthquake predictor system shows enhanced results in terms of accuracy, precision and Matthews Correlation Coefficient for the three considered regions in comparison to contemporary results", notes = "Centre for Earthquake Studies, National Centre for Physics, Pakistan", } @Article{ASKARI:2022:renene, author = "Ighball Baniasad Askari and Amin Shahsavar and Mehdi Jamei and Francesco Calise and Masoud Karbasi", title = "A parametric assessing and intelligent forecasting of the energy and exergy performances of a dish concentrating photovoltaic/thermal collector considering six different nanofluids and applying two meticulous soft computing paradigms", journal = "Renewable Energy", volume = "193", pages = "149--166", year = "2022", ISSN = "0960-1481", DOI = "doi:10.1016/j.renene.2022.04.155", URL = "https://www.sciencedirect.com/science/article/pii/S0960148122006231", keywords = "genetic algorithms, genetic programming, Dish concentrating photovoltaic thermal system, Exergy, Multi-gene genetic optimization, Nanofluid, Thermodynamic analysis", abstract = "In the present study, the application of six engine oil-based Nano fluids (NFs) in a solar concentrating photovoltaic thermal (CPVT) collector is investigated. The calculations were performed for different values of nanoparticle volume concentration, receiver tube diameter, concentrator surface area, receiver length, receiver actual to the maximum number of channels ratio, beam radiation, and a constant volumetric flow rate. Besides, two novel soft computing paradigms namely, the cascaded forward neural network (CFNN) and Multi-gene genetic programming (MGGP) were adopted to predict the first law efficiency (?I) and second law efficiency (?II) of the system based on the influential parameters, as the input features. It was found that the increase of nanoparticle concentration leads to an increase in ?I and a decrease in ?II. Moreover, the rise of both the concentrator surface area (from 5 m2 to 20 m2) and beam irradiance (from 150 W/m2 to 1000 W/m2) entails an increase in both the ?I (by 39percent and 261percent) and ?II (by 55percent and 438percent). Furthermore, it was reported that the pattern of changes in both ?I and ?II with serpentine tube diameter, receiver plate length, and absorber tube length is increasing-decreasing. The results of modeling demonstrated that the CFNN had superior performance than the MGGP model", } @InProceedings{EUSIPCO:2010, author = "Muhammad Waqar Aslam and Asoke Kumar Nandi", title = "Detection of Diabetes Using Genetic Programming", booktitle = "18th European Signal Processing Conference, EUSIPCO 2010", year = "2010", pages = "1184--1188", month = aug # " 23-27", organization = "Eurasip", keywords = "genetic algorithms, genetic programming", URL = "http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569291873.pdf", size = "5 pages", abstract = "Diabetes is one of the common and rapidly increasing diseases in the world. It is a major health problem in most of the countries. Due to its importance, the need for automated detection of this disease is increasing. The method proposed here uses genetic programming (GP) and a variation of genetic programming called GP with comparative partner selection (CPS) for diabetes detection. The proposed system consists of two stages. In first stage we use genetic programming to produce an individual from training data, that converts the available features to a single feature such that it has different values for healthy and patient (diabetes) data. In the next stage we use test data for testing of that individual. The proposed system was able to achieve 78.5 (pm 2.2)percent accuracy. The results showed that GP based classifier can assist in the diagnosis of diabetes disease.", owner = "waqar", timestamp = "2013.03.25", } @InProceedings{Aslam:2010:milcom, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke K. Nandi", title = "Automatic digital modulation classification using Genetic Programming with K-Nearest Neighbor", booktitle = "MILCOM 2010", year = "2010", month = oct # " 31-" # nov # " 3", pages = "1731--1736", abstract = "Automatic modulation classification is an intrinsically interesting problem with various civil and military applications. A generalised digital modulation classification algorithm has been developed and presented in this paper. The proposed algorithm uses Genetic Programming (GP) with K-Nearest Neighbour (K-NN). The algorithm is used to identify BPSK, QPSK, 16QAM and 64QAM modulations. Higher order cumulants have been used as input features for the algorithm. A two-stage classification approach has been used to improve the classification accuracy. The high performance of the method is demonstrated using computer simulations and in comparisons with existing methods.", keywords = "genetic algorithms, genetic programming, 16QAM, 64QAM, BPSK, K-nearest neighbour, QPSK, automatic digital modulation classification, civil application, computer simulations, military application, quadrature amplitude modulation, quadrature phase shift keying, signal classification", DOI = "doi:10.1109/MILCOM.2010.5680232", ISSN = "2155-7578", notes = "Also known as \cite{5680232}", } @InProceedings{EUSIPCO:2011, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Robust QAM Classification Using Genetic Programming and Fisher Criterion", booktitle = "19th European Signal Processing Conference, EUSIPCO 2011", year = "2011", pages = "995--999", address = "Barcelona, Spain", month = "28 " # aug # " - 2 " # sep, organization = "Eurasip", keywords = "genetic algorithms, genetic programming", URL = "http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569422149.pdf", size = "5 pages", abstract = "Automatic modulation recognition has seen increasing demand in recent years. It has found many applications in wireless communications, including both civilian and military applications. It is a scheme to identify automatically the modulation type of received signal by observing data samples of received signals in the presence of noise. In this paper a combination of genetic programming (GP) and Fisher criterion is proposed for classification of QAM modulation schemes for the first time. This method appears to be both efficient and robust. Due to an increase in importance of QAM modulations schemes in recent times we have used QAM for classification purpose. The modulations considered here are QAM16 and QAM64. Simulations and results show that the performance achieved using GP are better than other methods presented so far", timestamp = "2013.03.25", } @Article{Aslam:2012:ieeeTWC, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Automatic Modulation Classification Using Combination of Genetic Programming and {KNN}", journal = "IEEE Transactions on Wireless Communications", year = "2012", volume = "11", number = "8", pages = "2742--2750", month = aug, keywords = "genetic algorithms, genetic programming, Automatic modulation classification, K-nearest neighbour, Classification using genetic programming, Higher order cumulants", ISSN = "1536-1276", DOI = "doi:10.1109/TWC.2012.060412.110460", size = "9 pages", abstract = "Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbour (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.", notes = "Also known as \cite{6213036}", } @PhdThesis{AslamMuh_Feb2013_10353, author = "Muhammad Waqar Aslam", title = "Pattern recognition using genetic programming for classification of diabetes and modulation data", school = "University of Liverpool", year = "2013", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming", URL = "http://repository.liv.ac.uk/10353/1/AslamMuh_Feb2013_10353.pdf", URL = "http://repository.liv.ac.uk/10353/", URL = "http://ethos.bl.uk/OrderDetails.do?did=47&uin=uk.bl.ethos.579375", size = "220 pages", abstract = "The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been used in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are used to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods.", notes = "Supervisor: Asoke Kumar Nandi uk.bl.ethos.579375", } @Article{Aslam:2013:ESA, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Feature generation using genetic programming with comparative partner selection for diabetes classification", journal = "Expert Systems with Applications", volume = "40", number = "13", pages = "5402--5412", year = "2013", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2013.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0957417413002406", abstract = "Abstract The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov-Smirnov test, Kullback-Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), using the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbour and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.", keywords = "genetic algorithms, genetic programming, Pima Indian diabetes, Comparative partner selection", } @InProceedings{Aslam:2013:MLSP, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", booktitle = "IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013)", title = "Improved comparative partner selection with brood recombination for genetic programming", year = "2013", month = "22-25 " # sep, keywords = "genetic algorithms, genetic programming, brood recombination, improved comparative partner selection", DOI = "doi:10.1109/MLSP.2013.6661901", ISSN = "1551-2541", size = "5 pages", abstract = "The aim of all evolutionary methods is to find the best solution from search space without testing every solution in search space. This study employs strengths and weaknesses of solutions for finding the best solution of any problem in genetic programming. The strengths and weaknesses are used to assist in finding the right partners (solutions) during crossover operation. The probability of crossover between two solutions is evaluated using relative strengths and weaknesses as well as overall strengths of solutions (Improved Comparative Partner Selection (ICPS)). The solutions qualifying for crossover through ICPS criteria are supposed to produce better solutions and are allowed to produce more children through brood recombination. The brood recombination helps to exploit the search space close to the optimum solution more efficiently. The proposed method is applied on different benchmarking problems and results demonstrate that the method is highly efficient in exploring the search space.", notes = "Also known as \cite{6661901}", } @InProceedings{Aslam:2015:SAI, author = "Muhammad Waqar Aslam", booktitle = "Science and Information Conference (SAI 2015)", title = "Selection of fitness function in genetic programming for binary classification", year = "2015", pages = "489--493", abstract = "Fitness function is a key parameter in genetic programming (GP) and is also known as the driving force of GP. It determines how well a solution is able to solve the given problem. The design of fitness function is instrumental in performance improvement of GP. In this study we evaluate different fitness functions for binary classification using two benchmarking datasets. Two types of fitness functions are used. One type uses statistical distribution of classes in the datasets and the other uses machine learning classifiers. A detailed analysis and comparison are given between different fitness functions in terms of performance and computational complexity.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SAI.2015.7237187", month = jul, notes = "Also known as \cite{7237187}", } @Article{ASLAM:2018:ASC, author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke Kumar Nandi", title = "Diverse partner selection with brood recombination in genetic programming", journal = "Applied Soft Computing", volume = "67", pages = "558--566", year = "2018", keywords = "genetic algorithms, genetic programming, Diversity, Partner selection, Brood recombination", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2018.03.035", URL = "http://www.sciencedirect.com/science/article/pii/S1568494618301571", abstract = "The ultimate goal of learning algorithms is to find the best solution from a search space without testing each and every solution available in the search space. During the evolution process new solutions (children) are produced from existing solutions (parents), where new solutions are expected to be better than existing solutions. This paper presents a new parent selection method for the crossover operation in genetic programming. The idea is to promote crossover between two behaviourally (phenotype) diverse parents such that the probability of children being better than their parents increases. The relative phenotype strengths and weaknesses of pairs of parents are exploited to find out if their crossover is beneficial or not (diverse partner selection (DPS)). Based on the probable improvement in children compared to their parents, crossover is either allowed or disallowed. The parents qualifying for crossover through this process are expected to produce much better children and are allowed to produce more children than normal parents through brood recombination (BR). BR helps to explore the search space around diverse parents much more efficiently. Experimental results from different benchmarking problems demonstrate that the proposed method (DPS with BR) improves the performance of genetic programming significantly", } @InProceedings{Aslan:2019:evoapplications, author = "Mehmet Aslan and Sevil Sen", title = "Evolving Trust Formula to Evaluate Data Trustworthiness in {VANETs} Using Genetic Programming", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "413--429", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Trust management, Data trust, Vehicular Ad Hoc Networks, VANETs", isbn13 = "978-3-030-16691-5", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar19-1.pdf", DOI = "doi:10.1007/978-3-030-16692-2_28", size = "16 pages", abstract = "Vehicular Ad Hoc Networks (VANETs) provide traffic safety, improve traffic efficiency and present infotainment by sending messages about events on the road. Trust is widely used to distinguish genuine messages from fake ones. However, trust management in VANETs is a challenging area due to their dynamically changing and decentralized topology. In this study, a genetic programming based trust management model for VANETs is proposed to properly evaluate trustworthiness of data about events. A large number of features is introduced in order to take into account VANETs complex characteristics. Simulations with bogus information attack scenarios show that the proposed trust model considerably increase the security of the network.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{ASLAN:2023:vehcom, author = "Mehmet Aslan and Sevil Sen", title = "A dynamic trust management model for vehicular ad hoc networks", journal = "Vehicular Communications", volume = "41", pages = "100608", year = "2023", ISSN = "2214-2096", DOI = "doi:10.1016/j.vehcom.2023.100608", URL = "https://www.sciencedirect.com/science/article/pii/S2214209623000384", keywords = "genetic algorithms, genetic programming, Vehicular ad hoc networks, Security, Trust management, Evolutionary computation, Evolutionary dynamic optimization", abstract = "Trust management in vehicular ad hoc networks (VANETs) is a challenging dynamic optimization problem due to their decentralized, infrastructureless, and dynamically changing topology. Evolutionary computation (EC) algorithms are good candidates for solving dynamic optimization problems (DOPs), since they are inspired from the biological evolution that is occurred as a result of changes in the environment. In this study, we explore the use of genetic programming (GP) algorithm and evolutionary dynamic optimization (EDO) techniques to build a dynamic trust management model for VANETs. The proposed dynamic trust management model properly evaluates the trustworthiness of vehicles and their messages in the simulation of experimental scenarios including bogus information attacks. The simulation results show that the evolved trust calculation formula prevents the propagation of bogus messages over VANETs successfully and the dynamic trust management model detects changes in the problem and reacts to them in a timely manner. The best evolved formula achieves 89.38percent Matthews Correlation Coefficient (MCC), 91.81percent detection rate (DR), and 1.01percent false positive rate (FPR), when approx 5percent of the network traffic is malicious. The formula obtains 87.33percent MCC, 92.01percent DR, and 4.8percent FPR when approx 40percent of the network traffic is malicious, demonstrating its robustness to increasing malicious messages. The proposed model is also run on a real-world traffic model and obtains high MCC and low FPR values. To the best of our knowledge, this is the first application of EC and EDO techniques that generate a trust formula automatically for dynamic trust management in VANETs", } @Article{Asonitis:2023:GPEM, author = "Tasos Asonitis and Richard Allmendinger and Matt Benatan and Ricardo Climent", title = "{SonOpt}: understanding the behaviour of bi-objective population-based optimisation algorithms through sound", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", pages = "article no. 3", note = "Special Issue: Evolutionary Computation in Art, Music and Design", keywords = "genetic algorithms, genetic programming, Sonification, Multi-objective optimisation, Population-based optimisation algorithms, Algorithm behaviour, Hypervolume, Sound", ISSN = "1389-2576", URL = "https://rdcu.be/c7KTf", DOI = "doi:10.1007/s10710-023-09451-5", code_url = "https://github.com/tasos-a/SonOpt-2.0", size = "41 pages", abstract = "We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt used two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path", } @Article{Asouti:2009:GPEM, author = "V. G. Asouti and I. C. Kampolis and K. C. Giannakoglou", title = "A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "373--389", month = dec, keywords = "genetic algorithms, Asynchronous evolutionary algorithms, Metamodels, Grid computing, Aerodynamic shape optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9090-5", size = "17 pages", abstract = "A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single- and multi-objective CFD-based designs of isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated.", notes = "Parallel CFD & Optimization Unit, Lab. of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, P.O. Box 64069, Athens, 15710, Greece", } @Article{journals/swevo/AssimiJN17, author = "Hirad Assimi and Ali Jamali and Nader Nariman-Zadeh", title = "Sizing and topology optimization of truss structures using genetic programming", journal = "Swarm and Evolutionary Computation", year = "2017", volume = "37", pages = "90--103", month = dec, keywords = "genetic algorithms, genetic programming, topology optimisation, sizing optimisation, truss structure", bibdate = "2017-12-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/swevo/swevo37.html#AssimiJN17", DOI = "doi:10.1016/j.swevo.2017.05.009", abstract = "This paper presents a genetic programming approach for simultaneous optimisation of sizing and topology of truss structures. It aims to find the optimal cross-sectional areas and connectivities of the joints to achieve minimum weight in the search space. The structural optimisation problem is subjected to kinematic stability, maximum allowable stress and deflection. This approach uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the optimum solution. This method has the capability to identify redundant truss elements and joints in the design space. The obtained results are compared with existing popular and competent techniques in literature and its competence as a tool in the optimisation problem are demonstrated in solving some benchmark examples, the proposed approach provided lighter truss structures than the available solutions reported in the literature.", } @Article{journals/eswa/AssimiJ18, author = "Hirad Assimi and Ali Jamali", title = "A hybrid algorithm coupling genetic programming and Nelder-Mead for topology and size optimization of trusses with static and dynamic constraints", journal = "Expert Systems with Applications", year = "2018", volume = "95", pages = "127--141", ISSN = "0957-4174", keywords = "genetic algorithms, genetic programming", bibdate = "2018-01-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/eswa/eswa95.html#AssimiJ18", DOI = "doi:10.1016/j.eswa.2017.11.035", } @Article{assimi:NCaA, author = "Hirad Assimi and Ali Jamali and Nader Nariman-zadeh", title = "Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator", journal = "Neural Computing and Applications", year = "2019", volume = "31", number = "10", pages = "5729--5749", month = oct, keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Topology, Truss, Adaptive mutant operator", ISSN = "0941-0643", URL = "http://link.springer.com/article/10.1007/s00521-018-3401-9", DOI = "doi:10.1007/s00521-018-3401-9", size = "21 pages", abstract = "Most real-world engineering problems deal with multiple conflicting objectives simultaneously. In order to address this issue in truss optimization, this paper presents a multi-objective genetic programming approach for sizing and topology optimization of trusses. It aims to find the optimal cross-sectional areas and connectivities between the nodes to achieve a set of trade-off solutions to satisfy all the optimization objective functions subjected to some constraints such as kinematic stability, maximum allowable stress in members and nodal deflections. It also uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the potential final set of solutions. This approach also employs an adaptive mutant factor besides the classical genetic operators to improve the exploring capabilities of Genetic Programming in structural optimization. The intrinsic features of genetic programming help to identify redundant truss members and nodes in the design space, while no violation of constraints occurs. Our approach applied to some numerical examples and found a better non-dominated solution set in the most cases in comparison with the competent methods available in the literature.", } @InProceedings{Assis:2014:CICS, author = "Carlos A. S. Assis and Adriano C. M. Pereira and Marconi A. Pereira and Eduardo G. Carrano", booktitle = "IEEE Symposium on Computational Intelligence in Cyber Security (CICS 2014)", title = "A genetic programming approach for fraud detection in electronic transactions", year = "2014", month = dec, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CICYBS.2014.7013373", size = "9 pages", abstract = "The volume of on line transactions has increased considerably in the recent years. Consequently, the number of fraud cases has also increased, causing billion dollar losses each year worldwide. Therefore, it is mandatory to employ mechanisms that are able to assist in fraud detection. In this work, it is proposed the use of Genetic Programming (GP) to identify frauds (charge back) in electronic transactions, more specifically in online credit card operations. A case study, using a real dataset from one of the largest Latin America electronic payment systems, has been conducted in order to evaluate the proposed algorithm. The presented algorithm achieves good performance in fraud detection, obtaining gains up to 17percent with regard to the actual company baseline. Moreover, several classification problems, with considerably different datasets and domains, have been used to evaluate the performance of the algorithm. The effectiveness of the algorithm has been compared with other methods, widely employed for classification. The results show that the proposed algorithm achieved good classification effectiveness in all tested instances.", notes = "Centro Fed. de Educ., Tecnol. de Minas Gerais, Belo Horizonte, Brazil Also known as \cite{7013373}", } @InProceedings{Assuncao:2017:CEC, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Automatic generation of neural networks with structured Grammatical Evolution", year = "2017", pages = "1557--1564", abstract = "The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANN. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", DOI = "doi:10.1109/CEC.2017.7969488", month = jun, notes = "Also known as \cite{7969488}", } @InProceedings{Assuncao:2017:GECCO, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Towards the Evolution of Multi-layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4920-8", address = "Berlin, Germany", pages = "393--400", size = "8 pages", URL = "http://doi.acm.org/10.1145/3071178.3071286", DOI = "doi:10.1145/3071178.3071286", acmid = "3071286", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, Artificial Neural Networks, Classification, Grammar-based Genetic Programming, NeuroEvolution", month = "15-19 " # jul, abstract = "Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs) composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons.", notes = "Also known as \cite{Assun\&\#xE7;\&\#xE3;o:2017:TEM:3071178.3071286} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{Assuncao:2018:EuroGP, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Using {GP} is {NEAT}: Evolving Compositional Pattern Production Functions", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "3--18", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-77552-4", DOI = "doi:10.1007/978-3-319-77553-1_1", abstract = "The success of Artificial Neural Networks (ANNs) highly depends on their architecture and on how they are trained. However, making decisions regarding such domain specific issues is not an easy task, and is usually performed by hand, through an exhaustive trial-and-error process. Over the years, researches have developed and proposed methods to automatically train ANNs. One example is the HyperNEAT algorithm, which relies on NeuroEvolution of Augmenting Topologies (NEAT) to create Compositional Pattern Production Networks (CPPNs). CPPNs are networks that encode the mapping between neuron positions and the synaptic weight of the ANNs connection between those neurons. Although this approach has obtained some success, it requires meticulous parametrisation to work properly. In this article we present a comparison of different Evolutionary Computation methods to evolve Compositional Pattern Production Functions: structures that have the same goal as CPPNs, but that are encoded as functions instead of networks. In addition to NEAT three methods are used to evolve such functions: Genetic Programming (GP), Grammatical Evolution, and Dynamic Structured Grammatical Evolution. The results show that GP is able to obtain competitive performance, often surpassing the other methods, without requiring the fine tuning of the parameters.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @InProceedings{Assuncao:2018:EuroGPa, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Evolving the Topology of Large Scale Deep Neural Networks", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "19--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Convolutional Neural Networks, Deep Neural Networks, Genetic Algorithm, Dynamic Structured Grammatical Evolution", isbn13 = "978-3-319-77552-4", URL = "http://www.human-competitive.org/sites/default/files/assuncao-paper-a.pdf", DOI = "doi:10.1007/978-3-319-77553-1_2", size = "16 pages", abstract = "In the recent years Deep Learning has attracted a lot of attention due to its success in difficult tasks such as image recognition and computer vision. Most of the success in these tasks is merit of Convolutional Neural Networks (CNNs), which allow the automatic construction of features. However, designing such networks is not an easy task, which requires expertise and insight. In this paper we introduce DENSER, a novel representation for the evolution of deep neural networks. In concrete we adapt ideas from Genetic Algorithms (GAs) and Grammatical Evolution (GE) to enable the evolution of sequences of layers and their parameters. We test our approach in the well-known image classification CIFAR-10 dataset. The results show that our method: (i) outperforms previous evolutionary approaches to the generations of CNNs; (ii) is able to create CNNs that have state-of-the-art performance while using less prior knowledge (iii) evolves CNNs with novel topologies, unlikely to be designed by hand. For instance, the best performing CNNs obtained during evolution has an unexpected structure using six consecutive dense layers. On the CIFAR-10 the best model reports an average error of 5.87percent on test data.", notes = "2018 HUMIES finalist Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Misc{DBLP:journals/corr/abs-1801-01563, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "{DENSER:} Deep Evolutionary Network Structured Representation", howpublished = "arXiv", year = "2018", edition = "v3", month = "1 " # jun, volume = "abs/1801.01563", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", URL = "http://www.human-competitive.org/sites/default/files/assuncao-paper-b_0.pdf", URL = "http://arxiv.org/abs/1801.01563", size = "11 pages", abstract = "Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation. The algorithm not only searches for the best network topology (e.g., number of layers, type of layers), but also tunes hyper-parameters, such as, learning parameters or data augmentation parameters. The automatic design is achieved using a representation with two distinct levels, where the outer level encodes the general structure of the network, i.e., the sequence of layers, and the inner level encodes the parameters associated with each layer. The allowed layers and range of the hyper-parameters values are defined by means of a human-readable Context-Free Grammar. DENSER was used to evolve ANNs for CIFAR-10, obtaining an average test accuracy of 94.13percent. The networks evolved for the CIFA--10 are tested on the MNIST, Fashion-MNIST, and CIFAR-100; the results are highly competitive, and on the CIFAR-100 we report a test accuracy of 78.75percent. our CIFAR-100 results are the highest performing models generated by methods that aim at the automatic design of Convolutional Neural Networks (CNNs), and are amongst the best for manually designed and fine-tuned CNNs.", notes = "See \cite{Assuncao:2019:GPEM}. 2018 HUMIES finalist https://github.com/fillassuncao/denser-models Python 2", } @InProceedings{Assuncao:2019:EuroGP, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "Fast {DENSER}: Efficient Deep {NeuroEvolution}", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "197--212", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, ANN: Poster", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_13", size = "16 pages", abstract = "The search for Artificial Neural Networks (ANNs) that are effective in solving a particular task is a long and time consuming trial-and-error process where we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of such approaches is that they require large amounts of computational resources, and take a long time to generate a solution that is considered acceptable for the problem at hand. The current paper extends Deep Evolutionary Network Structured Representation (DENSER): a general-purpose NeuroEvolution (NE) approach that combines the principles of Genetic Algorithms with Grammatical Evolution; to adapt DENSER to optimise networks of different structures, or to solve various problems the user only needs to change the grammar that is specified in a text human-readable format. The new method, Fast DENSER (F-DENSER), speeds up DENSER, and adds another representation-level that allows the connectivity of the layers to be evolved. The results demonstrate that F-DENSER has a speedup of 20 times when compared to the time DENSER takes to find the best solutions. Concerning the effectiveness of the approach, the results are highly competitive with the state-of-the-art, with the best performing network reporting an average test accuracy of 91.46percent on CIFAR-10. This is particularly remarkable since the reduction in the running time does not compromise the performance of the generated solutions.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{Assuncao:2019:GPEM, author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro", title = "{DENSER}: deep evolutionary network structured representation", journal = "Genetic Programming and Evolvable Machines", year = "2019", volume = "20", number = "1", pages = "5--35", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", ISSN = "1389-2576", URL = "https://arxiv.org/abs/1801.01563", DOI = "doi:10.1007/s10710-018-9339-y", code_url = "https://github.com/fillassuncao/denser-models", size = "31 pages", abstract = "Deep evolutionary network structured representation (DENSER) is a novel evolutionary approach for the automatic generation of deep neural networks (DNNs) which combines the principles of genetic algorithms (GAs) with those of dynamic structured grammatical evolution (DSGE). The GA-level encodes the macro structure of evolution, i.e., the layers, learning, and/or data augmentation methods (among others); the DSGE-level specifies the parameters of each GA evolutionary unit and the valid range of the parameters. The use of a grammar makes DENSER a general purpose framework for generating DNNs: one just needs to adapt the grammar to be able to deal with different network and layer types, problems, or even to change the range of the parameters. DENSER is tested on the automatic generation of convolutional neural networks (CNNs) for the CIFAR-10 dataset, with the best performing networks reaching accuracies of up to 95.22percent. Furthermore, we take the fittest networks evolved on the CIFAR-10, and apply them to classify MNIST, Fashion-MNIST, SVHN, Rectangles, and CIFAR-100. The results show that the DNNs discovered by DENSER during evolution generalise, are robust, and scale. The most impressive result is the 78.75percent classification accuracy on the CIFAR-100 dataset, which, sets a new state-of-the-art on methods that seek to automatically design CNNs.", } @Misc{assunccao2019automatic, author = "Filipe Assuncao and Joao Correia and Ruben Conceicao and Mario Pimenta and Bernardo Tome and Nuno Lourenco and Penousal Machado", title = "Automatic Design of Artificial Neural Networks for Gamma-Ray Detection", howpublished = "arXiv", year = "2019", month = "9 " # may, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", URL = "https://arxiv.org/abs/1905.03532", URL = "http://human-competitive.org/sites/default/files/f_denser_gamma_hadron.pdf", size = "8 pages", abstract = "The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.", notes = "Entered 2019 Humies. Also known as \cite{DBLP:journals/corr/abs-1905-03532}", } @InProceedings{Assuncao:2020:EuroGP, author = "Filipe Assuncao and Nuno Lourenco and Bernardete Ribeiro and Penousal Machado", title = "Incremental Evolution and Development of Deep Artificial Neural Networks", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "35--51", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, ANN, Incremental development, NeuroEvolution, Convolutional Neural Networks", isbn13 = "978-3-030-44093-0", video_url = "https://youtu.be/XuBDIgbpqZM", DOI = "doi:10.1007/978-3-030-44094-7_3", abstract = "NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks (ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems.", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Assuncao:2020:evoapplications, author = "Filipe Assuncao and Nuno Lourenco and Bernardete Ribeiro and Penousal Machado", title = "Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution", booktitle = "23rd International Conference, EvoApplications 2020", year = "2020", month = "15-17 " # apr, editor = "Pedro A. Castillo and Juan Luis {Jimenez Laredo} and Francisco {Fernandez de Vega}", series = "LNCS", volume = "12104", publisher = "Springer Verlag", address = "Seville, Spain", pages = "530--545", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Automated Machine Learning, Scikit-Learn, Dynamic Structured Grammatical", isbn13 = "978-3-030-43721-3", video_url = "https://www.youtube.com/watch?v=ZXG4MohPDXQ", DOI = "doi:10.1007/978-3-030-43722-0_34", abstract = "The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE: a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.", notes = "CISUC, Department of Informatics Engineering, University of Coimbra, Portugal http://www.evostar.org/2020/ EvoApplications2020 held in conjunction with EuroGP'2020, EvoMusArt2020 and EvoCOP2020", } @Article{ASTERIS:2021:CBM, author = "Panagiotis G. Asteris and Athanasia D. Skentou and Abidhan Bardhan and Pijush Samui and Paulo B. Lourenco", title = "Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests", journal = "Construction and Building Materials", volume = "303", pages = "124450", year = "2021", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.124450", URL = "https://www.sciencedirect.com/science/article/pii/S0950061821022078", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Compressive strength of Concrete, Non-destructive testing methods, Soft computing, Artificial Intelligence", abstract = "This study presents a comparative assessment of conventional soft computing techniques in estimating the compressive strength (CS) of concrete using two non-destructive tests, namely ultrasonic pulse velocity and rebound hammer test. In specific, six conventional soft computing models namely back-propagation neural network (BPNN), relevance vector machine, minimax probability machine regression, genetic programming, Gaussian process regression, and multivariate adaptive regression spline, were used. To construct and validate these models, a total of 629 datasets were collected from the literature. Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values. The results of the employed MARS and BPNN models are significantly better than those obtained in earlier studies. Thus, these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level", } @Article{ASTERIS:2021:ES, author = "Panagiotis G. Asteris and Paulo B. Lourenco and Mohsen Hajihassani and Chrissy-Elpida N. Adami and Minas E. Lemonis and Athanasia D. Skentou and Rui Marques and Hoang Nguyen and Hugo Rodrigues and Humberto Varum", title = "Soft computing-based models for the prediction of masonry compressive strength", journal = "Engineering Structures", volume = "248", pages = "113276", year = "2021", ISSN = "0141-0296", DOI = "doi:10.1016/j.engstruct.2021.113276", URL = "https://www.sciencedirect.com/science/article/pii/S0141029621013997", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Machine learning, Masonry, Metaheuristic algorithms, Compressive strength", abstract = "Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature", } @Article{ASTERIS:2021:TG, author = "Panagiotis G. Asteris and Anna Mamou and Mohsen Hajihassani and Mahdi Hasanipanah and Mohammadreza Koopialipoor and Tien-Thinh Le and Navid Kardani and Danial J. Armaghani", title = "Soft computing based closed form equations correlating {L} and {N}-type {Schmidt} hammer rebound numbers of rocks", journal = "Transportation Geotechnics", year = "2021", volume = "29", month = jul, pages = "100588", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Machine learning, Metaheuristic algorithms, Non-destructive testing, Rocks, Schmidt hammer rebound number", ISSN = "2214-3912", DOI = "doi:10.1016/j.trgeo.2021.100588", URL = "https://www.sciencedirect.com/science/article/pii/S2214391221000787", abstract = "This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than plus-minus20percent deviation from the experimental data for 97.27percent of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40-75.97 and which is made available as supplementary material", notes = "Also known as \cite{ASTERIS2021100588}", } @Article{ates:2023:Algorithms, author = "Cihan Ates and Dogan Bicat and Radoslav Yankov and Joel Arweiler and Rainer Koch and Hans-Jorg Bauer", title = "Model Predictive Evolutionary Temperature Control via {Neural-Network-Based} Digital Twins", journal = "Algorithms", year = "2023", volume = "16", number = "8", pages = "Article No. 387", keywords = "genetic algorithms, genetic programming", ISSN = "1999-4893", URL = "https://www.mdpi.com/1999-4893/16/8/387", DOI = "doi:10.3390/a16080387", abstract = "In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is used to virtually test alternative control actions for a multi-objective optimisation task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by using an evolutionary algorithm on measured data, a population of control laws can be effectively learnt in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller's performance, even with limited training data.", notes = "also known as \cite{a16080387}", } @InProceedings{Athapaththu:2020:ICAC, author = "A. M. H. N Athapaththu and D. U. S Illeperumarachchi and H. M. K. U Herath and H. K Jayasinghe and W. H. Rankothge and Narmadha Gamage", title = "Supply and Demand Planning for Water: A Sustainable Water Management System", booktitle = "2020 2nd International Conference on Advancements in Computing (ICAC)", year = "2020", volume = "1", pages = "305--310", abstract = "Sustainable water management requires maintaining the balance between the demand and supply, specifically addressing water demand in urban, agricultural, and natural systems. Having an insight on water supply forecasting and water consumption forecasting, will be useful to generate an optimal water distribution plan. A platform that targets the sustainable water management concepts for domestic usage and paddy cultivation is proposed in this paper, with the following components: (1) forecasting water levels of reservoirs, (2) forecasting water consumption patterns, and (3) optimizing the water distribution. We have used Recurrent Neural Network (RNN) and, Long Short-Term Memory (LSTM) for forecasting modules and, Genetic Programming (GP) for optimizing water distribution. Our results show that, using our proposed modules, sustainable water management related services can be automated efficiently and effectively.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICAC51239.2020.9357256", month = dec, notes = "Also known as \cite{9357256}", } @Article{journals/aai/AticiE17, author = "Umit Atici and Adem Ersoy", title = "Applied Genetic Programming for Predicting Specific Cutting Energy for Cutting Natural Stones", journal = "Applied Artificial Intelligence", year = "2017", number = "5-6", volume = "31", pages = "439--452", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-12-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/aai/aai31.html#AticiE17", DOI = "doi:10.1080/08839514.2017.1378140", abstract = "n the processing of marbles and other natural stones, the major cost involved in sawing with circular diamond sawblades is the energy cost. This paper reports a new and efficient approach to the formulation of SEcut using gene expression programming (GEP) based on not only rock characteristics but also design and operating parameters. Twenty-three rock types classified into four groups were cut using three types of circular diamond saws at different feed rates, depths of cut, and peripheral speeds. The input parameters used to develop the GEP-based SEcut prediction model were as follows: physico-mechanical rock characteristics (uniaxial compressive strength, Shore scleroscope hardness, Schmidt rebound hardness, and Bohme surface abrasion), operating parameters (feed rate, depth of cut, and peripheral speed), and a design variable (diamond concentration in the sawblade). The performance of the model was comprehensively evaluated on the basis of statistical criteria such as R2 (0.95).", } @InProceedings{Atiencia-Villagomez:2012:ICIEA, author = "Jose Miguel {Atiencia Villagomez} and Askhat Diveev and Elena Sofronova", title = "The network operator method for synthesis of intelligent control system", booktitle = "7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012", year = "2012", editor = "Wenxiang Xie", pages = "174--179", address = "Singapore", month = "18-20 " # jul, keywords = "genetic algorithms, genetic programming, aircraft control", isbn13 = "978-1-4577-2117-5", DOI = "doi:10.1109/ICIEA.2012.6360718", abstract = "Application of the network operator for the synthesis of intelligent control systems is considered. An example of the synthesis of automatic control on the space trajectories of unmanned helicopter is given.", notes = "Network operator matrices, NOM, logical network operator. quadrotor. http://www.network-operator.com/ www page includes demo. Entered for 2013 HUMIES GECCO 2013 Peoples' Friendship University of Russia, Moscow, Russia. Also known as \cite{6360718}", } @Article{Atiquzzaman:2016:IJHST, title = "Prediction of inflows from dam catchment using genetic programming", author = "Md Atiquzzaman and Jaya Kandasamy", journal = "International Journal of Hydrology Science and Technology", year = "2016", month = mar # "~28", volume = "6", number = "2", pages = "103--117", keywords = "genetic algorithms, genetic programming, MIKE11-NAM, hydroinformatics, climate scenarios, forecasting, hydrology, rainfall prediction, inflows, inflow prediction, catchment runoff, dam catchment, water management, water resources, Australia, flow simulation", publisher = "Inderscience Publishers", ISSN = "2042-7816", bibsource = "OAI-PMH server at www.inderscience.com", language = "eng", rights = "Inderscience Copyright", URL = "http://www.inderscience.com/link.php?id=75560", DOI = "doi:10.1504/IJHST.2016.075560", abstract = "Application of hydroinformatics tools for managing water resources is common in the water industry. Over the last few decades, several hydroinformatics tools including genetic programming (GP) have been developed and applied in hydrology. GP has been successfully applied for calibration of numerous event-based rainfall and runoff models. However, applying GP to predict long-term time series for the management of water resources is limited. This study demonstrates GP's application in long-term prediction of catchment runoff concerning a dam located in Oberon, New South Wales, Australia. The calibration showed excellent agreement between the observed and simulated flows recorded over 30 years. The model was then applied for the assessment of catchment yields for a future 100 years flows based on two assumed climatic scenarios.", } @Article{ATIQUZZAMAN:2018:CG, author = "Md Atiquzzaman and Jaya Kandasamy", title = "Robustness of Extreme Learning Machine in the prediction of hydrological flow series", journal = "Computer \& Geosciences", volume = "120", pages = "105--114", year = "2018", keywords = "genetic algorithms, genetic programming, Catchment, Flow series, Prediction, Hydrology, Modeling, Extreme learning machine", URL = "http://www.sciencedirect.com/science/article/pii/S0098300417304867", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2018.08.003", URL = "http://www.sciencedirect.com/science/article/pii/S0098300417304867", abstract = "Prediction of hydrological flow series generated from a catchment is an important aspect of water resources management and decision making. The underlying process underpinning catchment flow generation is complex and depends on many parameters. Determination of these parameters using a trial and error method or optimization algorithm is time consuming. Application of Artificial Intelligence (AI) based machine learning techniques including Artificial Neural Network, Genetic Programming (GP) and Support Vector Machine (SVM) replaced the complex modeling process and at the same time improved the prediction accuracy of hydrological time-series. However, they still require numerous iterations and computational time to generate optimum solutions. This study applies the Extreme Learning Machine (ELM) to hydrological flow series modeling and compares its performance with GP and Evolutionary Computation based SVM (EC-SVM). The robustness and performance of ELM were studied using the data from two different catchments located in two different climatic conditions. The robustness of ELM was evaluated by varying number of lagged input variables the number of hidden nodes and input parameter (regularization coefficient). Higher lead days prediction and extrapolation capability were also investigated. The results show that (1) ELM yields reasonable results with two or higher lagged input variables (flows) for 1-day lead prediction; (2) ELM produced satisfactory results very rapidly when the number of hidden nodes was greater than or equal to 1000; (3) ELM showed improved results when regularization coefficient was fine-tuned; (4) ELM was able to extrapolate extreme values well; (5) ELM generated reasonable results for higher number of lead days (second and third) predictions; (6) ELM was computationally much faster and capable of producing better results compared to other leading AI methods for prediction of flow series from the same catchment. ELM has the potential for forecasting real-time hydrological flow series", } @InProceedings{Atkin:1993:GPLAMS, author = "M. Atkin and P. R. Cohen", title = "Genetic programming to learn an agent's monitoring strategy", booktitle = "Proceedings of the AAAI-93 Workshop on Learning Action Models", year = "1993", editor = "Wei-Min Shen", pages = "36--41", publisher = "AAAI Press", URL = "http://www.aaai.org/Papers/Workshops/1993/WS-93-06/WS93-06-009.pdf", URL = "http://www.aaai.org/Library/Workshops/ws93-06.php", keywords = "genetic algorithms, genetic programming", size = "6 pages", abstract = "Many tasks require an agent to monitor its environment, but little is known about appropriate monitoring strategies to use in particular situations. Our approach is to learn good monitoring strategies with a genetic programming algorithm. To this end, we have developed a simple agent programming language in which we represent monitoring strategies as programs that control a simulated robot, and a simulator in which the programs can be evaluated. The effect of different environments and tasks is determined experimentally; changing features of the environment will change which strategies are learnt. The correspondence can then be analysed.", notes = "Also available as \cite{Atkin:1993:GPLAMSa}?", } @TechReport{Atkin:1993:GPLAMSa, author = "M. Atkin and P. R. Cohen", title = "Genetic programming to learn an agent's monitoring strategy", institution = "Computer Science Department, University of Massachusetts", year = "1993", type = "Technical report", number = "TR-93-26", address = "Amherst, MA, USA", URL = "http://www-eksl.cs.umass.edu/papers/93-26.ps", keywords = "genetic algorithms, genetic programming", notes = "Also available as \cite{Atkin:1993:GPLAMS}?", size = "15 pages", } @InProceedings{Atkin:1994:LMSDGP, author = "Marc S. Atkin and Paul R. Cohen", title = "Learning monitoring strategies: A difficult genetic programming application", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "328--332a", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, cupcake problem, agent control language, genetic programming application, monitoring strategy learning, optimal strategies, possible behaviour, learning (artificial intelligence), monitoring; optimisation", URL = "http://www-eksl.cs.umass.edu/papers/AtkinIEEE.pdf", URL = "http://citeseer.ist.psu.edu/94049.html", DOI = "doi:10.1109/ICEC.1994.349931", size = "6 pages", abstract = "Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviours. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.", notes = "Novel? chrome/program structure linear, close to assembly language, used GOTOs and interrupt handlers. Did _not_ get performance improvement on changing to parse trees. Did evolve progs to control agents which moved to the goal without colliding with an obstacle. Finally cautions about problems with GP scaling up. {"}Also tried local mating (also known as fine grain parallelism){"} Also available as Technical Report 94-52, Dept. of Computer Science, University of Massachusetts/Amherst, USA?", } @TechReport{atkin:1995:mea, author = "Marc S. Atkin and Paul R. Cohen", title = "Monitoring in Embedded Agents", institution = "Experimental Knowledge Systems Laboratory, Computer Science Department, University of Massachusetts", year = "1995", type = "Computer Science Technical Report", number = "95-66", address = "Box 34610, Lederle Graduate Research Center, Amherst. MA 01003-4610, USA", keywords = "genetic algorithms, genetic programming", URL = "http://www-eksl.cs.umass.edu/papers/ijcai95-msa_95-66.pdf", abstract = "Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it difficult, and show that while periodic monitoring strategies are often the easiest to derive, they are not always the most appropriate. We demonstrate mathematically and empirically that for a wide class of problems, the so-called 'cupcake problems', there exists a simple strategy, interval reduction, that outperforms periodic monitoring. We also show how features of the environment may influence the choice of the optimal strategy. The paper concludes with some thoughts about a monitoring strategy taxonomy, and what its defining features might be.", notes = "refs to Atkin's Masters Thesis. Simulated robot in 2 dee world, sensors, conditionals, loop. LTB, explains what the cupcake problem is. interrupt handlers. Theoretical justification for cupcake result.", size = "11 pages", } @Article{atkin:1995:AB, author = "Marc S. Atkin and Paul R. Cohen", title = "Monitoring Strategies for Embedded Agents: Experiments and Analysis", journal = "Adaptive Behavior", year = "1995", volume = "4", number = "2", pages = "125--172", month = "Fall", keywords = "genetic algorithms, genetic programming, Monitoring, embedded agents, planning", URL = "http://www-eksl.cs.umass.edu/papers/atkin96.pdf", abstract = "Monitoring is an important activity for any embedded agent. To operate effectively, agents must gather information about their environment. The policy by which they do this is called a monitoring strategy. Our work has focused on classifying different types of monitoring strategies and understanding how strategies depend on features of the task and environment. We have discovered only a few general monitoring strategies, in particular periodic and interval reduction, and speculate that there are no more. The relative advantages and generality of each strategy will be discussed in detail. The wide applicability of interval reduction will be demonstrated both empirically and analytically. We conclude with a number of general laws that state when a strategy is most appropriate.", } @InProceedings{Atkins:2010:cec, author = "Daniel L Atkins and Roman Klapaukh and Will N Browne and Mengjie Zhang", title = "Evolution of aesthetically pleasing images without human-in-the-loop", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Evolutionary Art is a sub-field of Evolutionary Computing that involves creating interesting images using Evolutionary Techniques. Previously Genetic Programming has been used to create such images autonomously -that is, without a human in the loop. However, this work did not explore alternative fitness measures, consider colour in fitness or provide independent validation of results. Four fitness functions based on the concept that the pleasingness of an image is based on the ratio of image complexity to processing complexity are explored. We introduce the use of Shannon Entropy as a measure of image complexity to compare with Jpeg Compression. Similarly, we introduce Run Length Encoding to compare with Fractal Compression as a measure of processing complexity. A survey of 100 participants showed that it is possible to generate aesthetically pleasing graphics using each fitness function. Importantly, it was the introduction of colour that separated the aesthetic effects of the fitness measures.", DOI = "doi:10.1109/CEC.2010.5586283", notes = "Three 'separate' GP programs: one for each colour, per image. RMIT-GP package. Web based trial with Likert scale (1..5). Nice pictures. WCCI 2010. Also known as \cite{5586283}", } @InProceedings{Atkins:2011:ADIGPAtAFEfIC, title = "A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification", author = "Daniel Atkins and Kourosh Neshatian and Mengjie Zhang", pages = "238--245", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, automatic image feature extraction, baseline system, classifier system, domain independent genetic programming, human-extracted features, image classification, feature extraction, image classification", DOI = "doi:10.1109/CEC.2011.5949624", abstract = "In this paper we explore the application of Genetic Programming (GP) to the problem of domain-independent image feature extraction and classification. We propose a new GP-based image classification system that extracts image features autonomously, and compare its performance against a baseline GP-based classifier system that uses human-extracted features. We found that the proposed system has a similar performance to the baseline system, and that GP is capable of evolving a single program that can both extract useful features and use those features to classify an image.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @TechReport{Atkins:1998:space, author = "Diane J. Atkins", title = "Applying Space Technology to Enhance Control of an Artificial Arm for Children and Adults with Amputations", institution = "The Institute for Rehabilitation and Research (TIRR)", year = "1998", number = "IN-63 006665?", address = "USA", month = "30 " # jun, keywords = "genetic algorithms, genetic programming, myoelectric, MRI", URL = "http://hdl.handle.net/2060/19990025668", URL = "http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19990025668.pdf", size = "4 pages", notes = "Texas medical Center, NASA Johnson Space Center. TIRR Amputee Program. Dr. Kirstin Ann Farry. Intelligenta Inc. NIH. SBIR. Dept of Education. Appears to have been written in 1999.", } @InProceedings{Atkinson:2018:EuroGP, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Evolving Graphs by Graph Programming", booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming", year = "2018", month = "4-6 " # apr, editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez", series = "LNCS", volume = "10781", publisher = "Springer Verlag", address = "Parma, Italy", pages = "35--51", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", isbn13 = "978-3-319-77552-4", URL = "http://eprints.whiterose.ac.uk/126500/1/AtkinsonPlumpStepney.EuroGP.18.pdf", DOI = "doi:10.1007/978-3-319-77553-1_3", abstract = "Rule-based graph programming is a deep and rich topic. We present an approach to exploiting the power of graph programming as a representation and as an execution medium in an evolutionary algorithm (EGGP). We demonstrate this power in comparison with Cartesian Genetic Programming (CGP), showing that it is significantly more efficient in terms of fitness evaluations on some classic benchmark problems. We hypothesise that this is due to its ability to exploit the full graph structure, leading to a richer mutation set, and outline future work to test this hypothesis, and to exploit further the power of graph programming within an EA.", notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018", } @Misc{DBLP:journals/corr/abs-1810-10453, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Semantic Neutral Drift", howpublished = "arXiv", year = "2018", month = "24 " # oct, keywords = "genetic algorithms, genetic programming Evolutionary Algorithms, Neutral Drift, Semantic Equivalence, Mutation Operators, Graph Programming", eprint = "1810.10453", timestamp = "Wed, 31 Oct 2018 14:24:29 +0100", biburl = "https://dblp.org/rec/bib/journals/corr/abs-1810-10453", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://arxiv.org/abs/1810.10453", size = "16 pages", abstract = "We introduce the concept of Semantic Neutral Drift (SND) for evolutionary algorithms, where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for an evolutionary algorithm if that facilitates the inclusion of SND.", notes = "P-GP 2, EGGP, DeMorgan, Bit Added, Bit-Comparator, Bit-Multiplier", } @InProceedings{Atkinson:2019:EuroGP, author = "Timothy Atkinson and John Drake and Athena Karsa and Jerry Swan", title = "Quantum Program Synthesis: Swarm Algorithms and Benchmarks", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "19--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-16669-4", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_2", size = "16 pages", abstract = "In the two decades since Shor celebrated quantum algorithm for integer factorisation, manual design has failed to produce the anticipated growth in the number of quantum algorithms. Hence, there is a great deal of interest in the automatic synthesis of quantum circuits and algorithms. Here we present a set of experiments which use Ant Programming to automatically synthesise quantum circuits. In the proposed approach, ants choosing paths in high-dimensional Cartesian space are analogous to transformation of qubits in Hilbert space. In addition to the proposed algorithm, we introduce new evaluation criteria for searching the space of quantum circuits, both for classical simulation and simulation on a quantum computer. We demonstrate that the proposed approach significantly outperforms random search on a suite of benchmark problems based on these new measures.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{Atkinson:2019:GECCO, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Evolving graphs with horizontal gene transfer", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "968--976", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321788", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Evolving Graphs, Horizontal Gene Transfer, Neutrality", size = "9 pages", abstract = "We introduce a form of neutral Horizontal Gene Transfer (HGT) to Evolving Graphs by Graph Programming (EGGP). We introduce the mu x lambda evolutionary algorithm, where u parents each produce l children who compete with only their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from 14 symbolic regression benchmark problems show that the introduction of the u x l EA and HGT events improve the performance of EGGP. Comparisons with Genetic Programming and Cartesian Genetic Programming strongly favour our proposed approach.", notes = "See also \cite{Atkinson:GPEM:H2019} Also known as \cite{3321788} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Atkinson:GPEM:H2019, author = "Timothy Atkinson and Detlef Plump and Susan Stepney", title = "Horizontal gene transfer for recombining graphs", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "3", pages = "321--347", month = sep, note = "Special Issue: Highlights of Genetic Programming 2019 Events", keywords = "genetic algorithms, genetic programming, Graph-based genetic programming, Neuroevolution, Horizontal gene transfer, HGT, EGGP", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-020-09378-1", size = "27 pages", abstract = "We introduce a form of neutral horizontal gene transfer (HGT) to evolving graphs by graph programming (EGGP). We introduce the mu x lambda evolutionary algorithm (EA), where mu parents each produce lambda children who compete only with their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from symbolic regression problems show that the introduction of the mu x lambda EA and HGT events improve the performance of EGGP. Comparisons with genetic programming and Cartesian genetic programming strongly favour our proposed approach. We also investigate the effect of using HGT events in neuroevolution tasks. We again find that the introduction of HGT improves the performance of EGGP, demonstrating that HGT is an effective cross-domain mechanism for recombining graphs.", } @PhdThesis{Atkinson:thesis, author = "Timothy Atkinson", title = "Evolving Graphs by Graph Programming", school = "University of York", year = "2019", address = "UK", keywords = "genetic algorithms, genetic programming, EGGP", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.803685", URL = "http://etheses.whiterose.ac.uk/26524/", URL = "http://etheses.whiterose.ac.uk/26524/1/thesis_whiterose.pdf", size = "276 pages", abstract = "Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search.", notes = "date also given as March 2020. Supervisors: Susan Stepney and Detlef Plump", } @InProceedings{atkinson-abutridy:1999:A, author = "John A. Atkinson-Abutridy and Julio R. Carrasco-Leon", title = "An evolutionary model for dynamically controlling a behavior-based autonomous agent", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "16--24", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @InProceedings{atlan:1994:gpjss, author = "Laurent Atlan and Jerome Bonnet and Martine Naillon", title = "Learning Distributed Reactive Strategies by Genetic Programming for the General Job Shop Problem", booktitle = "Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium", year = "1994", address = "Pensacola, Florida, USA", month = may, organisation = "Dassault-Aviation, Artificial Intelligence Department", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.ens.fr/pub/reports/biologie/disgajsp.ps.Z", size = "11 pages", abstract = "proposed is a general system to infer symbolic policy functions for distributed reactive scheduling in non-stationary environments. The job shop problem is only used as a validating case study. Our system is based both on an original distributed scheduling model and on genetic programming for the inference of symbolic policy functions. The purpose is to determine heuristic policies that are local in time, long term near-optimal, and robust with respect to perturbations. Furthermore, the policies are local in state space: the global decision problem is split into as many decision problems as there are agents, i.e. machines in the job shop problem. If desired, the genetic algorithm can use expert knowledge as a priori knowledge, via implementation of the symbolic representation of the policy functions.", notes = "'To be published in the proceedings of the Seventh Annual Florida Artificial Intelligence Research Symposium' DGT/DEA/IA2 December 1993 Combination of GP and Giffler and Thompson algorithm Dassault-Aviation DGT/DEA/IA2 Artificial Intelligence Department 78, Quai Marcel Dassault, 92214 Saint-Cloud France", } @InProceedings{Atmosukarto:2010:UGP:1904935.1906046, author = "Indriyati Atmosukarto and Linda G. Shapiro and Carrie Heike", title = "The Use of Genetic Programming for Learning {3D} Craniofacial Shape Quantifications", booktitle = "Proceedings of the 2010 20th International Conference on Pattern Recognition", year = "2010", editor = "Aytul Ercil", pages = "2444--2447", address = "Istanbul, Turkey", month = "23-26 " # aug, organisation = "International Association for Pattern Recognition (IAPR)", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, 3D Shape quantification", isbn13 = "978-0-7695-4109-9", URL = "http://www.cs.washington.edu/research/VACE/Multimedia/icpr10_Atmosukarto.pdf", URL = "http://grail.cs.washington.edu/pub/papers/atmosukarto2010uog.pdf", DOI = "doi:10.1109/ICPR.2010.598", acmid = "1906046", size = "4 pages", abstract = "Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2].", notes = "ICPR '10", } @PhdThesis{AtmosukartoPhd, author = "Indriyati Atmosukarto", title = "{3D} Shape Analysis for Quantification, Classification, and Retrieval", school = "Computer Science and Engineering, University of Washington", year = "2010", address = "USA", keywords = "genetic algorithms, genetic programming", URL = "http://grail.cs.washington.edu/theses/AtmosukartoPhd.pdf", size = "139 pages", abstract = "Three-dimensional objects are now commonly used in a large number of applications including games, mechanical engineering, archaeology, culture, and even medicine. As a result, researchers have started to investigate the use of 3D shape descriptors that aim to encapsulate the important shape properties of the 3D objects. This thesis presents new 3D shape representation methodologies for quantification, classification and retrieval tasks that are flexible enough to be used in general applications, yet detailed enough to be useful in medical craniofacial dysmorphology studies. The methodologies begin by computing low-level features at each point of the 3D mesh and aggregating the features into histograms over mesh neighbourhoods. Two different methodologies are defined. The first methodology begins by learning the characteristics of salient point histograms for each particular application, and represents the points in a 2D spatial map based on longitude-latitude transformation. The second methodology represents the 3D objects by using the global 2D histogram of the azimuth-elevation angles of the surface normals of the points on the 3D objects. Four datasets, two craniofacial datasets and two general 3D object datasets, were obtained to develop and test the different shape analysis methods developed in this thesis. Each dataset has different shape characteristics that help explore the different properties of the methodologies. Experimental results on classifying the craniofacial datasets show that our methodologies achieve higher classification accuracy than medical experts and existing state-of-the-art 3D descriptors. Retrieval and classification results using the general 3D objects show that our methodologies are comparable to existing view-based and feature-based descriptors and outperform these descriptors in some cases. Our methodology can also be used to speed up the most powerful general 3D object descriptor to date.", notes = "GPLAB, Matlab", } @Article{Atmosukarto:2011:GPEM, author = "Indriyati Atmosukarto", title = "{GPLAB}: software review", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "12", number = "4", pages = "457--459", month = dec, note = "Software Review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8d6", DOI = "doi:10.1007/s10710-011-9142-5", size = "3 pages", notes = "Matlab", } @Article{ATTIAS:2023:cej, author = "Rinat Attias and Sourav Bhowmick and Yoed Tsur", title = "Distribution function of relaxation times: An alternative to classical methods for evaluating the reaction kinetics of oxygen evolution reaction", journal = "Chemical Engineering Journal", volume = "476", pages = "146708", year = "2023", ISSN = "1385-8947", DOI = "doi:10.1016/j.cej.2023.146708", URL = "https://www.sciencedirect.com/science/article/pii/S1385894723054396", keywords = "genetic algorithms, genetic programming, Tafel slope, DFRT, DRT, ISGP, EIS, Reaction kinetics", abstract = "Reaction kinetics of RuO2 is precisely evaluated by distribution function of relaxation times (DFRT) model using impedance spectroscopy analysis by genetic programming (ISGP). Effective resistances of the Faradaic processes, measured using Electrochemical impedance spectroscopy (EIS) at various overpotentials, were determined using DFRT, by separating and associating three electrochemical phenomena occurring during the reaction. The effective resistances are used to generate a Tafel plot of potential as a function of log1Reff. The classical method, based on linear sweep voltammetry (LSV), to evaluate the Tafel slope is associated with some considerations for accurate results. For RuO2, a reference catalyst, LSV illustrates an average Tafel slope of 182 pm 8 mV/dec while the effective resistance method, estimated using DFRT, shows an average of 181 pm 3 mV/dec. A relative error of 0.3 percent between the two methodologies, and a lower standard deviation for DFRT demonstrate the higher precision and effectiveness of ISGP in determining the reaction kinetics via Tafel slope analysis. Therefore, using DFRT with the ability to separate Faradaic from non-Faradaic processes to evaluate the relevant part of the effective resistance, reaction kinetics can be estimated, avoiding shortcomings of the classical Tafel method", } @InProceedings{Atwater:2012:GECCO, author = "Aaron Atwater and Malcolm I. Heywood and Nur Zincir-Heywood", title = "GP under streaming data constraints: a case for pareto archiving?", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "703--710", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330262", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Classification as applied to streaming data implies that only a small number of new training instances appear at each generation and are never explicitly reintroduced by the stream. Pareto competitive coevolution provides a potential framework for archiving useful training instances between generations under an archive of finite size. Such a coevolutionary framework is defined for the online evolution of classifiers under genetic programming. Benchmarking is performed under multi-class data sets with class imbalance and training partitions with between 1,000's to 100,000's of instances. The impact of enforcing different constraints for accessing the stream are investigated. The role of online adaptation is explicitly documented and tests made on the relative impact of label error on the quality of streaming classifier results.", notes = "Also known as \cite{2330262} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Atwater:2013:GECCO, author = "Aaron Atwater and Malcolm I. Heywood", title = "Benchmarking {Pareto} archiving heuristics in the presence of concept drift: diversity versus age", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "885--892", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463489", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A framework for coevolving genetic programming teams with Pareto archiving is benchmarked under two representative tasks for non-stationary streaming environments. The specific interest lies in determining the relative contribution of diversity and aging heuristics to the maintenance of the Pareto archive. Pareto archiving, in turn, is responsible for targeting data (and therefore champion individuals) as appropriate for retention beyond the limiting scope of the sliding window interface to the data stream. Fitness sharing alone is considered most effective under a non-stationary stream characterised by continuous (incremental) changes. Fitness sharing with an aging heuristic acts as the preferred heuristic when the stream is characterised by non-stationary stepwise changes.", notes = "Also known as \cite{2463489} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{Auerbach:2014:ALIFE, author = "Joshua E. Auerbach and Deniz Aydin and Andrea Maesani and Przemyslaw M. Kornatowski and Titus Cieslewski and Gregoire Heitz and Pradeep R. Fernando and Ilya Loshchilov and Ludovic Daler and Dario Floreano", title = "{RoboGen}: Robot Generation through Artificial Evolution", booktitle = "Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14", year = "2014", editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson", series = "Complex Adaptive Systems", pages = "136--137", address = "New York", month = "30 " # jul # "-2 " # aug, organisation = "International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, RoboGen", isbn13 = "9780262326216 ?", URL = "http://mitpress.mit.edu/sites/default/files/titles/content/alife14/ch022.html", DOI = "doi:10.7551/978-0-262-32621-6-ch022", size = "2 pages", abstract = "Science instructors from a wide range of disciplines agree that hands-on laboratory components of courses are pedagogically necessary (Freedman, 1997). However, certain shortcomings of current laboratory exercises have been pointed out by several authors (Mataric, 2004; Hofstein and Lunetta, 2004). The overarching theme of these analyses is that hands-on components of courses tend to be formulaic, closed-ended, and at times outdated. To address these issues, we envision a novel platform that is not only a didactic tool but is also an experimental testbed for users to play with different ideas in evolutionary robotics (Nolfi and Floreano, 2000), neural networks, physical simulation, 3D printing, mechanical assembly, and embedded processing. Here, we introduce RoboGen an open-source software and hardware platform designed for the joint evolution of robot morphologies and controllers a la Sims (1994); Lipson and Pollack (2000); Bongard and Pfeifer (2003). Robo-Gen has been designed specifically to allow evolved robots to be easily manufactured via widely available desktop 3D-printers, and the use of simple, open-source, low-cost, off-the-shelf electronic components. RoboGen features an evolution engine complete with a physics simulator, as well as utilities both for generating design files of body components for 3D printing, and for compiling neural-network controllers to run on an Arduino microcontroller board. In this paper, we describe the RoboGen platform, and provide some metrics to assess the success of using it as the hands-on component of a masters-level bio-inspired artificial intelligence course.", notes = "Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne. ALIFE 14 http://mitpress.mit.edu/books/artificial-life-14 ALIFE14NYC@gmail.com", } @InProceedings{sbrn2000meta029, author = "Douglas A. Augusto and Helio J. C. Barbosa", title = "Symbolic Regression via Genetic Programming", booktitle = "{VI} Brazilian Symposium on Neural Networks (SBRN'00)", year = "2000", pages = "173", address = "Rio de Janeiro, RJ, Brazil", month = jan # " 22-25", publisher = "IEEE", note = "VI Simposio Brasileiro de Redes Neurais", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-0856-1", identifier = "sbrn2000article029", language = "eng", source = "sbrn2000", broken = "http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560173abs.htm", DOI = "doi:10.1109/SBRN.2000.889734", abstract = "In this work, we present an implementation of symbolic regression, which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments, which are summarized in this paper.", } @InProceedings{Augusto:2008:gecco, author = "Douglas A. Augusto and Helio J. C. Barbosa and Nelson F. F. Ebecken", title = "Coevolution of data samples and classifiers integrated with grammatically-based genetic programming for data classification", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1171--1178", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, competitive coevolution, context-free grammar, data classification", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1171.pdf", DOI = "doi:10.1145/1389095.1389328", size = "8 pages", abstract = "The present work treats the data classification task by means of evolutionary computation techniques using three ingredients: genetic programming, competitive coevolution, and context-free grammar. The robustness and symbolic/interpretative qualities of the genetic programming are employed to construct classification trees via Darwinian evolution. The flexible formal structure of the context-free grammar replaces the standard genetic programming representation and describes a language which encodes trees of varying complexity. Finally, competitive coevolution is used to promote competitions between data samples and classification trees in order to create and sustain an evolutionary arms-race for improved solutions", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389328}", } @InProceedings{Augusto:2010:gecco, author = "Douglas Adriano Augusto and Helio Jose Correa Barbosa and Nelson Francisco Favilla Ebecken", title = "Coevolutionary multi-population genetic programming for data classification", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "933--940", keywords = "genetic algorithms, genetic programming, distributed genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830650", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This work presents a new evolutionary ensemble method for data classification, which is inspired by the concepts of bagging and boosting, and aims at combining their good features while avoiding their weaknesses. The approach is based on a distributed multiple-population genetic programming (GP) algorithm which exploits the technique of coevolution at two levels. On the inter-population level the populations cooperate in a semi-isolated fashion, whereas on the intrapopulation level the candidate classifiers coevolve competitively with the training data samples. The final classifier is a voting committee composed by the best members of all the populations. The experiments performed in a varying number of populations show that our approach outperforms both bagging and boosting for a number of benchmark problems.", notes = "Also known as \cite{1830650} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Augusto:2011:GECCOcomp, author = "Douglas A. Augusto and Helio J. C. Barbosa and Andre M. S. Barreto and Heder S. Bernardino", title = "A new approach for generating numerical constants in grammatical evolution", booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", pages = "193--194", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution: Poster", isbn13 = "978-1-4503-0690-4", DOI = "doi:10.1145/2001858.2001966", abstract = "A new approach for numerical-constant generation in Grammatical Evolution is presented. Experiments comparing our method with the three most popular methods for constant creation are performed. By varying the number of bits to represent a constant, we can increase our methods precision to the desired level of accuracy, overcoming by a large margin the other approaches.", notes = "Also known as \cite{2001966} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Augusto:2011:EPIA, author = "Douglas Adriano Augusto and Helio J. C. Barbosa and Andre {da Motta Salles Barreto} and Heder S. Bernardino", title = "Evolving Numerical Constants in Grammatical Evolution with the Ephemeral Constant Method", booktitle = "Proceedings 15th Portuguese Conference on Artificial Intelligence, {EPIA 2011}", year = "2011", editor = "Luis Antunes and Helena Sofia Pinto", volume = "7026", series = "Lecture Notes in Computer Science", pages = "110--124", address = "Lisbon, Portugal", month = oct # " 10-13", keywords = "genetic algorithms, genetic programming, grammatical evolution, constant creation", isbn13 = "978-3-642-24768-2", DOI = "doi:10.1007/978-3-642-24769-9_9", affiliation = "Laboratorio Nacional de Computacao Cientifica, Petropolis, RJ, Brazil", } @Article{Augusto2012, author = "Douglas A. Augusto and Helio J. C. Barbosa", title = "Accelerated parallel genetic programming tree evaluation with {OpenCL}", journal = "Journal of Parallel and Distributed Computing", volume = "73", number = "1", pages = "86--100", year = "2013", note = "Metaheuristics on GPUs", ISSN = "0743-7315", DOI = "doi:10.1016/j.jpdc.2012.01.012", URL = "http://www.sciencedirect.com/science/article/pii/S074373151200024X", keywords = "genetic algorithms, genetic programming, GPU, OpenCL, GP-GPU, Accelerated tree evaluation", abstract = "Inspired by the process of natural selection, genetic programming (GP) aims at automatically building arbitrarily complex computer programs. Being classified as an embarrassingly parallel technique, GP can theoretically scale up to tackle very diverse problems by increasingly adding computational power to its arsenal. With today's availability of many powerful parallel architectures, a challenge is to take advantage of all those heterogeneous compute devices in a portable and uniform way. This work proposes both (i) a transcription of existing GP parallelisation strategies into the OpenCL programming platform; and (ii) a freely available implementation to evaluate its suitability for GP, by assessing the performance of parallel strategies on the CPU and GPU processors from different vendors. Benchmarks on the symbolic regression and data classification domains were performed. On the GPU we could achieve 13 billion node evaluations per second, delivering almost 10 times the throughput of a twelve-core CPU.", notes = "Genetic Programming in OpenCL Source", } @InCollection{Augusto:2012:GPnew, author = "Douglas A. Augusto and Heder S. Bernardino and Helio J. C. Barbosa", title = "Parallel Genetic Programming on Graphics Processing Units", booktitle = "Genetic Programming - New Approaches and Successful Applications", publisher = "InTech", year = "2012", editor = "Sebastian Ventura", chapter = "5", pages = "95--114", keywords = "genetic algorithms, genetic programming, GPU, OpenCL, stack-based interpreter", isbn13 = "978-953-51-0809-2", DOI = "doi:10.5772/48364", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.374.7459", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.745", size = "20 pages", notes = "data level parallelism, SIMD, no performance figures. Part of \cite{Ventura:2012:GPnew}. Open access book CC BY 3.0 license", } @InProceedings{Augusto:2013:GECCOcomp, author = "Douglas A. Augusto and Heder S. Bernardino and Helio J. C. Barbosa", title = "Improving recruitment effectiveness using genetic programming techniques", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "177--178", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464673", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A real-world problem, namely to improve the recruitment effectiveness of a certain company, is tackled here by evolving accurate and human-readable classifiers by means of grammar-based genetic programming techniques.", notes = "Also known as \cite{2464673} Distributed at GECCO-2013.", } @InProceedings{Augusto:2013:CCI.CBIC, author = "D. A. Augusto and H. S. Bernardino and H. J. C. Barbosa", booktitle = "BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC 2013)", title = "Predicting the Performance of Job Applicants by Means of Genetic Programming", year = "2013", month = sep, pages = "98--103", abstract = "Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques, a real-world problem of meritocracy in jobs by evolving classifiers that were both accurate and human-readable.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BRICS-CCI-CBIC.2013.27", notes = "Also known as \cite{6855836}", } @InCollection{Augustoetal2013, author = "Douglas Adriano Augusto and Heder Soares Bernardino and Helio Jose Correa Barbosa", title = "Programa\c{c}\~{a}o Gen\'{e}tica", booktitle = "Meta-Heursticas em Pesquisa Operacional", publisher = "Omnipax", year = "2013", editor = "Heitor Silvrio Lopes and Luiz Carlos de Abreu Rodrigues and Maria Teresinha Arns Steiner", chapter = "5", pages = "69--86", address = "Curitiba, PR", edition = "1", keywords = "genetic algorithms, genetic programming, operations research, Optimization", isbn13 = "978-85-64619-10-4", URL = "http://omnipax.com.br/site/?page_id=387", DOI = "doi:10.7436/2013.mhpo.05", size = "18 pages", abstract = "Genetic programming is an evolutionary metaheuristic designed to automatically generate programs by means of an iterative process inspired by the theory of natural selection. In operational research, genetic programming techniques are normally used to infer heuristics for decision-making problems. In this way, genetic programming is a hyper-heuristic creating new search methods which are more efficient that those traditionally considered. This chapter describes genetic programming and presents its applications in the operations research field.", abstract = "A programacao genetica e uma meta-heuristica evolucionaria destinada a geracao automatica de programas atraves de um processo iterativo inspirado pela teoria da selecao natural. Em pesquisa operacional, tecnicas de programacao genetica sao normalmente usadas para inferir heuristicas para problemas de tomada de decisao. Desta forma, a programacao genetica assume o papel de hiper-heuristica criando novos metodos de busca mais eficientes que os tradicionalmente considerados. O presente capitulo descreve a programacao genetica e apresenta suas aplicacoes no campo da pesquisa operacional.", notes = "In Portuguese", } @InProceedings{Augustsson:2002:gecco, author = "Peter Augustsson and Krister Wolff and Peter Nordin", title = "Creation Of {A} Learning, Flying Robot By Means Of Evolution", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1279--1285", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, evolutionary robotics, evolutionary algorithm, flying", ISBN = "1-55860-878-8", URL = "http://fy.chalmers.se/~wolff/Papers/ANW_gecco02.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-22.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/ROB196.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/ROB196.pdf", size = "7 pages", abstract = "We demonstrate the first instance of a real on-line robot learning to develop feasible flying (flapping) behavior, using evolution. Here we present the experiments and results of the first use of evolutionary methods for a flying robot. With nature's own method, evolution, we address the highly non-linear fluid dynamics of flying. The flying robot is constrained in a test bench where timing and movement of wing flapping is evolved to give maximal lifting force. The robot is assembled with standard off-the-shelf R/C servomotors as actuators. The implementation is a conventional steady-state linear evolutionary algorithm.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Winner of the best-paper award at GECCO-2002, in Evolutionary Robotics.", } @InProceedings{aurnhammer:evows07, author = "Melanie Aurnhammer", title = "Evolving Texture Features by Genetic Programming", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}, {EvoTransLog}", year = "2007", month = "11-13 " # apr, editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang", series = "LNCS", volume = "4448", publisher = "Springer Verlag", address = "Valencia, Spain", pages = "351--358", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-71804-8", DOI = "doi:10.1007/978-3-540-71805-5_38", abstract = "Feature extraction is a crucial step for Computer Vision applications. Finding appropriate features for an application often means hand-crafting task specific features with many parameters to tune. A generalisation to other applications or scenarios is in many cases not possible. Instead of engineering features, we describe an approach which uses Genetic Programming to generate features automatically. In addition, we do not predefine the dimension of the feature vector but pursue an iterative approach to generate an appropriate number of features. We present this approach on the problem of texture classification based on co-occurrence matrices. Our results are compared to those obtained by using seven Haralick texture features, as well as results reported in the literature on the same database. Our approach yielded a classification performance of up to 87percent which is an improvement of 30percent over the Haralick features. We achieved an improvement of 12percent over previously reported results while reducing the dimension of the feature vector from 78 to four.", notes = "EvoWorkshops2007", } @TechReport{austin:2003:WP, author = "M. P. Austin and R. G. Bates and M. A. H. Dempster and S. N. Williams", title = "Adaptive systems for foreign exchange trading", institution = "Judge Institute of Management, University of Cambridge", year = "2003", type = "Working paper", number = "WP 15/2003", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/WP1503.pdf", notes = "Research Papers in Management Studies. in Eclectic \cite{Austin:2004:E} See \cite{Austin:2004:QF} ", size = "12 pages", } @Article{Austin:2004:E, author = "Mark Austin and Graham Bates and Michael Dempster and Stacy Williams", title = "Adaptive systems for foreign exchange trading", journal = "Eclectic", year = "2004", volume = "18", pages = "21--26", month = "Autumn", keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/adaptive.pdf", size = "6 pages", abstract = "A joint project between academics and bankers has shown how banks can improve the forecasting performance of their technical trading systems in foreign exchange markets. Professor Michael Dempster and Graham Bates, both of the Centre for Financial Research, Cambridge, and Dr Mark Austin and Dr Stacy Williams, both of HSBC Global Markets, outline the results of their research. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest it can be done. Automated trading systems are being used successfully to predict intraday and daily exchange rates. Trading systems using only publicly available technical indicators can be profitable ? but those that also use proprietary information can be more accurate and therefore more profitable. A joint project by the Centre for Financial Research (at the Judge Institute of Management, Cambridge University) and HSBC used the bank's customer order information to show that using proprietary information in trading systems can improve their forecasting performance and profitability. The research findings also intuitively make sense. Successful traders in the FX markets apply human judgement to a range of information and techniques. In this project the researchers mimicked these traders by combining the techniques of technical analysis with the stream of public and non-public information available to them.", } @Article{Austin:2004:QF, title = "Adaptive systems for foreign exchange trading", author = "Mark P. Austin and Graham Bates and Michael A. H. Dempster and Vasco Leemans and Stacy N. Williams", journal = "Quantitative Finance", month = aug, number = "4", pages = "37--45", publisher = "Routledge, part of the Taylor and Francis Group", volume = "4", year = "2004", keywords = "genetic algorithms, genetic programming, fx trading", citeulike-article-id = "98141", ISSN = "1469-7688", URL = "http://www-cfr.jbs.cam.ac.uk/archive/PRESENTATIONS/seminars/2006/dempster2.pdf", DOI = "doi:10.1080/14697680400008593", size = "9 pages", abstract = "Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University's Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank's proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.", notes = "Also in Eclectic 18 Autumn (2004) pp21-26 \cite{Austin:2004:E} www.eclectic.co.uk and technical report WP15/2003 \cite{austin:2003:WP} ", } @InProceedings{autones:2004:eurogp, author = "Mathieu Autones and Aryel Beck and Phillippe Camacho and Nicolas Lassabe and Herve Luga and Franccois Scharffe", title = "Evaluation of chess position by modular neural network generated by genetic algorithm", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "1--10", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_1", abstract = "Chess engine Tempo. One of the major difficulties for this type of program lies in the function for evaluating game positions. This function is composed of a large number of parameters which have to be determined and then adjusted. We propose an alternative which consists in replacing this function by an artificial neuron network (ANN). Without topological knowledge of this complex network, we use the evolutionist methods for its inception, thus enabling us to obtain, among other things, a modular network. Finally, we present our results: (i) reproduction of the XOR function which validates the method used and (ii) generation of an evaluation function", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{Aversano:2005:WSEC, author = "Lerina Aversano and Massimiliano {Di Penta} and Kunal Taneja", title = "A genetic programming approach to support the design of service compositions", booktitle = "Proceedings of the first International Workshop of Engineering Service Compositions, WESC'05", year = "2005", editor = "Christian Zirpins and Guadalupe Ortiz and Winfried Lamersdorf and Wolfgang Emmerich", pages = "17--24", address = "Amsterdam, The Netherlands", number = "RC23821 (W0512-008)", month = dec, series = "IBM Research Reports", keywords = "genetic algorithms, genetic programming", URL = "http://domino.research.ibm.com/library/cyberdig.nsf/papers/DE71563B7B69D362852570D000548D0D/$File/rc23821.pdf", notes = "Slides http://www.rcost.unisannio.it/mdipenta/papers/wesc05.pdf parts of \cite{WESC05}", } @Article{Aversano:2006:IJCSSE, author = "Lerina Aversano and Massimiliano {Di Penta} and Kunal Taneja", title = "A genetic programming approach to support the design of service compositions", journal = "International Journal of Computer Systems Science \& Engineering", year = "2006", volume = "21", number = "4", pages = "247--254", month = jul, organisation = "Curtin University of Technology, Australia", publisher = "CRL Publishing, admin@crlpublishing.co.uk", keywords = "genetic algorithms, genetic programming, SBSE, service compositions, distributed software, workflow", ISSN = "0267 6192", URL = "http://www.rcost.unisannio.it/mdipenta/papers/csse06.pdf", size = "8 pages", oai = "oai:CiteSeerXPSU:10.1.1.145.843", abstract = "The design of service composition is one of the most challenging research problems in service-oriented software engineering. Building composite services is concerned with identifying a suitable set of services that orchestrated in some way is able to solve a business goal which cannot be resolved using a single service amongst those available. Despite the literature reports several approaches for (semi) automatic service composition, several problems, such as the capability of determining the composition's topology, still remain open. This paper proposes a search-based approach to semi-automatically support the design of service compositions. In particular, the approach uses genetic programming to automatically generate workflows that accomplish a business goal and exhibit a given QoS level, with the aim of supporting the service integrator activities in the finalization of the workflow.", notes = "WSDL, BPEL4WS. GP tree made of sequence, switch flow, loop nodes. Pop=100, Generations=1000, initioal pop<= 5 nodes. Fitness based on precision and recall. GP compared with exhuastive search. Cited by \cite{Rodriguez-Mier:2010:EI}, cites \{1068189} GECCO 2005. SeCSEP", } @InProceedings{conf/hais/AvilaGV09, title = "Multi-label Classification with Gene Expression Programming", author = "J. L. Avila and Eva Lucrecia {Gibaja Galindo} and Sebastian Ventura", bibdate = "2009-06-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/hais/hais2009.html#AvilaGV09", booktitle = "Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009", publisher = "Springer", year = "2009", volume = "5572", editor = "Emilio Corchado and Xindong Wu and Erkki Oja and Alvaro Herrero and Bruno Baruque", isbn13 = "978-3-642-02318-7", pages = "629--637", series = "Lecture Notes in Computer Science", address = "Salamanca, Spain", month = jun # " 10-12", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://dx.doi.org/10.1007/978-3-642-02319-4", DOI = "doi:10.1007/978-3-642-02319-4_76", abstract = "In this paper, we introduce a Gene Expression Programming algorithm for multi label classification. This algorithm encodes each individual into a discriminant function that shows whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. In order to evaluate the quality of our algorithm, its performance is compared to that of four recently published algorithms. The results show that our proposal is the best in terms of accuracy, precision and recall", } @InCollection{Avila-Jimenez:2010:HAIS, author = "Jose Luis Avila-Jimenez and Eva Gibaja and Sebastian Ventura", title = "Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study", booktitle = "Hybrid Artificial Intelligence Systems", year = "2010", series = "Lecture Notes in Computer Science", editor = "Emilio Corchado and Manuel {Grana Romay} and Alexandre {Manhaes Savio}", publisher = "Springer", pages = "9--16", volume = "6077", address = "San Sebastian, Spain", month = jun # " 23-25", DOI = "doi:10.1007/978-3-642-13803-4_2", keywords = "genetic algorithms, genetic programming, gene expression programming", abstract = "The present work expounds a preliminary work of a genetic programming algorithm to deal with multi-label classification problems. The algorithm uses Gene Expression Programming and codifies a classification rule into each individual. A niching technique assures diversity in the population. The final classifier is made up by a set of rules for each label that determines if a pattern belongs or not to the label. The proposal have been tested over several domains and compared with other multi-label algorithms and the results shows that it is specially suitable to handle with nominal data sets.", affiliation = "University of Cordoba Department of Computer Sciences and Numerical Analysis", } @Article{journals/mvl/Avila-JimenezGZV11, author = "Jose Luis Avila-Jimenez and Eva Lucrecia {Gibaja Galindo} and Amelia Zafra and Sebastian Ventura", title = "A Gene Expression Programming Algorithm for Multi-Label Classification", journal = "Journal of Multiple-Valued Logic and Soft Computing", year = "2011", number = "2-3", volume = "17", pages = "183--206", keywords = "genetic algorithms, genetic programming, gene expression programming, multi-label classification, discriminant functions, machine learning", ISSN = "1542-3980", URL = "http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-17-number-2-3-2011/mvlsc-17-2-3-p-183-206/", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv17n2-3contents.html", broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC17.2-3abstracts/MVLSCv17n2-3p183-206Avila.html", abstract = "This paper presents a Gene Expression Programming algorithm for multilabel classification which encodes a discriminant function into each individual to show whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. The algorithm has been compared to other recently published algorithms. The results found on several datasets demonstrate the feasibility of this approach in the tackling of multi-label problems.", notes = "SPECIAL ISSUE Soft Computing Techniques in Data Mining", bibdate = "2011-07-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/mvl/mvl17.html#Avila-JimenezGZV11", } @PhdThesis{Avila-Jimenez:thesis, author = "Jose Luis Avila-Jimenez", title = "Genetic Programing for multi-label classification", titulo = "Modelos de Aprendizaje Basados en Programacion Genetica para Clasificacion Multi-Etiqueta", school = "Department of Computers Science and Numerical Analysis, University of Cordoba", year = "2013", address = "Spain", month = jun, keywords = "genetic algorithms, genetic programming", broken = "http://www.uco.es/~ma1vesos/en/research/phdstudents.html", URL = "http://www.uco.es/grupos/kdis/docs/thesis/2013-JLAvila.pdf", broken = "http://www.uco.es/grupos/kdis/index.php?option=com_jresearch&view=thesis&task=show&id=4&Itemid=51&lang=en", URL = "https://dialnet.unirioja.es/servlet/tesis?codigo=70284", size = "226 pages", abstract = "El problema de clasificacion consiste en asociar una serie de etiquetas a una serie de ejemplos o patrones. En la clasificacion clasica a cada patron de entrenamiento solamente se le puede asociar una sola etiqueta de un conjunto de etiquetas. Por tanto se consideran que los conjuntos de clases objetivo en los que se agruparan los patrones son por definicion conjuntos disjuntos. En el caso de la clasificacion multi-etiqueta, los conjuntos objetivos no son disjuntos, pudiendo haber patrones a los que se les asocie mas de una etiqueta. Por tanto, los ejemplos se asocian a un conjunto de etiquetas y el resultado puede tomar varios valores dentro del conjunto de etiquetas[1]. El objetivo que se plantea en esta tesis es el desarrollo de una serie de modelos de programacion genetica para resolver problemas de clasificacion multi-etiqueta.", notes = "In Spanish. Supervisor: Sebastian Ventura Soto 2nd supervisor Eva Lucrecia Gibaja Galindo TIN2008-06681-C06-03 P08-TIC-3720", } @InProceedings{conf/lion/Awad11, author = "Wasan Shaker Awad", title = "Designing Stream Cipher Systems Using Genetic Programming", booktitle = "Selected papers from the 5th International Conference on Learning and Intelligent Optimization (LION 5) 2011", year = "2011", editor = "Carlos A. {Coello Coello}", volume = "6683", series = "Lecture Notes in Computer Science", pages = "308--320", address = "Rome, Italy", month = jan # " 17-21", note = "Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-25565-6", DOI = "doi:10.1007/978-3-642-25566-3_23", size = "13 pages", abstract = "Genetic programming is a good technique for finding near-global optimal solutions for complex problems, by finding the program used to solve the problems. One of these complex problems is designing stream cipher systems automatically. Steam cipher is an important encryption technique used to protect private information from an unauthorised access, and it plays an important role in the communication and storage systems. In this work, we propose a new approach for designing stream cipher systems of good properties, such as high degree of security and efficiency. The proposed approach is based on the genetic programming. Three algorithms are presented here, which are simple genetic programming, simulated annealing programming, and adaptive genetic programming. Experiments were performed to study the effectiveness of these algorithms in solving the underlying problem.", bibdate = "2011-11-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/lion/lion2011.html#Awad11", affiliation = "Department of Information Systems, College of Information Technology, University of Bahrain, Sakheer, Bahrain", } @InCollection{Awange2016, author = "Joseph L. Awange and Bela Palancz", title = "Symbolic Regression", booktitle = "Geospatial Algebraic Computations: Theory and Applications", publisher = "Springer", year = "2016", chapter = "11", pages = "203--216", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-25465-4", DOI = "doi:10.1007/978-3-319-25465-4_11", abstract = "Symbolic regression (SR) is the process of determining the symbolic function, which describes a data set-effectively developing an analytic model, which summarizes the data and is useful for predicting response behaviours as well as facilitating human insight and understanding. The symbolic regression approach adopted herein is based upon genetic programming wherein a population of functions are allowed to breed and mutate with the genetic propagation into subsequent generations based upon a survival-of-the-fittest criteria. Amazingly, this works and, although computationally intensive, summary solutions may be reasonably discovered using current laptop and desktop computers.", notes = "Author Affiliations Curtin University, Perth, West Australia, Australia Budapest University of Technology and Economics, Budapest, Hungary", } @Article{AWOYERA:2020:JMRT, author = "Paul O. Awoyera and Mehmet S. Kirgiz and A. Viloria and D. Ovallos-Gazabon", title = "Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques", journal = "Journal of Materials Research and Technology", year = "2020", volume = "9", number = "4", pages = "9016--9028", month = jul # "--" # aug, keywords = "genetic algorithms, genetic programming, Gene expression programming, Artificial neural networks, ANN, Predictor, Response, Self-Compacting concrete, Geopolymers", ISSN = "2238-7854", DOI = "doi:10.1016/j.jmrt.2020.06.008", URL = "http://www.sciencedirect.com/science/article/pii/S2238785420314095", size = "13 pages", abstract = "There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model development involved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor", notes = "Department of civil Engineering, Covenant University, Ota, Nigeria", } @InProceedings{Awuley:2016:CEC, author = "Anthony Awuley and Brian J. Ross", title = "Feature Selection and Classification Using Age Layered Population Structure Genetic Programming", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "2417--2426", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744088", abstract = "This paper presents a new algorithm called Feature Selection Age Layered Population Structure (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS is a modification of Hornby's ALPS algorithm - an evolutionary algorithm renown for avoiding pre-mature convergence on difficult problems. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal-symbol selection for the construction of GP trees/sub-trees. The FSALPS meta-heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non-converging evolutionary process that favours selection of features with high discrimination of class labels. We compared the performance of canonical GP, ALPS and FSALPS on some high-dimensional benchmark classification datasets, including a hyperspectral vision problem. Although all algorithms had similar classification accuracy, ALPS and FSALPS usually dominated canonical GP in terms of smaller and efficient trees. Furthermore, FSALPS significantly outperformed canonical GP, ALPS, and other feature selection strategies in the literature in its ability to perform dimensionality reduction", notes = "WCCI2016 MSc: (167 pages) https://core.ac.uk/download/pdf/62649745.pdf", } @MastersThesis{Aydogan:mastersthesis, author = "Emre Aydogan", title = "Automatic Generation of Mobile Malwares Using Genetic Programming", school = "Hacettepe Universitesi", year = "2014", address = "Ankara, Turkey", month = aug, keywords = "genetic algorithms, genetic programming, mobile malware, static analysis, obfuscation, evolutionary computation,", URL = "https://web.cs.hacettepe.edu.tr/~ssen/files/thesis/EmreTez.pdf", size = "82 pages", abstract = "The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at any time, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming. We aim to evaluate existing security solutions based on static analysis techniques against these evolved unknown malwares. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices.", notes = "Genetik Programlama Kullanilarak Mobil Zararli Yazilimların Otomatik Olarak Uretilmesi In Turkish Supervisor: Sevil Sen", } @InProceedings{Aydogan:2015:evoApplications, author = "Emre Aydogan and Sevil Sen", title = "Automatic Generation of Mobile Malwares Using Genetic Programming", booktitle = "18th European Conference on the Applications of Evolutionary Computation", year = "2015", editor = "Antonio M. Mora and Giovanni Squillero", series = "LNCS", volume = "9028", publisher = "Springer", pages = "745--756", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Mobile malware, Static analysis, Obfuscation, Evolutionary computation", isbn13 = "978-3-319-16548-6", URL = "http://web.cs.hacettepe.edu.tr/~ssen/files/papers/EvoStar15.pdf", DOI = "doi:10.1007/978-3-319-16549-3_60", size = "12 pages", abstract = "The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at anytime, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming (GP). We aim to evaluate the efficacy of current anti-virus products, using static analysis techniques, in the market. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices.", notes = "evoRISK EvoApplications2015 held in conjunction with EuroGP'2015, EvoCOP2015 and EvoMusArt2015 http://www.evostar.org/2015/cfp_evoapps.php", } @InProceedings{Aydogan:2019:WFCS, author = "E. Aydogan and S. Yilmaz and S. Sen and I. Butun and S. Forsstroem and M. Gidlund", booktitle = "2019 15th IEEE International Workshop on Factory Communication Systems (WFCS)", title = "A Central Intrusion Detection System for {RPL-Based} Industrial Internet of Things", year = "2019", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WFCS.2019.8758024", abstract = "Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers use the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.", notes = "Also known as \cite{8758024}", } @Article{AYDOGAN:2023:istruc, author = "Mehmet Safa Aydogan and Sema Alacali and Guray Arslan", title = "Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming", journal = "Structures", volume = "47", pages = "2209--2219", year = "2023", ISSN = "2352-0124", DOI = "doi:10.1016/j.istruc.2022.12.054", URL = "https://www.sciencedirect.com/science/article/pii/S2352012422012425", keywords = "genetic algorithms, genetic programming, Moment redistribution, RC continuous beams, Prediction, Design codes, Gene expression programming", abstract = "Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers four input parameters that are thought to influence moment redistribution the most in statically indeterminate RC beams using gene expression programming (GEP). For this reason, an experimental database of 108 data points was collected from experimental studies in the literature to predict the moment redistribution of the RC beams using genetic programming. All of these collected data points belong to two-span RC continuous beams. The results of the GEP formulation were statistically compared with the experimental results obtained from the literature and the results from the equations provided by the current design code provisions. The results of the comparison revealed that the proposed GEP-based formulation has the best performance and accuracy among the proposed models. Moreover, the sensitivity analysis and parametric study were also carried out to evaluate the most critical parameters affecting on moment redistribution of RC continuous beams", } @InProceedings{Ayerdi:2021:FSE-IND, author = "Jon Ayerdi and Valerio Terragni and Aitor Arrieta and Paolo Tonella and Goiuria Sagardui and Maite Arratibel", title = "Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study", booktitle = "ESEC/FSE 2021", year = "2021", editor = "Miltiadis Allamanis and Moritz Beller", pages = "1264--1274", address = "Athens, Greece", month = "23-28 " # aug, publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, CPS, mutation testing, metamorphic testing, evolutionary algorithm, cyber physical systems, quality of service, oracle generation, oracle improvement", isbn13 = "978-1-4503-8562-6", URL = "https://www.conference-publishing.com/list.php?Event=FSE21&Full=noabs#fse21ind-p28-p-title", DOI = "doi:10.1145/3468264.3473920", size = "11 pages", abstract = "One of the major challenges in the verification of complex industrial Cyber-Physical Systems is the difficulty of determining whether a particular system output or behaviour is correct or not, the so-called test oracle problem. Metamorphic testing alleviates the oracle problem by reasoning on the relations that are expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. In this paper, we present a case study aiming at automating this process. To this end, we implemented GAssertMRs, a tool to automatically generate MRs with genetic programming. We assess the cost-effectiveness of this tool in the context of an industrial case study from the elevation domain. Our experimental results show that in most cases GAssertMRs outperforms the other baselines, including manually generated MRs developed with the help of domain experts. We then describe the lessons learned from our experiments and we outline the future work for the adoption of this technique by industrial practitioners.", notes = "Mondragon University, Spain; University of Auckland, New Zealand; USI Lugano, Switzerland; Orona, n.n.", } @InProceedings{ayerdi:2022:GECCOhop, author = "Jon Ayerdi and Valerio Terragni and Aitor Arrieta and Paolo Tonella and Goiuria Sagardui and Maite Arratibel", title = "Evolutionary Generation of Metamorphic Relations for {Cyber-Physical} Systems", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Marcus Gallagher", pages = "15--16", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, quality of service, cyber physical systems, metamorphic testing, oracle improvement, oracle generation, genetic programming, evolutionary algorithm, mutation testing, metamorphic testing", isbn13 = "978-1-4503-9268-6/22/07", URL = "https://valerio-terragni.github.io/assets/pdf/ayerdi-gecco-2022.pdf", DOI = "doi:10.1145/3520304.3534077", size = "2 pages", abstract = "A problem when testing Cyber-Physical Systems (CPS) is the difficulty of determining whether a particular system output or behaviour is correct or not. Metamorphic testing alleviates such a problem by reasoning on the relations expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. This paper summarizes our recent publication: {"}Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study{"}, presented at ESEC/FSE 2021. In that publication we presented GAssertMRs, the first technique to automatically generate MRs for CPS, leveraging GP to explore the space of candidate solutions. We evaluated GAssertMRs in an industrial case study, outperforming other baselines.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Misc{DBLP:journals/corr/abs-2312-15302, author = "Jon Ayerdi and Valerio Terragni and Gunel Jahangirova and Aitor Arrieta and Paolo Tonella", title = "Automatically Generating Metamorphic Relations via Genetic Programming", howpublished = "arXiv", volume = "abs/2312.15302", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2312.15302", DOI = "doi:10.48550/ARXIV.2312.15302", eprinttype = "arXiv", eprint = "2312.15302", timestamp = "Thu, 18 Jan 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2312-15302.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See \cite{Genmorph_TSE_2024}", } @Article{Genmorph_TSE_2024, author = "Jon Ayerdi and Valerio Terragni and Gunel Jahangirova and Aitor Arrieta and Paolo Tonella", title = "GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming", journal = "IEEE Transactions on Software Engineering", volume = "50", number = "7", pages = "1888--1900", month = jul, keywords = "genetic algorithms, genetic programming, Testing, Java, Generators, Space exploration, Manuals, metamorphic testing, oracle problem, metamorphic relations, mutation analysis, mutation testing, Filters, GenMorph, EvoSuite", ISSN = "0098-5589", URL = "https://kclpure.kcl.ac.uk/portal/en/publications/genmorph-automatically-generating-metamorphic-relations-via-genet", DOI = "doi:10.1109/TSE.2024.3407840", size = "11 pages", abstract = "Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. GENMORPH, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are Boolean, numerical, or ordered sequences. GENMORPH uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GENMORPH generates effective MRs for 18 out of 23 methods (mutation score >20 percent). Furthermore, it can increase RANDOOP fault detection capability in 7 out of 23 methods, and EVOSUITE in 14 out of 23 methods.", language = "English", notes = "also known as \cite{10542726} See \cite{DBLP:journals/corr/abs-2312-15302} To be presented at ICSE 2025 https://conf.researchr.org/track/icse-2025/icse-2025-journal-first-papers#Accepted-Papers", } @Article{journals/peerjpre/AyralA17, author = "Hakan Ayral and Songul Albayrak", title = "Parallel and in-process compilation of individuals for genetic programming on {GPU}", journal = "PeerJ PrePrints", year = "2017", volume = "5", pages = "e2936", keywords = "genetic algorithms, genetic programming, GPU", bibdate = "2017-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/peerjpre/peerjpre5.html#AyralA17", DOI = "doi:10.7287/peerj.preprints.2936v1", abstract = "Three approaches to implement genetic programming on GPU hardware are compilation, interpretation and direct generation of machine code. The compiled approach is known to have a prohibitive overhead compared to other two.", notes = "NOT PEER-REVIEWED", } @Article{journals/jsw/AyralA17, author = "Hakan Ayral and Songul Albayrak", title = "Effects of Population, Generation and Test Case Count on Grammatical Genetic Programming for Integer Lists", journal = "Journal of Software", year = "2017", number = "6", volume = "12", pages = "483--492", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1796-217X", bibdate = "2017-06-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jsw/jsw12.html#AyralA17", DOI = "doi:10.17706/jsw.12.6.483-492", abstract = "This paper investigates how grammatical genetic programming performs for evolving simple integer list manipulation functions. We propose three sub-problems which are related to, or component of integer sorting problem as defined by genetic programming literature. We further investigate the effects of modifying evolutionary parameters, such as the number of generations allowed, number of populations, and number of test cases, on the number and distribution of successful solutions. Finally, we propose an AST based dead-code removal for the intron induced non-functional codes on evolved individuals.", notes = "Yildiz Technical University, Computer Engineering Department, Istanbul, Turkey.", } @Article{Aytek:2008:JH, author = "Ali Aytek and Ozgur Kisi", title = "A genetic programming approach to suspended sediment modelling", journal = "Journal of Hydrology", year = "2008", volume = "351", number = "3-4", pages = "288--298", month = "15 " # apr, keywords = "genetic algorithms, genetic programming, Suspended sediment load, Rating curves, Soft computing", DOI = "doi:10.1016/j.jhydrol.2007.12.005", abstract = "This study proposes genetic programming (GP) as a new approach for the explicit formulation of daily suspended sediment-discharge relationship. Empirical relations such as sediment rating curves are often applied to determine the average relationship between discharge and suspended sediment load. This type of models generally underestimates or overestimates the amount of sediment. During recent decades, some black box models based on artificial neural networks have been developed to overcome this problem. But these type of models are implicit that can not be simply used by other investigators. Therefore it is still necessary to develop an explicit model for the discharge-sediment relationship. It is aimed in this study, to develop an explicit model based on genetic programming. Explicit models obtained using the GP are compared with rating curves and multi-linear regression techniques in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The results indicate that the proposed GP formulation performs quite well compared to sediment rating curves and multi-linear regression models and is quite practical for use.", notes = "Gaziantep University, Civil Engineering Department, Hydraulics Division, 27310 Gaziantep, Turkey Erciyes University, Civil Engineering Department, Hydraulics Division, 38039 Kayseri, Turkey", } @Article{Aytek:2008:JESS, author = "Ali Aytek and M Asce and Murat Alp", title = "An application of artificial intelligence for rainfall-runoff modeling", journal = "Journal of Earth System Science", year = "2008", volume = "117", number = "2", pages = "145--155", month = apr, email = "aytek@gantep.edu.tr", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://www.ias.ac.in/jess/apr2008/d093.pdf", size = "11 pages", abstract = "This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.", } @InCollection{aytekin:1995:4-OPmap, author = "Tevfik Aytekin and Emin Erkan Korkmaz and Halil Altay G{\"{u}}vennir", title = "An application of genetic programming to the 4-OP problem using map-trees", booktitle = "Progress in Evolutionary Computation", publisher = "Springer-Verlag", year = "1995", editor = "Xin Yao", volume = "956", series = "Lecture Notes in Artificial Intelligence", pages = "28--40", address = "Heidelberg, Germany", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.bilkent.edu.tr/tech-reports/1994/BU-CEIS-9441.ps.z", URL = "http://citeseer.ist.psu.edu/16240.html", DOI = "doi:10.1007/3-540-60154-6_45", size = "13 pages", abstract = "In Genetic programming (GP) applications the programs are expressed as parse trees. A node of a parse tree is an element either from the function-set or terminal-set, and an element of a terminal set can be used in a parse tree more than once. However, when we attempt to use the elements in the terminal set at most once, we encounter problems in creating the initial random population and in crossover and mutation operations. 4-Op problem is an example for such a situation. We developed a technique called map-trees to overcome these anomalies. Experimental results on 4-Op using map-trees are presented.", notes = " Also technical report BU-CEIS-9441 Bilkent University Department of Computer Engineering", } @InProceedings{azad:2002:gecco, author = "R. Muhammad Atif Azad and Conor Ryan and Mark E. Burke and Ali R. Ansari", title = "A Re-examination Of The Cart Centering Problem Using The Chorus System", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "707--715", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP144.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP144.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", abstract = "The cart centering problem is well known in the field of evolutionary algorithms and has often been used as a proof of concept problem for techniques such as Genetic Programming. This paper describes the application of a grammar based, position independent encoding scheme, Chorus, to the problem. It is shown that using the traditional experimental set up employed to solve the problem, Chorus is able to come up with the solutions which appear to beat the theoretically optimal solution, known and accepted for decades in the field of control theory. However, further investigation into the literature of the relevant area reveals that there is an inherent error in the standard E.C. experimental approach to this problem, leaving room for a multitude of solutions to out perform the apparent best. This argument is validated by the performance of Chorus, producing better solutions at a number of occasions.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{azad:2002:gecco:workshop, title = "A Position Independent Evolutionary Automatic Programming Algorithm - The {Chorus} System", author = "R. Muhammad Atif Azad", pages = "260--263", booktitle = "Graduate Student Workshop", editor = "Sean Luke and Conor Ryan and Una-May O'Reilly", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{azad:2003:gecco, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Structural Emergence with Order Independent Representations", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1626--1638", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1007/3-540-45110-2_57", abstract = "This paper compares two grammar based Evolutionary Automatic Programming methods, Grammatical Evolution (GE) and Chorus. Both systems evolve sequences of derivation rules which can be used to produce computer programs, however, Chorus employs a position independent representation, while GE uses polymorphic codons, the meaning of which depends on the context in which they are used. We consider issues such as the order in which rules appear in individuals, and demonstrate that an order always emerges with Chorus, which is similar to that of GE, but more flexible. The paper also examines the final step of evolution, that is, how perfect individuals are produced, and how they differ from their immediate neighbours. We demonstrate that, although Chorus appears to be more flexible structure-wise, GE tends to produce individuals with a higher neutrality, suggesting that its representation can, in some cases, make finding the perfect solution easier.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @PhdThesis{Azad:thesis, author = "Raja Muhammad Atif Azad", title = "A Position Independent Representation for Evolutionary Automatic Programming Algorithms - The Chorus System", school = "University of Limerick", year = "2003", address = "Ireland", month = dec, URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/azad_thesis.ps.gz", size = "212 pages", keywords = "genetic algorithms, genetic programming, Chorus System, Grammatical Evolution", abstract = "We describe a new position independent encoding system, Chorus, for grammar based Evolutionary Algorithms. This scheme is coarsely based on the manner in nature in which genes produce proteins that regulate the metabolic pathways of the cell. The phenotype is the behaviour of the cells metabolism, which corresponds to the development of the computer program in our case. In this procedure, the actual protein encoded by a gene is the same regardless of the position of the gene within the genome. We show that the Chorus system has a very convenient Regular Expression type schema notation that can be used to describe the presence of various phenotypic traits. This notation is used to demonstrate that massive areas of neutrality can exist in the search landscape, and the system is also shown to be able to dispense with large areas of the search space that are unlikely to contain useful solutions. The searching capability of the system is exemplified by its application on a number of proof of concept problems, where the system has shown comparable performance to Genetic Programming and Grammatical Evolution and, in certain cases, it has produced superior results. We also analyse the role of the crossover in the Chorus System and conclude by showing its application on a real world problem from the blood flow domain.", notes = "Supervisor: Conor Ryan", } @Article{Azad:2004:ASC, author = "R. Muhammad Atif Azad and Ali R. Ansari and Conor Ryan and Michael Walsh and Tim McGloughlin", title = "An evolutionary approach to Wall Sheer Stress prediction in a grafted artery", journal = "Applied Soft Computing", publisher = "Elsevier", year = "2004", volume = "4", number = "2", pages = "139--148", month = may, keywords = "genetic algorithms, genetic programming, grammatical evolution, chorus system, Wall Shear Stress, Laser Doppler anemometry, Mathematical modeling, Computational Fluid Dynamics", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2003.11.001", abstract = "Restoring the blood supply to a diseased artery is achieved by using a vascular bypass graft. The surgical procedure is a well documented and successful technique. The most commonly cited hemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is Wall Shear Stress (WSS). WSS distributions are predicted using numerical simulations as they can provide quick and precise results to assess the effects that alternative graft/artery junction geometries have on the WSS distributions in bypass grafts. Validation of the numerical model is required and in vitro studies, using laser Doppler anemometry (LDA), have been employed to achieve this. Numerically, the Wall Shear Stress is predicted using velocity values stored in the computational cell near the wall and assuming zero velocity at the wall. Experimentally obtained velocities require a mathematical model to describe their behavior. This study employs a grammar based evolutionary algorithm termed Chorus for this purpose and demonstrates that Chorus successfully attains this objective. It is shown that even with the lack of domain knowledge, the results produced by this automated system are comparable to the results in the literature.", notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description", } @InCollection{azad:2005:GPTP, author = "R. Muhammad Atif Azad and Conor Ryan", title = "An Examination of Simultaneous Evolution of Grammars and Solutions", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "10", pages = "141--158", address = "Ann Arbor", month = "12-14 " # may, publisher = "Kluwer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Evolving Grammars, Grammatical ADFs, Generative Representations", ISBN = "0-387-28110-X", DOI = "doi:10.1007/0-387-28111-8_10", size = "18 pages", abstract = "This chapter examines the notion of co-evolving grammars with a population of individuals. This idea has great promise because it is possible to dynamically reshape the solution space while evolving individuals. We compare such a system with a more standard system with fixed grammars and demonstrate that, on a selection of benchmark problems, the standard approach appears to be better. Several different context free grammars, including one inspired by Koza's GPPS system are examined, and a number of surprising results appear, which indicate that several representative GP benchmark problems are best tackled by a standard GP approach.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @InProceedings{Azad:2008:geccocomp, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Gecco 2008 grammatical evolution tutorial", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 tutorials", pages = "2339--2366", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2339.pdf", DOI = "doi:10.1145/1388969.1389058", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, chorus, GAuGE, grammars, linear strings", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1389058}", } @InProceedings{Azad:2010:gecco, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Abstract functions and lifetime learning in genetic programming for symbolic regression", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "893--900", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830645", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong. This paper proposes a simple alternative to this powerlessness by allowing an individual to tune its internal nodes and going through several evaluations before it has to compete with other individuals. We demonstrate that this system, Chameleon, outperforms standard GP over a selection of symbolic regression type problems on both training and test sets; that the system works harmoniously with two other well known extensions to GP, that is, linear scaling and a diversity promoting tournament selection method; that it can benefit dramatically from a simple cache; that adding to functions set does not always add to the tuning expense; and that tuning alone can be enough to promote smaller trees in the population. Finally, we touch upon the consequences of ignoring the effects of complexity when focusing on just the tree sizes to induce parsimony pressure in GP populations.", notes = "Also known as \cite{1830645} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Azad:2011:GECCO, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Variance based selection to improve test set performance in genetic programming", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1315--1322", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001754", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper proposes to improve the performance of Genetic Programming (GP) over unseen data by minimizing the variance of the output values of evolving models alongwith reducing error on the training data. Variance is a well understood, simple and inexpensive statistical measure; it is easy to integrate into a GP implementation and can be computed over arbitrary input values even when the target output is not known. Moreover, we propose a simple variance based selection scheme to decide between two models (individuals). The scheme is simple because, although it uses bi-objective criteria to differentiate between two competing models, it does not rely on a multi-objective optimisation algorithm. In fact, standard multi-objective algorithms can also employ this scheme to identify good trade-offs such as those located around the knee of the Pareto Front. The results indicate that, despite some limitations, these proposals significantly improve the performance of GP over a selection of high dimensional (multi-variate) problems from the domain of symbolic regression. This improvement is manifested by superior results over test sets in three out of four problems, and by the fact that performance over the test sets does not degrade as often witnessed with standard GP; neither is this performance ever inferior to that on the training set. As with some earlier studies, these results do not find a link between expressions of small sizes and their ability to generalise to unseen data.", notes = "Also known as \cite{2001754} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{azad:2014:EuroGP, author = "R. Muhammad Atif Azad and Conor Ryan", title = "The Best Things Don't Always Come in Small Packages: Constant Creation in Grammatical Evolution", booktitle = "17th European Conference on Genetic Programming", year = "2014", editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim", series = "LNCS", volume = "8599", publisher = "Springer", pages = "186--197", address = "Granada, Spain", month = "23-25 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Grammatical Evolution :poster", isbn13 = "978-3-662-44302-6", DOI = "doi:10.1007/978-3-662-44303-3_16", abstract = "This paper evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results against those from Genetic Programming (GP). Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. The GE methods all perform significantly better than GP on unseen test data, and we demonstrate that the standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material.", notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014", } @Article{Azad:2014:EC, author = "Raja Muhammad Atif Azad and Conor Ryan", title = "A Simple Approach to Lifetime Learning in Genetic Programming based Symbolic Regression", journal = "Evolutionary Computation", year = "2014", volume = "22", number = "2", pages = "287--317", month = "Summer", keywords = "genetic algorithms, genetic programming, hill climbing, Lamarckian, genetic repair, Memetic Algorithms, lifetime learning, local search, hybrid genetic algorithms", ISSN = "1063-6560", URL = "http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00111", URL = "https://pure.ul.ie/en/publications/a-simple-approach-to-lifetime-learning-in-genetic-programming-bas", URL = "https://ref2021-resultsapp-live.azurewebsites.net/outputs/6b1e8c2e-083e-4fc5-9f66-10dc5172e041", DOI = "doi:10.1162/EVCO_a_00111", size = "31 pages", abstract = "Genetic Programming (GP) coarsely models natural evolution to evolve computer programs. Unlike in nature, where individuals can often improve their fitness through lifetime experience, the fitness of GP individuals generally does not change during their lifetime, and there is usually no opportunity to pass on acquired knowledge. This paper introduces the Chameleon system to address this discrepancy and augment GP with lifetime learning by adding a simple local search that operates by tuning the internal nodes of individuals. Although not the first attempt to combine local search with GP, its simplicity means that it is easy to understand and cheap to implement. A simple cache is added which leverages the local search to reduce the tuning cost to a small fraction of the expected cost, and we provide a theoretical upper limit on the maximum tuning expense given the average tree size of the population and show that this limit grows very conservatively as the average tree size of the population increases. We show that Chameleon uses available genetic material more efficiently by exploring more actively than with standard GP, and demonstrate that not only does Chameleon outperform standard GP (on both training and test data) over a number of symbolic regression type problems, it does so by producing smaller individuals and that it works harmoniously with two other well known extensions to GP, namely, linear scaling and a diversity-promoting tournament selection method.", notes = "Chameleon, cache, UCI, depth first hill climbing, internal nodes (functions) only. Disappeared Sep 2014 http://casnew.iti.upv.es/index.php/evocompetitions/105-symregcompetition", } @InProceedings{Azad:2014:NaBIC, author = "R. Muhammad Atif Azad and David Medernach and Conor Ryan", title = "Efficient Approaches to Interleaved Sampling of training data for Symbolic Regression", booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing", year = "2014", editor = "Ana Maria Madureira and Ajith Abraham and Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and Choo yun Huoy", pages = "176--183", address = "Porto, Portugal", month = "30 " # jul # " - 1 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-5937-2/14", DOI = "doi:10.1109/NaBIC.2014.6921874", abstract = "The ability to generalise beyond the training set is paramount for any machine learning algorithm and Genetic Programming (GP) is no exception. This paper investigates a recently proposed technique to improve generalisation in GP, termed Interleaved Sampling where GP alternates between using the entire data set and only a single data point in alternate generations. This paper proposes two alternatives to using a single data point: the use of random search instead of a single data point, and simply minimising the tree size. Both the approaches are more efficient than the original Interleaved Sampling because they simply do not evaluate the fitness in half the number of generations. The results show that in terms of generalisation, random search and size minimisation are as effective as the original Interleaved Sampling; however, they are computationally more efficient in terms of data processing. Size minimisation is particularly interesting because it completely prevents bloat while still being competitive in terms of training results as well as generalisation. The tree sizes with size minimisation are substantially smaller reducing the computational expense substantially.", notes = "NaBIC 2014 http://www.mirlabs.net/nabic14/", } @InProceedings{Azad:2014:GECCOcomp, author = "R. Muhammad Atif Azad and David Medernach and Conor Ryan", title = "Efficient interleaved sampling of training data in genetic programming", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "127--128", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598480", DOI = "doi:10.1145/2598394.2598480", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The ability to generalise beyond the training set is important for Genetic Programming (GP). Interleaved Sampling is a recently proposed approach to improve generalisation in GP. In this technique, GP alternates between using the entire data set and only a single data point. Initial results showed that the technique not only produces solutions that generalise well, but that it so happens at a reduced computational expense as half the number of generations only evaluate a single data point. This paper further investigates the merit of interleaving the use of training set with two alternatives approaches. These are: the use of random search instead of a single data point, and simply minimising the tree size. Both of these alternatives are computationally even cheaper than the original setup as they simply do not invoke the fitness function half the time. We test the utility of these new methods on four, well cited, and high dimensional problems from the symbolic regression domain. The results show that the new approaches continue to produce general solutions despite taking only half the fitness evaluations. Size minimisation also prevents bloat while producing competitive results on both training and test data sets. The tree sizes with size ionisation are substantially smaller than the rest of the setups, which further brings down the training costs.", notes = "Also known as \cite{2598480} Distributed at GECCO-2014.", } @Article{Azad:2017:GPEM, author = "Raja Muhammad Atif Azad", title = "Krzysztof Krawiec: Behavioral program synthesis with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "1", pages = "111--113", month = mar, note = "Book review", keywords = "genetic algorithms, genetic programming, program synthesis, machine learning", ISSN = "1389-2576", URL = "https://rdcu.be/dR8eg", DOI = "doi:10.1007/s10710-016-9283-7", size = "3 pages", abstract = "Review of \cite{KrawiecBPS2016}", notes = "Springer 2016, ISBN: 978-3-319-27563-5", } @InCollection{Azad:2018:hbge, author = "R. Muhammad Atif Azad and Conor Ryan", title = "Comparing Methods to Creating Constants in Grammatical Evolution", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "10", pages = "245--262", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_10", abstract = "This chapter evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results by comparing against those from a reasonably standard Genetic Programming (GP) setup. Specifically, the chapter compares a standard GE method to constant creation termed digit concatenation with what this chapter calls compact methods to constant creation. Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we investigate if more compact methods, which are more similar to the GP style of constant creation (Ephemeral Random Constants (ERCs), perform better. The results are surprising. Against common wisdom, a standard GE approach of digit concatenation does not produce individuals that are any larger than those from methods which are designed to use less genetic material. In fact, while GP characteristically evolves increasingly larger individuals, GE (after an initial growth or drop in sizes) tends to keep individual sizes stable despite no explicit mechanisms to control size growth. Furthermore, various GE setups perform acceptably well on unseen test data and typically outperform GP. Overall, these results encourage a belief that standard GE methods to symbolic regression are relatively resistant to pathogenic evolutionary tendencies of code bloat and overfitting.", notes = "Part of \cite{Ryan:2018:hbge}", } @InProceedings{azam:1998:dsi:cs, author = "Farooq Azam and H. F. VanLandingham", title = "Dynamic Systems Identification: A Comparitive Study", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "2--5", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "4 pages", notes = "GP-98LB", } @InProceedings{azam:1998:dsiGP, author = "Farooq Azam and H. F. VanLandingham", title = "Dynamic Systems Identification using Genetic Programming", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "6", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", size = "1 page", notes = "GP-98LB", } @Article{MDAZAMATHULLA2008477, author = "H. Md Azamathulla and A. {Ab. Ghani} and N. A. Zakaria and S. H. Lai and C. K. Chang and C. S. Leow and Z. Abuhasan", title = "Genetic programming to predict ski-jump bucket spill-way scour", journal = "Journal of Hydrodynamics, Ser. B", volume = "20", number = "4", pages = "477--484", year = "2008", ISSN = "1001-6058", DOI = "doi:10.1016/S1001-6058(08)60083-9", URL = "http://www.sciencedirect.com/science/article/B8CX5-4TCY8GV-B/2/f3004ab0cd7ed153a22b7f5d637afc89", month = aug, keywords = "genetic algorithms, genetic programming, neural networks, spillway scour, ski-jump bucket", abstract = "Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.", } @Article{Azamathulla:2010:JHE, author = "H. Md. Azamathulla and Aminuddin {Ab Ghani} and Nor Azazi Zakaria and Aytac Guven", title = "Genetic Programming to Predict Bridge Pier Scour", journal = "Journal of Hydraulic Engineering", year = "2010", volume = "136", number = "3", pages = "165--169", keywords = "genetic algorithms, genetic programming, Local scour, Bridge pier, Artificial neural networks, Radial basis function", DOI = "doi:10.1061/(ASCE)HY.1943-7900.0000133", size = "5 page", abstract = "Bridge pier scouring is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by authors), in the form of artificial neural networks (ANNs) and genetic programming (GP). 398 data sets of field measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth of bridge piers.", } @Article{Azamathulla2011, author = "H. Md. Azamathulla and Aytac Guven and Yusuf Kagan Demir", title = "Linear genetic programming to scour below submerged pipeline", journal = "Ocean Engineering", volume = "38", number = "8-9", pages = "995--1000", year = "2011", month = jun, ISSN = "0029-8018", DOI = "doi:10.1016/j.oceaneng.2011.03.005", URL = "http://www.sciencedirect.com/science/article/B6V4F-52M3TGW-1/2/279184e6554e6b6977d8b9f0180c9f53", keywords = "genetic algorithms, genetic programming, Local scour, Neuro-fuzzy, Pipelines", abstract = "Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline.", } @Article{azamathulla:2011:WRM, author = "Hazi Mohammad Azamathulla and Aminuddin Ab. Ghani", title = "Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams", journal = "Water Resources Management", year = "2011", volume = "25", number = "6", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-010-9759-9", DOI = "doi:10.1007/s11269-010-9759-9", } @Article{Azamathulla:2012:JH, author = "H. Md. Azamathulla and Z. Ahmad", title = "{GP} approach for critical submergence of intakes in open channel flows", journal = "Journal of Hydroinformatics", year = "2012", volume = "14", number = "4", pages = "937--943", month = oct, keywords = "genetic algorithms, genetic programming, critical submergence, intakes, open channel", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/up/pdf/jh2012089.pdf", DOI = "doi:10.2166/hydro.2012.089", size = "7 pages", abstract = "This technical note presents the genetic programming (GP) approach to predict the critical submergence for horizontal intakes in open channel flow for different bottom clearances. Laboratory data from the literature for the critical submergence for a wide range of flow conditions were used for the development and testing of the proposed method. Froude number, Reynolds number, Weber number and ratio of intake velocity and channel velocity were considered dominant parameters affecting the critical submergence. The proposed GP approach produced satisfactory results compared to the existing predictors.", notes = "Vortex, water dam", } @Article{Azamathulla:2012a:JH, author = "H. Md. Azamathulla", title = "Gene-expression programming to predict scour at a bridge abutment", journal = "Journal of Hydroinformatics", year = "2012", volume = "14", number = "2", pages = "324--331", keywords = "genetic algorithms, genetic programming, gene expression programming, artificial neural networks, bridge abutments, local scour, radial basis function", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/014/0324/0140324.pdf", DOI = "doi:10.2166/hydro.2011.135", size = "8 pages", abstract = "The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modelling in predicting the scour depth at an abutment.", notes = "ANN, RBF. 'The overall performance of the GEP model is superior to the ANN model.' p330", } @Article{Azamathulla2012142, author = "H. Md. Azamathulla and Z. Ahmad", title = "Gene-expression programming for transverse mixing coefficient", journal = "Journal of Hydrology", volume = "434-435", pages = "142--148", year = "2012", month = apr, ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2012.02.018", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412001187", keywords = "genetic algorithms, genetic programming, Transverse mixing, Open channel flow, gene expression programming, River systems", abstract = "This study presents gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to predict the transverse mixing coefficient in open channel flows. Laboratory data were collected in the present study and also from the literature for the transverse mixing coefficient covering wide range of flow conditions. These data were used for the development and testing of the proposed method. A functional relation for the estimation of transverse mixing coefficient has been developed using GEP. The proposed GEP approach produced satisfactory results compared to the existing predictors for the transverse mixing coefficient.", } @Article{Azamathulla2012203, author = "H. Md. Azamathulla and A. Zahiri", title = "Flow discharge prediction in compound channels using linear genetic programming", journal = "Journal of Hydrology", volume = "454-455", pages = "203--207", year = "2012", month = "6 " # aug, ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2012.05.065", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412004684", keywords = "genetic algorithms, genetic programming, Stage-discharge curve, Flooded rivers, Floodplains", abstract = "Flow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage-discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models.", } @Article{Azamathulla:2012b:JH, author = "H. Md. Azamathulla", title = "Gene expression programming for prediction of scour depth downstream of sills", journal = "Journal of Hydrology", year = "2012", volume = "460-461", pages = "156--159", month = "16 " # aug, ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2012.06.034", URL = "http://www.sciencedirect.com/science/article/pii/S0022169412005197?v=s5", keywords = "genetic algorithms, genetic programming, gene expression programming, Grade control structures, local scour, water resources engineering", size = "4 pages", abstract = "Local scour is an important issue in environmental and water resources engineering in order to prevent degradation of river bed and safe the stability of grade control structures, stilling basins, aprons, ski-jump bucket spillways, bed sills, weirs, check dams, etc. This short communication presents Gene-Expression Programming (GEP), which is an extension to Genetic Programming (GP), as an alternative approach to predict scour depth downstream of sills. Published data were compiled from the literature for the scour depth downstream of sills. The proposed GEP approach gives satisfactory results (R2=0.967 and RMSE =0.088) compared to existing predictors [Chinnarasri and Kositgittiwong, ICE Water Management, 161(5), 267-275, 2008] with R2 =0.87 and RMSE= 2.452 for relative scour depth.", notes = "also known as \cite{MdAzamathulla2012}", } @InCollection{Azamathulla:2013:MWGTE, author = "H. Md Azamathulla", title = "A Review on Application of Soft Computing Methods in Water Resources Engineering", editor = "Xin-She Yang and Amir Hossein Gandomi and Amir Hossein Alavi", booktitle = "Metaheuristics in Water, Geotechnical and Transport Engineering", publisher = "Elsevier", address = "Oxford", year = "2013", chapter = "2", pages = "27--41", keywords = "genetic algorithms, genetic programming, gene expression programming, Water resources engineering, applied soft computing, artificial neural network, adaptive neuro-fuzzy inference system, scour, river stage", isbn13 = "978-0-12-398296-4", DOI = "doi:10.1016/B978-0-12-398296-4.00002-7", URL = "http://www.sciencedirect.com/science/article/pii/B9780123982964000027", abstract = "This chapter reviews the application of soft computing techniques, namely radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), gene-expression programming (GEP), and linear genetic programming (LGP) in water resources engineering. The capabilities of these techniques have been illustated by applying them to the prediction of scour downstream of flip spillway/bridge pier and abutment scour/pipeline scour/culvert scour/sediment load in hydraulics, and the river stage-discharge curve in hydrology. The accuracy of the results obtained by the soft computing techniques supports their further use for the prediction of hydraulic and hydrologic variables. Availability of free and easy-to-apply software for a specified method can invite a huge number of its applications by enthusiastic investigators.", } @Article{journals/nca/Azamathulla13, title = "Gene-expression programming to predict friction factor for Southern Italian rivers", author = "H. Md. Azamathulla", journal = "Neural Computing and Applications", year = "2013", number = "5", volume = "23", pages = "1421--1426", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, Rivers, Friction factor, Streams, Gravel-bed", bibdate = "2013-09-27", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#Azamathulla13", URL = "http://dx.doi.org/10.1007/s00521-012-1091-2", size = "6 pages", abstract = "This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R-squared = 0.958 and RMSE = 0.079) compared with existing predictors.", } @Article{journals/nca/AzamathullaAG13, title = "An expert system for predicting Manning's roughness coefficient in open channels by using gene expression programming", author = "H. Md. Azamathulla and Zulfequar Ahmad and Aminuddin {Ab. Ghani}", journal = "Neural Computing and Applications", year = "2013", number = "5", volume = "23", keywords = "genetic algorithms, genetic programming, gene expression programming, GEP", bibdate = "2013-11-19", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#AzamathullaAG13", pages = "1343--1349", URL = "http://dx.doi.org/10.1007/s00521-012-1078-z", size = "7 pages", abstract = "Manning's roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Accurate estimation of Manning's roughness coefficient is essential for the computation of flow rate, velocity. Conventional formulae that are greatly based on empirical methods lack in providing high accuracy for the prediction of Manning's roughness coefficient. Consequently, new and accurate techniques are still highly demanded. In this study, gene expression programming (GEP) is used to estimate the Manning roughness coefficient. The estimated value of the roughness coefficient is used in Mannings equation to compute the flow parameters in open-channel flows in order to carry out a comparison between the proposed GEP-based approach and the conventional ones. Results show that computed discharge using estimated value of roughness coefficient by GEP is in good agreement (10percent) with the experimental results compared to the conventional formulae (R-squared = 0.97 and RMSE = 0.0034 for the training data and Rsquared = 0.94 and RMSE = 0.086 for the testing data).", } @Article{azamathulla:2018:AWS, author = "H. Md. Azamathulla and Upaka Rathnayake and Ahmad Shatnawi", title = "Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia", journal = "Applied Water Science", year = "2018", volume = "8", number = "6", keywords = "genetic algorithms, genetic programming, Gene expression programming", URL = "http://link.springer.com/article/10.1007/s13201-018-0831-6", DOI = "doi:10.1007/s13201-018-0831-6", } @InProceedings{Azaraien:2017:CADS, author = "Abbas Azaraien and Babak Djalaei and Mostafa E. Salehi", booktitle = "2017 19th International Symposium on Computer Architecture and Digital Systems (CADS)", title = "Evolutionary architecture design for approximate DCT", year = "2017", abstract = "Discrete Cosine Transform (DCT) which has a major role in image and video compression has also a major role in power consumption. Approximate Computing let us trade precision to save power in error resilient applications such as multimedia. Therefore, DCT is a potential candidate for approximation. In this paper, we propose a method for evolutionary design of DCT architecture exploiting the inherent behaviour of DCT. Unlike the prior works on DCT approximation, which concentrated mostly on optimizing, replacing, or removing less effective building blocks of DCT, in our proposed method we use the evolutionary method to find new structures for DCT. According to the results, the evolution methods lead to architectures with less area and acceptable accuracy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CADS.2017.8310731", ISSN = "2325-937X", month = dec, notes = "Also known as \cite{8310731}", } @Article{Azarhoosh:2020:RMPD, author = "A. R. Azarhoosh and Z. Zojaji and F. {Moghadas Nejad}", title = "Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate", journal = "Road Materials and Pavement Design", year = "2020", volume = "21", number = "3", pages = "850--866", keywords = "genetic algorithms, genetic programming, fatigue life, indirect tensile fatigue test, ITF test, precipitated calcium carbonate, PCC, CaCO3", publisher = "Taylor \& Francis", DOI = "doi:10.1080/14680629.2018.1513372", size = "17 pages", abstract = "Fatigue cracking is the most important structural failure in flexible pavements. The results of a laboratory study evaluating the fatigue properties of mixtures containing precipitated calcium carbonate (PCC) using indirect tensile fatigue (ITF) test were investigated in this paper. The hot mix asphalt (HMA) samples were made with four PCC contents (0percent, 5percent, 10percent, and 15percent), and tested at three different testing temperatures (2degrees Celcius, 10degrees Celcius and 20degrees Celcius) and stress levels (100, 300, and 500 kPa). Due to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the fatigue life of asphalt pavement is difficult. In this study, genetic programming (GP) is used to predict the fatigue life of HMA. Based on the results of the ITF test, PCC improved the fatigue behaviour of studied mixes at different temperatures. But, the considerable negative effect of the increase of the temperature on the fatigue life of HMA is evident. On the other hand, the results indicate The GP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers.", notes = "p865 'The GP models are capable of predicting the fatigue life of asphalt mixtures with high accuracy.' Department of Civil Engineering, Faculty of Engineering, University of Bojnord, Bojnord, Iran", } @Article{AZARHOOSH:2019:US, author = "Mohammad Javad Azarhoosh and Rouein Halladj and Sima Askari and Abbas Aghaeinejad-Meybodi", title = "Performance analysis of ultrasound-assisted synthesized nano-hierarchical {SAPO-34} catalyst in the methanol-to-lights-olefins process via artificial intelligence methods", journal = "Ultrasonics Sonochemistry", volume = "58", pages = "104646", year = "2019", ISSN = "1350-4177", DOI = "doi:10.1016/j.ultsonch.2019.104646", URL = "http://www.sciencedirect.com/science/article/pii/S1350417719305103", keywords = "genetic algorithms, genetic programming, Ultrasound-assisted synthesis, Nano-hierarchical SAPO-34, MTO process, Multi-linear regression, Artificial neural network", abstract = "The present study has focused on performance analysis of ultrasound-assisted synthesized nano-hierarchical silico-alumino-phosphate-34 (SAPO-34) catalyst during methanol-to-light-olefins (MTO) process. A classical method, i.e., multiple linear regression (MLR) and two intelligent methods, i.e., genetic programming (GP) and artificial neural networks (ANN) were used for modeling of the performance of nano-hierarchical SAPO-34 catalyst. We studied the influence of basic parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst such as crystallization time, ultrasonic irradiation time, ultrasonic intensity, amount of organic template (diethylamine (DEA) and carbon nanotube (CNT)) on its performance (methanol conversion and light olefins selectivity) in MTO process. The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, GP models were used in the following to predict the effect of main parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst. Finally, an optimal catalyst with the highest yield into light olefins was predicted using the genetic optimization algorithm. The genetic models were employed as an evaluation function in the genetic algorithm (GA). A good agreement between the outputs of GP models for the optimal catalyst and experimental results were obtained. The optimal ultrasound-assisted synthesized nano-hierarchical SAPO-34 was accompanied by light olefins selectivity of 77percent and methanol conversion of 94percent from the onset of the process after", } @Article{azarhoosh:2020:AJSE, author = "Alireza Azarhoosh and Salman Pouresmaeil", title = "Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming", journal = "Arabian Journal for Science and Engineering", year = "2020", volume = "45", number = "10", pages = "8427--8441", keywords = "genetic algorithms, genetic programming, Hot-mix asphalt, Marshall mix design, Index of aggregate particle shape and texture (particle index), Viscosity of bitumen, Genetic programming method", URL = "http://link.springer.com/article/10.1007/s13369-020-04776-0", DOI = "doi:10.1007/s13369-020-04776-0", size = "15 pages", abstract = "The mix design of asphalt concrete is usually accomplished in the Iranian ministry of road and transportation according to the Marshall method. Marshall mix design parameters are a function of grading and properties of aggregates, amount and type of bitumen in asphalt mixtures. Therefore, in order to determine these parameters and the optimum bitumen content, many samples with different compounds and conditions must be manufactured and tested in the laboratory, a process that requires considerable time and cost. Accordingly, the necessity of using new and advanced methods for the design and quality control of asphalt mixtures is becoming more and more evident. Therefore, in this study, a genetic programming simulation method was employed to predict the Marshall mix design parameters of asphalt mixtures. Also, multiple linear regression models were adopted as the base model to evaluate the models presented by the genetic programming method. The models proposed here predict the Marshall mix design parameters based on parameters such as the index of aggregate particle shape and texture, the amount and viscosity of the bitumen. The results demonstrated that the proposed methods are more efficient than the costly laboratory method, and genetic programming models with minimal error (identified in this study with RMSE and MAE parameters) and correlation coefficients > 0.9 can predict relatively accurate Marshall mix design parameters.", } @Article{AZARI:2020:AR, author = "Aryandokht Azari and Hossein Tavakoli and Brian D. Barkdoll and Omid Bozorg Haddad", title = "Predictive model of algal biofuel production based on experimental data", journal = "Algal Research", volume = "47", pages = "101843", year = "2020", ISSN = "2211-9264", DOI = "doi:10.1016/j.algal.2020.101843", URL = "http://www.sciencedirect.com/science/article/pii/S2211926419309087", keywords = "genetic algorithms, genetic programming, Bioenergy, Algae, Optimization, CO biofixation rate, Photobioreactors", abstract = "Algal biofuels are of growing interest in the quest to reduce carbon emissions in the atmosphere but the sensitivity of the fuel production to various factors is not well understood. Therefore, the effects of temperature, light intensity, carbon concentration, aeration rate, pH, and time on the CO2 biofixation rate of Chlorella vulgaris (ISC-23) were investigated using experimental, and Genetic Programming (GP) modeling techniques. The impacts of applying the cement industrial flue gas as a source of carbon, useful for the growth of microalgae, were also studied. Chlorella vulgaris (ISC-23) was cultivated in a laboratory photobioreactor on a BG-11 medium. The developed GP model was used to optimize the CO2 biofixation based on the studied variables and produce a predictive equation. By using statistical measurements and error analysis, the predictive equation was shown to agree with the experimentally obtained values. It was found that the optimum conditions occur at 26o C, and 3200 lx of light, in the existence of CO2. Applying 6percent CO2 as the input with the aeration rate of 0.5 vvm in 11 days was also reported as the optimum scenario for algae production with keeping the pH close to 7.5. The results indicate that the predictions determined with the proposed equation can be of practical worth for researchers and experts in the biofuel industry", } @InProceedings{Azari:2018:CEC, author = "Samaneh Azari and Mengjie Zhang and Bing Xue and Lifeng Peng", title = "Genetic Programming for Preprocessing Tandem Mass Spectra to Improve the Reliability of Peptide Identification", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2018.8477810", abstract = "Tandem mass spectrometry (MS/MS) is currently the most commonly used technology in proteomics for identifying proteins in complex biological samples. Mass spectrometers can produce a large number of MS/MS spectra each of which has hundreds of peaks. These peaks normally contain background noise, therefore a preprocessing step to filter the noise peaks can improve the accuracy and reliability of peptide identification. This paper proposes to preprocess the data by classifying peaks as noise peaks or signal peaks, i.e., a highly-imbalanced binary classification task, and uses genetic programming (GP) to address this task. The expectation is to increase the peptide identification reliability. Meanwhile, six different types of classification algorithms in addition to GP are used on various imbalance ratios and evaluated in terms of the average accuracy and recall. The GP method appears to be the best in the retention of more signal peaks as examined on a benchmark dataset containing 1, 674 MS/MS spectra. To further evaluate the effectiveness of the GP method, the preprocessed spectral data is submitted to a benchmark de novo sequencing software, PEAKS, to identify the peptides. The results show that the proposed method improves the reliability of peptide identification compared to the original un-preprocessed data and the intensity-based thresholding methods.", notes = "WCCI2018", } @InProceedings{DBLP:conf/acpr/AzariXZP19, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", editor = "Shivakumara Palaiahnakote and Gabriella Sanniti di Baja and Liang Wang and Wei Qi Yan", title = "A Decomposition Based Multi-objective Genetic Programming Algorithm for Classification of Highly Imbalanced Tandem Mass Spectrometry", booktitle = "Pattern Recognition - 5th Asian Conference, {ACPR} 2019, Auckland, New Zealand, November 26-29, 2019, Revised Selected Papers, Part {II}", series = "Lecture Notes in Computer Science", volume = "12047", pages = "449--463", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-41299-9_35", DOI = "doi:10.1007/978-3-030-41299-9_35", timestamp = "Mon, 24 Feb 2020 18:06:33 +0100", biburl = "https://dblp.org/rec/conf/acpr/AzariXZP19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{DBLP:conf/pricai/Azari0ZP19, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", editor = "Abhaya C. Nayak and Alok Sharma", title = "Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-Based Scoring Function for Re-ranking Peptide-Spectrum Matches", booktitle = "{PRICAI} 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part {III}", series = "Lecture Notes in Computer Science", volume = "11672", pages = "474--487", publisher = "Springer", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/978-3-030-29894-4_38", DOI = "doi:10.1007/978-3-030-29894-4_38", URL = "http://arxiv.org/abs/1908.08010", timestamp = "Wed, 25 Sep 2019 18:21:16 +0200", biburl = "https://dblp.org/rec/conf/pricai/Azari0ZP19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", notes = "See also arXiv abs/1908.08010 \cite{DBLP:journals/corr/abs-1908-08010}", } @InProceedings{Azari:2019:CEC, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", title = "Learning to Rank Peptide-Spectrum Matches Using Genetic Programming", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "3244--3251", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ranking function, peptide-spectrum match, tandem mass spectrometry", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790049", size = "8 pages", abstract = "The analysis of tandem mass spectrometry (MS/MS) proteomics data relies on automated methods that assign peptides to observed MS/MS spectra. Typically these methods return a list of candidate peptide-spectrum matches (PSMs), ranked according to a scoring function. Normally the highest-scoring candidate peptide is considered as the best match for each spectrum. However, these best matches do not necessary always indicate the true matches. Identifying a full-length correct peptide by peptide identification tools is crucial, and we do not want to assign a spectrum to the peptide which is not expressed in the given biological sample. Therefore in this paper, we present a new approach to improving the previous ordering/ranking of the PSMs, aiming at bringing the correct PSM for spectrum ahead of all the incorrect ones for the same spectrum. We develop a new method called GP-PSM-rank, which employs genetic programming (GP) to learn a ranking function by combining different feature functions", notes = "also known as \cite{8790049} IEEE Catalog Number: CFP19ICE-ART", } @Article{azari:2019:JASMS, author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng", title = "Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification", journal = "Journal of The American Society for Mass Spectrometry", year = "2019", volume = "30", number = "7", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13361-019-02196-5", DOI = "doi:10.1007/s13361-019-02196-5", } @PhdThesis{Azari:thesis, author = "Samaneh Azari", title = "Evolutionary Algorithms for Improving De Novo Peptide Sequencing", school = "Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10063/8898", URL = "https://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/8898/thesis_access.pdf", size = "268 pages", abstract = "De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.", notes = "supervisors: Mengjie Zhang, Bing Xue, Lifeng Peng", } @InProceedings{Azaria:2016:GPTP, author = "Itay Azaria and Achiya Elyasaf and Moshe Sipper", title = "Evolving Artificial General Intelligence for Video Controllers", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "53--63", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Hyper-Heuristic", isbn13 = "978-3-319-97087-5", URL = "https://www.cs.bgu.ac.il/~sipper/publications/Evolving%20Artificial%20General%20Intelligence.pdf", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_4", size = "12 pages", abstract = "The General Video Game Playing Competition (GVGAI) defines a challenge of creating controllers for general video game playing, a testbed, as it were-for examining the issue of artificial general intelligence. We develop herein a game controller that mimics human-learning behaviour, focusing on the ability to generalize from experience and diminish learning time as new games present themselves. We use genetic programming to evolve hyper heuristic-based general players, our results showing the effectiveness of evolution in meeting the generality challenge.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @InProceedings{eurogp:AzariaS05, author = "Yaniv Azaria and Moshe Sipper", editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. {van Hemert} and Marco Tomassini", title = "{GP-Gammon}: Using Genetic Programming to Evolve Backgammon Players", booktitle = "Proceedings of the 8th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3447", year = "2005", address = "Lausanne, Switzerland", month = "30 " # mar # " - 1 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-25436-6", pages = "132--142", DOI = "doi:10.1007/978-3-540-31989-4_12", DOI = "doi:10.1007/b107383", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "We apply genetic programming to the evolution of strategies for playing the game of backgammon. Pitted in a 1000-game tournament against a standard benchmark player---Pubeval---our best evolved program wins 58\% of the games, the highest verifiable result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach.", notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005", } @Article{azaria:2005:GPEM, author = "Yaniv Azaria and Moshe Sipper", title = "{GP-Gammon}: Genetically Programming Backgammon Players", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "3", pages = "283--300", month = sep, note = "Published online: 12 August 2005", keywords = "genetic algorithms, genetic programming, backgammon, self-learning, STGP, demes, coevolution", ISSN = "1389-2576", URL = "http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf", URL = "https://rdcu.be/c7iTQ", DOI = "doi:10.1007/s10710-005-2990-0", size = "18 pages", abstract = "We apply genetic programming to the evolution of strategies for playing the game of backgammon. We explore two different strategies of learning: using a fixed external opponent as teacher, and letting the individuals play against each other. We conclude that the second approach is better and leads to excellent results: Pitted in a 1000-game tournament against a standard benchmark player Pubeval our best evolved program wins 62.4 percent of the games, the highest result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach.", notes = "ECJ", } @InProceedings{Azevedo:2016:CSCI, author = "Leonardo Azevedo and Ruben Nunes and Amilcar Soares", booktitle = "2016 International Conference on Computational Science and Computational Intelligence (CSCI)", title = "Genetic Programming in Geostatistical Reservoir Geophysics", year = "2016", pages = "1208--1213", abstract = "Hydrocarbon reservoir modelling and characterisation is a critical step for the success of oil and/or gas exploration and production projects. Reservoir modelling is frequently based on the results provided by geostatistical seismic inversion techniques. These procedures are computationally heavy and expensive even for small-to-medium size fields due to the use of stochastic sequential simulation as the model perturbation technique. This work proposes the use of machine learning techniques, specifically symbolic regression, a category from the group of genetic programming methodologies, as a proxy to surpass the need of stochastic sequential simulation without compromising the advantage of using these simulation methodologies, for example uncertainty assessment of the property of interest. The proposed methodology is illustrated with an application example to a real case study and the results compared with the traditional geostatistical seismic inversion approach.", keywords = "genetic algorithms, genetic programming, Computational modelling, Correlation coefficient, Data models, Iterative methods, Mathematical model, Reflection, Stochastic processes, genetic programming geostatistical seismic inversion seismic reservoir characterisation", DOI = "doi:10.1109/CSCI.2016.0228", month = dec, notes = "Also known as \cite{7881521}", } @InProceedings{Azimlu:2019:ICCSE, author = "Fateme Azimlu and Shahryar Rahnamayan and Masoud Makrehchi and Naveen Kalra", booktitle = "2019 14th International Conference on Computer Science Education (ICCSE)", title = "Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models", year = "2019", pages = "785--791", month = aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCSE.2019.8845381", ISSN = "2473-9464", abstract = "Prediction is one of the most important tasks in the machine learning field. Data scientists employ various learning methods to find the most appropriate and accurate model for each family of applications or dataset. This study compares the symbolic regression using genetic programming (GP), with conventional machine learning techniques. In cases it is required to model an unknown, poorly understood, and/or complicated system. In these cases, we use genetic programming to generate a symbolic model without using any pre-known model. In this paper, the GP is studied as a tool for prediction in different types of datasets and conducted experiments to verify the superiority of GP over conventional models in certain conditions and datasets. The accuracy of GP-based regression results are compared with other machine learning techniques, and are found to be more accurate in certain conditions.", notes = "Also known as \cite{8845381}", } @InProceedings{Azimlu:2021:RWACMO, author = "Fateme Azimlu and Shahryar Rahnamayan and Masoud Makrehchi", title = "House Price Prediction Using Clustering and Genetic Programming along with Conducting a Comparative Study", booktitle = "Real-World Applications of Continuous and Mixed-integer Optimization", year = "2021", editor = "Kazuhisa Chiba and Akira Oyama and Pramudita Satria Palar and Koji Shimoyama and Hemant K. Singh", pages = "1809--1816", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic Regression, Regression, Machine Learning, Clustering, Multi-level-model, House Price Prediction", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3463141", size = "8 pages", abstract = "One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation; however, this approach is applicable to other large datasets.", notes = "University of Ontario Institute of Technology GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Aziz:2016:EuroGP, author = "Benjamin Aziz and Mohamed Bader and Cerana Hippolyte", title = "Search-Based SQL Injection Attacks Testing using Genetic Programming", booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming", year = "2016", month = "30 " # mar # "--1 " # apr, editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim", series = "LNCS", volume = "9594", publisher = "Springer Verlag", address = "Porto, Portugal", pages = "183--198", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Search-Based Testing, SQL Injections", isbn13 = "978-3-319-30668-1", DOI = "doi:10.1007/978-3-319-30668-1_12", abstract = "Software testing is a key phase of many development methodologies as it provides a natural opportunity for integrating security early in the software development lifecycle. However despite the known importance of software testing, this phase is often overlooked as it is quite difficult and labour-intensive to obtain test datasets to effectively test an application. This lack of adequate automatic software testing renders software applications vulnerable to malicious attacks after they are deployed as detected software vulnerabilities start having an impact during the production phase. Among such attacks are SQL injection attacks. Exploitation of SQL injection vulnerabilities by malicious programs could result in severe consequences such as breaches of confidentiality and false authentication. We present in this paper a search-based software testing technique to detect SQL injection vulnerabilities in software applications. This approach uses genetic programming as a means of generating our test datasets, which are then used to test applications for SQL injection-based vulnerabilities.", notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016", } @Article{Aziz:2016:GPEM, author = "Mahdi Aziz and Mohammad-H Tayarani-N and Mohammad R. Meybodi", title = "A two-objective memetic approach for the node localization problem in wireless sensor networks", journal = "Genetic Programming and Evolvable Machines", year = "2016", volume = "17", number = "4", pages = "321--358", month = dec, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9274-8", size = "38 pages", abstract = "Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then used for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes.", notes = "http://www.dennisweyland.net/blog/?p=12 doi:10.4018/jamc.2010040104", } @InProceedings{Azmi:2020:Morgeo, author = "Rida Azmi and Hicham Amar and Abderrahim Norelyaqine", booktitle = "2020 IEEE International conference of Moroccan Geomatics (Morgeo)", title = "Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming", year = "2020", keywords = "genetic algorithms, genetic programming, Remote sensing, High resolution, Data Mining", DOI = "doi:10.1109/Morgeo49228.2020.9121914", month = may, size = "6 pages", abstract = "Object based image analysis techniques give accurate results when a good knowledge base is extracted from remote sensing imagery. Data mining algorithms and especially evolutionary process can extract useful knowledge that can be used in different fields. In this paper, object-oriented classification was used, more particularly object-based image analysis approach (OOIA) to classify a large feature space composed of a very high spatial resolution satellite image (VHR). Genetic programming (GP) concept was applied to extract classification rules with an induction form. Comparison of the performance of three GP algorithms (Bojarczuc_GP, Falco_GP and Tan_GP) was mad using JCLEC Framework. Results showed two main conclusions. 1) testing and evaluation of the generated rules allow us to discover that GP algorithms can classify and extract useful knowledge from VHR satellite data. 2) evaluation of the performance of the three Genetic programming models demonstrates that the Bojarczuk model is efficient on accuracy classification than the Falco and Tan models.", notes = "Faculty of Sciences of Rabat, Mohamed V University, Rabat, Morocco Also known as \cite{9121914}", } @InProceedings{Azzali:2019:EuroGP, author = "Irene Azzali and Leonardo Vanneschi and Sara Silva and Illya Bakurov and Mario Giacobini", title = "A Vectorial Approach to Genetic Programming", booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming", year = "2019", month = "24-26 " # apr, editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco", series = "LNCS", volume = "11451", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "213--227", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Vector-based representation, Panel Data regression: Poster", isbn13 = "978-3-030-16669-4", URL = "https://hdl.handle.net/2318/1725688", URL = "https://iris.unito.it/retrieve/e27ce42f-33ca-2581-e053-d805fe0acbaa/Azzali.pdf", URL = "https://www.springer.com/us/book/9783030166694", DOI = "doi:10.1007/978-3-030-16670-0_14", size = "16 pages", abstract = "Among the various typologies of problems to which Genetic Programming (GP) has been applied since its origins, symbolic regression is one of the most popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a panel representation where each observation corresponds to a collection on a subject at a precise time instant. However, representing data in this form may imply a loss of information: for instance, the algorithm may not be able to recognize observations belonging to the same subject and their recording order. To maintain the source of knowledge supplied by ordered sequences as time series, we propose a new approach to GP that keeps instances of the same observation together in a vector, introducing vectorial variables as terminals. This new representation allows aggregate functions in the primitive GP set, included with the purpose of describing the behaviour of vectorial variables. In this work, we perform a comparative analysis of vectorial GP (VE-GP) against standard GP (ST-GP). Experiments are conducted on different benchmark problems to highlight the advantages of this new approach.", notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @Article{Azzali:GPEM, author = "Irene Azzali and Leonardo Vanneschi and Andrea Mosca and Luigi Bertolotti and Mario Giacobini", title = "Towards the use of genetic programming in the ecological modelling of mosquito population dynamics", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "4", pages = "629--642", month = dec, keywords = "genetic algorithms, genetic programming, West Nile Virus, WNV, Ecological modelling, Machine learning, Regression", ISSN = "1389-2576", URL = "https://iris.unito.it/retrieve/handle/2318/1722575/562795/Manuscript.pdf", URL = "https://rdcu.be/cQCew", DOI = "doi:10.1007/s10710-019-09374-0", size = "14 pages", abstract = "Predictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.", } @InProceedings{Azzali:2020:EuroGP, author = "Irene Azzali and Leonardo Vanneschi and Mario Giacobini", title = "Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "52--67", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Vector-based genetic programming, Time series, Sliding windows, Geometric semantic operators", isbn13 = "978-3-030-44093-0", DOI = "doi:10.1007/978-3-030-44094-7_4", abstract = "Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.", notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @Article{AZZALI:2020:ASC, author = "Irene Azzali and Leonardo Vanneschi and Illya Bakurov and Sara Silva and Marco Ivaldi and Mario Giacobini", title = "Towards the use of vector based {GP} to predict physiological time series", journal = "Applied Soft Computing", volume = "89", pages = "106097", year = "2020", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2020.106097", URL = "http://www.sciencedirect.com/science/article/pii/S1568494620300375", keywords = "genetic algorithms, genetic programming, Ventilation, Physiological data, Machine learning, Time series", abstract = "Prediction of physiological time series is frequently approached by means of machine learning (ML) algorithms. However, most ML techniques are not able to directly manage time series, thus they do not exploit all the useful information such as patterns, peaks and regularities provided by the time dimension. Besides advanced ML methods such as recurrent neural network that preserve the ordered nature of time series, a recently developed approach of genetic programming, VE-GP, looks promising on the problem in analysis. VE-GP allows time series as terminals in the form of a vector, including new strategies to exploit this representation. In this paper we compare different ML techniques on the real problem of predicting ventilation flow from physiological variables with the aim of highlighting the potential of VE-GP. Experimental results show the advantage of applying this technique in the problem and we ascribe the good performances to the ability of properly catching meaningful information from time series", } @InProceedings{Azzali:2022:evoapplications, author = "Irene Azzali and Nicole Dalia Cilia and Claudio {De Stefano} and Francesco Fontanella and Mario Giacobini and Leonardo Vanneschi", title = "Vectorial {GP for Alzheimer's} Disease Prediction Through Handwriting Analysis", booktitle = "25th International Conference, EvoApplications 2022", year = "2022", month = "20-22 " # apr, editor = "Juan Luis Jimenez Laredo and J. Ignacio Hidalgo and Kehinde Oluwatoyin Babaagba", series = "LNCS", volume = "13224", publisher = "Springer", address = "Madrid", pages = "517--530", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Alzheimer disease, Artificial intelligence, Handwriting analysis, Vectorial genetic programming", isbn13 = "978-3-031-02461-0", DOI = "doi:10.1007/978-3-031-02462-7_33", abstract = "Alzheimer Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE_GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE_GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the Last part of the paper.", notes = "Also (?) presented at WIVACE 2022 XVI International Workshop on Artificial Life and Evolutionary Computation Gaeta (LT), Italy September 14-16, 2022 http://wivace2022.unicas.it/files/programWIVACE2022.pdf http://www.evostar.org/2022/ EvoApplications2022 held in conjunction with EuroGP'2022, EvoCOP2022 and EvoMusArt2022", } @Article{AZZALI:2024:swevo, author = "Irene Azzali and Nicole D. Cilia and Claudio {De Stefano} and Francesco Fontanella and Mario Giacobini and Leonardo Vanneschi", title = "Automatic feature extraction with Vectorial Genetic Programming for Alzheimer's Disease prediction through handwriting analysis", journal = "Swarm and Evolutionary Computation", volume = "87", pages = "101571", year = "2024", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2024.101571", URL = "https://www.sciencedirect.com/science/article/pii/S2210650224001093", keywords = "genetic algorithms, genetic programming, Vectorial Genetic Programming, Alzheimer's Disease, Machine learning, Healthcare applications", abstract = "Alzheimer's Disease (AD) is an incurable neurodegenerative disease that strongly impacts the lives of the people affected. Even if, to date, there is no cure for this disease, its early diagnosis helps to manage the course of the disease better with the treatments currently available. Even more importantly, an early diagnosis will also be necessary for the new treatments available in the future. Recently, machine learning (ML) based tools have demonstrated their effectiveness in recognizing people's handwriting in the early stages of AD. In most cases, they use features defined by using the domain knowledge provided by clinicians. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is an enhanced version of GP that can manage time series directly. We applied VE_GP to data collected using an experimental protocol, which was defined to collect handwriting data to support the development of ML tools for the early diagnosis of AD based on handwriting analysis. The experimental results confirmed the effectiveness of the proposed approach in terms of classification performance, size, and simplicity", } @InProceedings{conf/adma/AzzawiHAXAA17, title = "Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets", author = "Hasseeb Azzawi and Jingyu Hou and Russul Alanni and Yong Xiang and Rana Abdu-Aljabar and Ali Azzawi", booktitle = "Advanced Data Mining and Applications - 13th International Conference, ADMA 2017, Singapore, November 5-6, 2017, Proceedings", editor = "Gao Cong and Wen-Chih Peng and Wei Emma Zhang and Chengliang Li and Aixin Sun", publisher = "Springer", year = "2017", volume = "10604", pages = "541--553", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2017-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/adma/adma2017.html#AzzawiHAXAA17", isbn13 = "978-3-319-69178-7", DOI = "doi:10.1007/978-3-319-69179-4_38", } @Article{Azzini:2011:IA, author = "Antonia Azzini and Andrea G. B. Tettamanzi", title = "Evolutionary {ANNs}: A state of the art survey", journal = "Intelligenza Artificiale", year = "2011", volume = "5", number = "1", pages = "19--35", keywords = "genetic algorithms, genetic programming, ANN, Neural Networks, Classification", ISSN = "1724-8035", broken = "http://iospress.metapress.com/content/T144511510806253", DOI = "doi:10.3233/IA-2011-0002", size = "17 pages", abstract = "Neuro-genetic systems have become a very important topic of study in evolutionary computation in recent years. They are models that use evolutionary algorithms to optimise neural network design. This article is a survey of the state of art of evolutionary ANN systems, with a focus on the most recent developments, presented in the literature during the last decade. The main purpose of this work is to provide an update and extension of Yao's milestone survey, published back in 1999, by taking the most recent literature into account.", notes = "Special issue: Selected papers from the 15 International Conference of the Italian Association for Artificial Intelligence Marco Maratea, Giovanni Adorni, Stefano Cagnoni and Marco Gori", } @InProceedings{B:2021:ICIRCA, author = "Akaramuthalvi J B and Suja Palaniswamy", title = "Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction", booktitle = "2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)", year = "2021", pages = "1533--1537", month = sep, keywords = "genetic algorithms, genetic programming, TPOT", DOI = "doi:10.1109/ICIRCA51532.2021.9544863", abstract = "Breast cancer is a type of cancer in which the breast cells grow out of control. It is one of the leading cause for the high pace of death in women. Breast cancer classification is mainly done with the help of Machine Learning (ML) algorithms. In this work, we did a comparative analysis by creating a framework using ML and Auto ML algorithms (genetic programming) to accurately classify the cells in the breast as cancerous or non-cancerous. The work focused on automating and optimizing the algorithms for better prediction of cancerous cells. In Auto ML, Tree- based Pipeline Optimization Tool (TPOT), a genetic programming approach is used for finding the suitable classifiers and to automatically select the significant features and parameter values associated with the classifiers. Wisconsin Breast cancer diagnostic dataset, which comprises of digitized images taken from fine needle aspirate of breast mass has been used in this work. Evaluation based on recall, precision and accuracy have showed good results.", notes = "Also known as \cite{9544863} Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India", } @Article{Baareh:2018:IJACSA, author = "Abdel Karim Baareh", title = "Evolutionary Design of a Carbon Dioxide Emission Prediction Model using Genetic Programming", journal = "International Journal of Advanced Computer Science and Applications", year = "2018", number = "3", volume = "9", pages = "298--303", keywords = "genetic algorithms, genetic programming, fossil fuels, carbon emission, forecasting", publisher = "The Science and Information (SAI) Organization", bibsource = "OAI-PMH server at thesai.org", language = "eng", oai = "oai:thesai.org:10.14569/IJACSA.2018.090341", URL = "http://thesai.org/Downloads/Volume9No3/Paper_41-Evolutionary_Design_of_a_Carbon_Dioxide_Emission.pdf", DOI = "doi:10.14569/IJACSA.2018.090341", size = "6 pages", abstract = "Weather pollution is considered as one of the most important, dangerous problem that affects our life and the society security from the different sides. The global warming problem affecting the atmosphere is related to the carbon dioxide emission (CO2) from the different fossil fuels along with temperature. In this paper, this phenomenon is studied to find a solution for preventing and reducing the poison CO2 gas emerged from affecting the society and reducing the smoke pollution. The developed model consists of four input attributes: the global oil, natural gas, coal, and primary energy consumption and one output the CO2 gas. The stochastic search algorithm Genetic Programming (GP) was used as an effective and robust tool in building the forecasting model. The model data for both training and testing cases were taken from the years of 1982 to 2000 and 2003 to 2010, respectively. According to the results obtained from the different evaluation criteria, it is nearly obvious that the performance of the GP in carbon gas emission estimation was very good and efficient in solving and dealing with the climate pollution problems.", } @InProceedings{Baars:2011:FedCSIS, author = "Arthur I. Baars and Kiran Lakhotia and Tanja E. J. Vos and Joachim Wegener", title = "Search-based testing, the underlying engine of Future Internet testing", booktitle = "Federated Conference on Computer Science and Information Systems (FedCSIS 2011)", year = "2011", pages = "917--923", address = "Szczecin", month = "18-21 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, Evolutionary computation, Internet, Optimisation, Search problems, Software, Testing, future Internet testing, search-based testing, software testing, time to market, evolutionary testing", isbn13 = "978-1-4577-0041-5", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6078178", size = "7 pages", abstract = "The Future Internet will be a complex interconnection of services, applications, content and media, on which our society will become increasingly dependent. Time to market is crucial in Internet applications and hence release cycles grow ever shorter. This, coupled with the highly dynamic nature of the Future Internet will place new demands on software testing. Search-Based Testing is ideally placed to address these emerging challenges. Its techniques are highly flexible and robust to only partially observable systems. This paper presents an overview of Search-Based Testing and discusses some of the open challenges remaining to make search-based techniques applicable to the Future Internet.", notes = "Brief mention of GP on page 919, mostly GAs Also known as \cite{6078178}", } @Article{Babaelahi:2016:Energy, author = "Mojtaba Babaelahi and Hoseyn Sayyaadi", title = "Analytical closed-form model for predicting the power and efficiency of Stirling engines based on a comprehensive numerical model and the genetic programming", journal = "Energy", volume = "98", pages = "324--339", year = "2016", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2016.01.031", URL = "http://www.sciencedirect.com/science/article/pii/S0360544216000505", abstract = "High accuracy and simplicity in use are two important required features of thermal models of Stirling engines. A new numerical second-order thermal model was presented through the improvement of our previous modified-PSVL model in order to have an elevated accuracy. The modified-PSVL model was modified by considering a non-isothermal model for heater and cooler. Then, the model called as CPMS-Comprehensive Polytropic Model of Stirling engine, was used to simulate the GPU-3 Stirling engine, and the obtained results were compared with those of the previous thermal models as well as the experimental data. For the sack of the simplicity, the combination of the CPMS model and genetic programming was employed to generate analytical closed-form correlation. In this regards, a comprehensive data bank of results of the CPMS was constructed and exported to the GP tool and analytical expressions of the power, efficiency, and polytropic indexes were obtained. It was shown that the analytical correlations not only had the same accuracy as the CPMS model, but also, it can be simply used without difficulties of numerical models. The CPMS and its out coming analytical expressions, predicted the power and efficiency of the GPU-3 Stirling with +1.13percent and +0.45 (as difference), respectively.", keywords = "genetic algorithms, genetic programming, Closed-form model, Comprehensive numerical model of Stirling engines, CPMS model, Non-isothermal heat exchangers, Polytropic model", } @Article{BABAELAHI:2017:EML, author = "Mojtaba Babaelahi and Hamed Eshraghi", title = "Optimum analytical design of medical heat sink with convex parabolic fin including variable thermal conductivity and mass transfer", journal = "Extreme Mechanics Letters", volume = "15", pages = "83--90", year = "2017", keywords = "genetic algorithms, genetic programming, Medical heat sink, Convex parabolic, Variable thermal conductivity, Fractional, Generalizes differential transformation method (GDTM)", ISSN = "2352-4316", DOI = "doi:10.1016/j.eml.2017.06.005", URL = "http://www.sciencedirect.com/science/article/pii/S2352431616302826", abstract = "Electronic medical devices have become more powerful in recent years. These medical devices contain arrays of electronic components, which required high-performance heat sinks to prevent from overheating and damaging. For the design of high-performance medical heat sinks, the temperature distribution should be evaluated. Thus, in this paper, the Generalized Differential Transformation Method (GDTM) is applied to the medical heat sink with a convex parabolic convective fin with variable thermal conductivity and mass transfer. In the first section of the current paper, the general heat balance equation related to the medical heat sink with convex parabolic fins is derived. Because of the fractional type of derivative, the concept of GDTM is employed to derive analytical solutions. The major aim of this study, which is exclusive for this article, is to find the closed-form analytical solution for the fractional differential equation in considered heat sink for the first time. In the next step, multiobjective optimization of the considerable fin is performed for minimum volume and maximum thermal efficiency. For evaluation of optimum design at various environmental conditions, the multiobjective optimizations are performed for a wide range of environmental conditions. In the final step, the results of multiobjective optimization in various environmental conditions are applied to the genetic programming tool and suitable analytical correlations are created for optimum geometrical design", keywords = "genetic algorithms, genetic programming, Medical heat sink, Convex parabolic, Variable thermal conductivity, Fractional, Generalizes differential transformation method (GDTM)", } @Article{BABAELAHI:2020:SETA, author = "Mojtaba. Babaelahi and Hamed. Jafari", title = "Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept", journal = "Sustainable Energy Technologies and Assessments", volume = "42", pages = "100845", year = "2020", ISSN = "2213-1388", DOI = "doi:10.1016/j.seta.2020.100845", URL = "https://www.sciencedirect.com/science/article/pii/S2213138820312728", keywords = "genetic algorithms, genetic programming, Solar, Micro gas turbine, Exergoeconomic, Environmental, Particle swarm optimization", abstract = "One of the crucial problems in the power systems is the selection of energy-efficient systems with suitable efficiency, cost, and environmental performance. Accordingly, this paper introduces a new power generation system that supplies a significant part of the required energy from solar energy and uses liquefied natural gas (LNG) fuel as an auxiliary source. To evaluation of the system, exergo-enviro-economic analysis and thermohydraulic design of are performed using Matlab code. A comparison of the governed results with the base cycle (ThermoFlex simulation) shows good improvement in exergy efficiency fuel consumption. Since the preparation of an analytical model has a practical effect on the selection of optimum configuration, an analytical model for objective functions is provided based on the exergoeconomic and environmental numerical model. For this analytical model, A large data bank from the numerical simulation results is obtained, and the artificial intelligence tool known as Genetic Programming is used for multivariate fitting. Finally, to find the optimal configuration, various optimizations (using the particle swarm optimization) have been made, and the final optimal design has been selected. The results indicated that the thermal and exergetic efficiencies in the ultimate optimum point increased about 6.252 and 8.842 percent, respectively", } @Article{Babanajad:2013:AiC, author = "Saeed K. Babanajad and Amir H. Gandomi and Danial {Mohammadzadeh S.} and Amir H. Alavi", title = "Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming", journal = "Automation in Construction", year = "2013", volume = "36", pages = "136--144", month = dec, keywords = "genetic algorithms, genetic programming, Discipulus, Multiaxial compression, Compressive strength, Ultimate strength, Linear genetic programming", ISSN = "0926-5805", URL = "http://www.sciencedirect.com/science/article/pii/S0926580513001301", DOI = "doi:10.1016/j.autcon.2013.08.016", size = "9 pages", abstract = "New numerical models are developed to predict the strength of concrete under multi-axial compression using linear genetic programming (LGP). The models are established based on a comprehensive database obtained from the literature. To verify the applicability of the derived models, they are employed to estimate the strength of parts of the test results that are not included in the modelling process. The external validation of the model is further verified using several statistical criteria. The results obtained by the proposed models are much better than those provided by several models found in the literature. The LGP-based equations are remarkably straightforward and useful for pre-design applications.", } @InCollection{Babanajad:2015:hbgpa, author = "Saeed K. Babanajad", title = "Application of Genetic Programming for Uniaxial and Multiaxial Modeling of Concrete", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "16", pages = "399--430", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_16", abstract = "In current chapter, an overview of recently established genetic programming based techniques for strength modelling of concrete has been presented. The comprehensive uniaxial and multiaxial strengths modelling of hardened concrete have been concentrated in this chapter as one of the main area of interests in concrete modeling for structural engineers. For this engineering case the literature has been reviewed and the most applied numerical/analytical/experimental models and national building codes have been introduced. After reviewing the artificial intelligence/machine learning based models, genetic programming based models are presented, with accent on the applicability and efficiency of each model and its suitability. The advantages and weaknesses of the aforementioned models are summarized and compared with existing numerical/analytical/experimental models and national building codes, and a few illustrative examples briefly are presented. The genetic programming based techniques are remarkably straightforward and have enabled reliable, stable, and robust tools for pre-design and design applications.", } @PhdThesis{Babanajad:thesis, author = "Saeed Karim Baba Najad Mamaghani", title = "Methods for Sensing, Analysis and Computation of Loads and Distributed Damage in Bridges", school = "College of Engineering, University of Illinois at Chicago", year = "2016", address = "USA", keywords = "genetic algorithms, genetic programming, Bridge Weigh in Motion, Damage Detection, Discrete Sensing, Fibre Optic Sensors, Distributed Sensing", URL = "https://dspace-prod.lib.uic.edu/bitstream/handle/10027/20220/Karimbabanajadmamaghani_Saeed.pdf", URL = "https://indigo.uic.edu/handle/10027/20220", URL = "https://oatd.org/oatd/search?q=Methods+for+Sensing%2C+Analysis+and+Computation+of+Loads+and+Distributed+Damage+in+Bridges&form=basic", URL = "http://hdl.handle.net/10027/20220", size = "171 pages", abstract = "The worldwide ageing of the infrastructure and the development of new technologies in the construction industry provided a need for structural health monitoring (SHM). SHM provides a tool for owners and researchers to assess the condition of a structure and monitor its behaviour under real life conditions. Road transport and the related infrastructures are clearly an integral part of the economic, political, and social development of the western world. As an example, highway bridges as a major part of infrastructures can be greatly damaged by excessively heavy vehicles or severe environmental conditions. It is therefore, important to assure that such facilities are well maintained and function properly in order to avoid major failures or the need for costly repairs. In current thesis, it is attempted to innovate techniques in order to measure the vehicles loads affecting the bridge elements as well as damage detection methods to monitor the defects along the in-service bridge structural components. Bridge Weigh-in-Motion (BWIM) is using an existing bridge to weigh trucks while they are moving at full highway speeds. A new method of BWIM has been established in order to weigh the passing trucks relying on the shear strain measurements near the bridge abutments which differs from the flexural strain based traditional systems. The shear strain are measured using the rosettes sensors installed on the webs of bridge girders to directly measure the individual axle weights of trucks passing over the bridge abutments. Two concrete slab on steel girder bridges, and a box girder prestressed concrete with different structural types, span lengths, and different sizes were instrumented for the performance verification of the proposed BWIM system. A series of truck runs were implemented in the field to calibrate and evaluate the proposed BWIM system's efficiency. In addition, current research formulated a reference-free distributed damage detection method in order to locate the defects that occur in structures under in-service operating conditions. The sensing method is developed on the basis of Brillouin scattering phenomena. It employs the dynamic distributed strain measurement data in order to sense the structural perturbations under in-service operations, i.e. bridges subjected to traffic loadings, or aircrafts during flights. The advantage of the method developed in this study is that it enables the structure to be monitored at any stage during its service life without the need for prior reference data. An experimental program was designed to investigate the feasibility of the proposed approach in detecting the locations of very small defects. Laboratory experiments were designed in order to simulate the effect of ambient conditions in bridges, especially in terms of realistic displacements, i.e. deflections occurring in highway bridges. In a following effort, a theoretical model was also investigated to analysis the strain transfer mechanism from the structure surface to the distributed optical fibre components in the presence of local defects. The main objective pertained to the accurate quantification of local defects sizes based on distributed monitoring of strains in large structural systems. The theoretical formulation simulated the strain distribution within the components of an optical fiber crossing over a single crack opening. The proposed model was formulated in a manner to quantify defects in the presence of structural vibration. Both linear and nonlinear mechanical characteristics of optical fibre components were also assumed in the formulation. The spatial resolution effect was further numerically implemented within the formulation in order to simulate the measurement configurations. An experimental program was designed for calibration as well as the validation of theoretical formulation. The experiments involved dynamic tests of a 15 meter long steel I beam with two fabricated defects with small opening displacements ranging between 50 and 550 microns.", notes = "2016-02-17 Supervisor Farhad Ansari", } @Article{Babanajad:2017:AES, author = "Saeed K. Babanajad and Amir H. Gandomi and Amir H. Alavi", title = "New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach", journal = "Advances in Engineering Software", year = "2017", keywords = "genetic algorithms, genetic programming, Gene expression programming, Artificial intelligence, Triaxial, Machine learning, Computer-aided, Strength model", ISSN = "0965-9978", URL = "http://www.sciencedirect.com/science/article/pii/S096599781630566X", DOI = "doi:10.1016/j.advengsoft.2017.03.011", abstract = "The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength (sigma 1) to mix design parameters and principal stresses (sigma 2, sigma 3), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions.", } @InProceedings{Babar:2013:ICDIM, author = "Zaheer Babar and Muhammad Waqas and Zahid Halim and Muhammad Arshad Islam", booktitle = "Eighth International Conference on Digital Information Management (ICDIM 2013)", title = "Genetic Programming Based Degree Constrained Spanning Tree Extraction", year = "2013", month = sep, pages = "241--246", keywords = "genetic algorithms, genetic programming, Degree Constraint Spanning Tree, Spanning Tree, Minimum Spanning Tree, Cyclic Interchange", DOI = "doi:10.1109/ICDIM.2013.6693966", size = "6 pages", abstract = "The problem of extracting a degree constraint spanning tree deals with extraction of a spanning tree from a given graph. Where, the degree of each node is greater than or equal to a thread hold value. Genetic Programming is an evolution based strategy appropriate for optimisation problems. In this paper we propose a genetic programming based solution to the degree constraint spanning tree extraction. The individuals of the population are represented as a tree and mutation is applied as the only genetic operator for evaluation to occur. We have further tested our proposed solution on different graph and found it to be suitable for degree constraint spanning tree extraction problem.", notes = "Also known as \cite{6693966}", } @InProceedings{Baber:2009:MSG, author = "C. Baber and N. Stanton and D. Howard and Robert J. Houghton", title = "Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis", booktitle = "NATO RTO Modelling and Simulation Group Symposium", year = "2009", editor = "J. Ruiz", number = "RTO-MP-MSG-069 AC/323(MSG-069)TP/297", pages = "Paper 15", address = "Brussels, Belgium", month = "15-16 " # oct, organisation = "NATO Science and Technology Organization", keywords = "genetic algorithms, genetic programming", isbn13 = "978-92-837-0100-2", URL = "http://ftp.rta.nato.int/public//PubFullText/RTO/MP/RTO-MP-MSG-069///MP-MSG-069-15.pdf", broken = "http://ftp.rta.nato.int/public//PubFullText/RTO/MP/RTO-MP-MSG-069///MP-MSG-069-15.doc", URL = "https://apps.dtic.mil/sti/citations/ADA568007", size = "14 pages", abstract = "A significant challenge in intelligence analysis involves knowing when a social network description is complete, i.e., when sufficient connections have been found to render the network complete. In this paper, a combination of methods is used to predict covert network structures for specific missions. The intention is to support hypothesis-generation in the Social Network Analysis of covert organisations. The project employs a four phase approach to modelling social networks, working from task descriptions rather than from contacts between individual: phase one involves the collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive Work Analysis, to provide both a process model of the operation and an indication of the constraints under which the operation will be performed; phase three involves the generation of alternative networks using Genetic Programming; phase four involves the analysis of the resulting networks using social network analysis. Subsequent analysis explores the resilience of the networks, in terms of their resistance to losses of agents or tasks. The project demonstrates that it is possible to define a set of structures that can be tackled using different intervention strategies, demonstrates how patterns of social network structures can be predicted on the basis of task knowledge, and how these structures can be used to guide the gathering of intelligence and to define plausible Covert Networks.", notes = "Cited by \cite{conf/ichit/HowardC12} RTO-MP-MSG-069 - Current uses of M&S Covering Support to Operations, Human Behaviour Representation, Irregular Warfare, Defence against Terrorism and Coalition Tactical Force Integration Utilisation actuelle M&S couvrant le soutien aux operations, la representation du comportement humain, la guerre asymetrique, la defense contre le terrorisme et l'integration d'une force tactique de coalition Broken Feb 2019 https://www.cso.nato.int/pubs/rdp.asp?RDP=RTO-MP-MSG-069", } @InProceedings{baber:2002:EuroGP, title = "Evolutionary Algorithm Approach to Bilateral Negotiations", author = "Vinaysheel Baber and Rema Ananthanarayanan and Krishna Kummamuru", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "202--211", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_20", abstract = "The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted. The technology has created an opportunity to get beyond single-issue negotiation by determining sellers' and buyers' preferences across multiple issues, thereby creating possible joint gains for all parties. We develop simple multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets. In this study, we show how a genetic algorithm based technique, coupled with a simple heuristic can achieve good results in business negotiations. The negotiations' outcomes are evaluated on two dimensions: joint utility and number of ex-changes of offers to reach a deal. The results are promising and indicate possible use of such approaches in actual electronic commerce systems.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @PhdThesis{Babic:thesis, author = "Matej Babic", title = "Analiza kaljenih materialov s pomocjo fraktalne geometrije", alternate_title = "Analysis of Hardened Materials Using Fractal Geometry", school = "Racunalnistvo in Informatiko, Fakulteta za Elektrotehniko, Univerza v Mariboru", year = "2014", address = "Maribor", keywords = "genetic algorithms, genetic programming, inteligentni sistemi, algoritmi, hibridni sistemi, strojno ucenje, fraktalna geometrija, teorija grafov, topografija materiala po toplotni obdelavi, intelligent system, algorithms, hybrid system, machine learning, ANN, fractal geometry, graph theory, topography of materials after heat treatment", URL = "https://dk.um.si/IzpisGradiva.php?id=46366&lang=eng&prip=rul:10960417:d1", URL = "https://dk.um.si/Dokument.php?id=67818&lang=eng", URL = "https://core.ac.uk/download/pdf/67581852.pdf", URL = "https://www.proquest.com/docview/2194811728", size = "178 pages", abstract = "In this dissertation we study intelligent systems and the search for knowledge, computing paradigms that are useful and beneficial for the heat treatment of materials. To identify the complexity of different heat-treated samples, we used the fractal geometry method. We designed an intelligent system through which we announced topographical properties of the material after heat treatment. We have also developed a new algorithm for 3D graph visibility. With the help of the topological properties density of 3D graphs, we have built an intelligent system which can predict the topographic characteristics of the samples after heat treatment. Fractal geometry can be used to analyse complex structures that occur in the heat treatment of materials. Thus, the use of fractal geometry demonstrates the advantages of laser heat treatment techniques over the inductive, classical and the hardening furnace. Fractal geometry is a new approach, based on the characterisation of irregular microstructures, and serves as an assessment tool for determining structural properties. It can be used in the analysis of different heattreated materials. Fractal geometry is based on the idea of invariant magnification, which means that the observed image is not the same regardless of how strong the microscope is. It should be noted that the fractal dimension does not fully characterise the geometry, but is rather an indication of irregularities. Fractal geometry was used here to determine the topographical properties of hardened materials . We have introduced a new method for calculating the fractal dimension of a 3D object. With the development of laser technology in the field of heat treatment of materials there is an increased need to develop new methods with which to determine (set) better resistance of material, lower friction and better heat resistance of material. We therefore aim to build intelligent systems to increase productivity in the field of heat treatment of materials. With the help of the intelligent system we intend to show which technique of heat treatment is best. In this dissertation we present four new composite hybrid methods: * composite hybrid genetic algorithms - multiple regression - neural network-multiple regression (we call it a hybrid loop). * composite hybrid genetic algorithm - neural network - multiple regression- neural network (we call it the optimal hybrid loop). * composite hybrid genetic algorithm - neural network - multiple regression-neural network - multiple regression (we call it the cyclic hybrid). * composite hybrid genetic algorithm - multiple regression - neural network -multiple regression - neural network (we call it the optimal linear hybrid). Composite hybrid performances were slightly worse than expected, because of the shortcomings of the individual basic methods. The multiple regression method is the worst method and adversely affected the composite hybrid. The new composite hybrids give better results than existing composite hybrid systems, however. We want to improofe results of new hybrid system, thus we built new composite hybrib, hyiper hybrid. At the end of the dissertation further comments are made and a two new hybrid systems proposed which we call the spiral hybrid and optimal spiral hybrid. This method are useful when a large number of basic methods are employed. We also propose combining (pooling) the six new hybrid methods presented in the new hyper hybrids.", notes = "Solvenian https://books.google.co.uk/books/about/Hybrid_System_of_Machine_Learning_Using.html?id=dBZ3zQEACAAJ Hybrid System of Machine Learning Using Genetic Programming and Multiple Regression Supervisor: Peter Kokol", } @Article{babic:2014:MT, author = "Matej Babic and Peter Kokol and Igor Belic and Peter Panjan and Miha Kovacic and Joze Balic and Timotej Verbovsek", title = "Prediction of the hardness of hardened specimens with a neural network", journal = "Materiali in tehnologije/Materials and Technology", year = "2014", volume = "48", number = "3", pages = "409--414", month = may # "-" # jun, keywords = "ANN, fractal dimension, laser, hardening, neural network", ISSN = "1580-2949", URL = "http://mit.imt.si/Revija/", URL = "http://mit.imt.si/Revija/izvodi/mit143/babic.htm", URL = "http://mit.imt.si/Revija/izvodi/mit143/babic.pdf", size = "6 pages", abstract = "In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimise the structure and properties of tool steel, it is necessary to take into account the effect of the self-organisation of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased.", notes = "Is this GP? Page 412 gives a linear regression model. UDK 621.785.616: 620.178:004.032.26 MTAEC9, 48(3)409(2014)", } @Article{babic:2014:etv, author = "Matej Babic and Peter Kokol and Igor Belic and Peter Panjan and Miha Kovacic and Joze Balic", title = "Using of genetic programming in engineering", journal = "Elektrotehniski vestnik", year = "2014", volume = "81", number = "3", pages = "143--147", month = jul, keywords = "genetic algorithms, genetic programming, engineering, complex geometry structure", ISSN = "0013-5852", URL = "http://ev.fe.uni-lj.si/3-2014/Babic.pdf", size = "5 pages", abstract = "Intelligent systems are process coupled with robotics in industrial usually settings, though they may be used as diagnostic systems connected only to passive sensors. In this paper we use a new method which combines an intelligent genetic algorithm and multiple regression to predict the hardness of hardened specimens. The hardness of a material is an important mechanical property affecting mechanical properties of materials. The Microstructures of the hardened specimens are very complex and cannot be described them with the classical Euclidian geometry. Thus, we use a new method, i.e. fractal geometry. By using the method intelligent-system, genetic programming and multiple regression, improved production the process laser-hardening increases because of the decreased time of the process and, the improved increased topographical property of the used materials. The genetic-programming modelling results show a good agreement with the measured hardness of the hardened specimens.", abstract_si = "Inteligentni sistemi naj bi se po navadi povezali skupaj z robotiko v nastavitvah industrijskih procesov, ceprav so lahko sistemi za diagnostiko povezani samo za pasivne senzorje. Vtem clanku bomo uporabili metodo, ki zdruzuje inteligentne genetske algoritme in multiplo regresijo za napoved trdote kaljenih vzorcev. Trdota materiala je pomembna mehanska lastnost, ki vpliva na mehanske lastnosti materialov. Mikrostrukture kaljenih vzorcev so zelo kompleksne in jih ne moremo opisati s klasicno evklidsko geometrijo. Zato smo uporabili novo metodo, fraktalno geometrijo. Z metodo inteligentnega sistema, genetskim programiranjem in multiplo regresijo smo povecali proizvodnjo pri laserskem kaljenju, saj smo skrajšali cas procesa in povecali topografsko lastnost materiala. Rezultati modeliranja genetskega programiranja se dobro ujemajo z izmerjenimi vrednostmi trdote kaljenih vzorcev.", notes = "http://ev.fe.uni-lj.si/online.html Journal of Electrical Engineering and Computer Science", } @Article{babic:2016:EV, author = "Matej Babic", title = "A new hybrid-system method of Machine Learning using a new method of fractal geometry and a new method of graph theory", journal = "Elektrotehniski vestnik", year = "2016", volume = "83", number = "1-2", pages = "42--46", keywords = "genetic algorithms, genetic programming, image processing, intelligent system, visibility graphs, fractal dimension", ISSN = "0013-5852", URL = "https://ev.fe.uni-lj.si/1-2-2016/Babic.pdf", URL = "https://ev.fe.uni-lj.si/online.php?vol=83", URL = "https://www.proquest.com/docview/1789072317?sourcetype=Scholarly%20Journals", size = "5 pages", abstract = "a hybrid system method to predict the volume of the robot-laser-hardened specimens when one of the parameters in the existing model cannot be measured or calculated the intelligent-system is presented. Also, we have a model of the intelligent system to predict the volume of hardened specimens developed by someone, but we can not calculate one parameter in it. Thus, we develop a new method of the hybrid intelligent system to solve this problem. We develop a hybrid of genetic programming and multiple regression. To predict the volume of hardened specimens, we use teh neural network, genetic algorithm and multiple regression. The genetic programming modelling results show a good agreement with the measured volume of hardened specimens. We analyse the SEM picture of the microstructure of robot-laser-hardened specimens with a mathematical method. In this open problem we use the graph theory and fractal geometry. Fractal dimensions are calculated using image processing of a SEM micrographs in combination with a box-counting algorithm using ImageJ software.", notes = "cites \cite{Babic:thesis} Jozef Stefan Institute, Slovenia", } @Article{babic:2017:CAI, author = "Matej Babic and Ladislav Hluchy and Peter Krammer and Branko Matovic and Ravi Kumar and Pavel Kovac", title = "New Method for Constructing a Visibility Graph-Network in {3D} Space and a New Hybrid System of Modeling", journal = "Computing and Informatics", year = "2017", volume = "36", number = "5", pages = "1107--1126", month = "19 " # dec, keywords = "genetic algorithms, genetic programming, Artificial intelligence, visibility graphs, pattern recognition, modeling, hybrid system", ISSN = "1335-9150", URL = "https://www.cai.sk/ojs/index.php/cai/issue/view/179", URL = "https://www.cai.sk/ojs/index.php/cai/article/view/2017_5_1107/856", DOI = "doi:10.4149/cai_2017_5_1107", size = "20 pages", abstract = "This paper describes a new method for constructing a visibility graph in 3D space. We use a method for predicting porosity of hardened specimens...", } @Article{Babic:2021:Remote_Sensing, author = "Matej Babic and Dusan Petrovic and Jost Sodnik and Bozo Soldo and Marko Komac and Olena Chernieva and Miha Kovacic and Matjaz Mikos and Michele Cali", title = "Modeling and Classification of Alluvial Fans with {DEMs} and Machine Learning Methods: A Case Study of {Slovenian} Torrential Fans", journal = "Remote Sensing", year = "2021", volume = "13", number = "9", article-number = "1711", month = "28 " # apr, keywords = "genetic algorithms, genetic programming, random forest, RF, support vector machine, SVM, neural network, ANN, digital elevation model, torrential fan surfaces, geomorphometric parameters, graph method, debris flows", publisher = "MDPI", ISSN = "2072-4292", URL = "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=127268", URL = "https://www.mdpi.com/2072-4292/13/9/1711", DOI = "doi:10.3390/rs13091711", size = "18 pages", abstract = "Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyse and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.", notes = "Faculty of Information Studies, Ljubljanska cesta 31a, SI-8000 Novo Mesto, Slovenia", } @InProceedings{Babic:2021:SpliTech, author = "Matej Babic and Branko Ster and Janez Povh and Joel J. P. C. Rodrigues", title = "A New Composite Method of Modeling Bicycle Traffic using Convolutional Neural Networks and Genetic programming", booktitle = "2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)", year = "2021", month = sep, keywords = "genetic algorithms, genetic programming, ANN, GoNM", DOI = "doi:10.23919/SpliTech52315.2021.9566405", abstract = "This study proposes a new composite method of modelling bicycle traffic in the town of Novo mesto, Slovenia, using Convolutional neural networks and Genetic programming. Public passenger transport (PPT) is important for every municipality, as the current transport system faces well-known problems such as congestion, environmental impact, lack of parking areas, increased safety risks and high energy consumption. The wider Novo mesto region, with about 30000 inhabitants, is an important industrial center and is heavily dependent on urban traffic. The aim of the research is to analyze and model bicycle rentals. Convolutional neural networks and genetic programming are used to predict bicycle traffic over 35 weeks.", notes = "Also known as \cite{9566405} See also \cite{babic:2022:IJQR} Faculty of Information studies, Novo mesto, Slovenia", } @Article{BABIC:2021:PM, author = "M. Babic and G. Lesiuk and D. Marinkovic and M. Cali", title = "Evaluation of microstructural complex geometry of robot laser hardened materials through a genetic programming model", journal = "Procedia Manufacturing", volume = "55", pages = "253--259", year = "2021", note = "FAIM 2021", ISSN = "2351-9789", DOI = "doi:10.1016/j.promfg.2021.10.036", URL = "https://www.sciencedirect.com/science/article/pii/S235197892100233X", keywords = "genetic algorithms, genetic programming, Microstructure geometry, Fractal geometry, Laser beam process parameters, Forecast model, Hardened steels", abstract = "Surface-hardening process of steel materials by robot laser technologies can involve the challenge of modeling the determining process parameters through non-conventional tools in order to evaluate the quality of the heat treatment. In the current study a new method based on fractal geometry, used to determine the microstructural properties of laser hardened steels manufactured by anthropomorphic robots, is presented. The assumptions were that the microstructure of laser hardened steel can be studied as a complex structural geometry and the modeling of the analyzed complex geometries can be made through genetic programming for prediction purposes. The effect of process parameters and their joint combination on the final microstructures geometry of the heat treated steel was investigated. In particular, the influence of temperature, laser beam velocity, and impact angle were studied since they were showed in a preliminary study to be the process parameters that most significantly influenced the quality of the heat treated steel. The developed model reached a precision of the prediction equal to 98.59 percent", } @Article{babic:2022:IJQR, author = "Matej Babic and Cristiano Fragassa and Dragan Marinkovic and Janez Povh", title = "Machine Learning Tools in the Analyze of a Bike Sharing", journal = "International Journal for Quality Research", year = "2022", volume = "16", number = "2", pages = "375--394", keywords = "genetic algorithms, genetic programming, GoNM, Transportation Systems Engineering, bicycle, Cycles, Bike-Sharing System (PBS), Artificial Intelligence (AI), Machine Learning (ML), Hybrid Intelligent Systems, Weather Conditions", ISSN = "1800-6450", URL = "http://ijqr.net/paper.php?id=989", URL = "http://ijqr.net/journal/v16-n2/4.pdf", DOI = "doi:10.24874/IJQR16.02-04", size = "20 pages", abstract = "Advanced models, based on artificial intelligence and machine learning, are used here to analyze a bike-sharing system. The specific target was to predict the number of rented bikes in the Nova Mesto, Slovenia public bike share scheme. For this purpose, the topological properties of the transport network were determined and related to the weather conditions. Pajek software was used and the system behavior during a 30-week period was investigated. Open questions were, for instance: how many bikes are shared in different weather conditions? How the network topology impacts the bike sharing system? By providing a reasonable answer to these and similar questions, several accurate ways of modeling the bike sharing system which account for both topological properties and weather conditions, were developed and used for its optimization.", notes = "02.06.2021 seminar: https://www.fpp.uni-lj.si/en/research/kappra-coffee-talks-on-research/2022053112194477/kappra-no-24 http://ijqr.net/", } @Article{babic:2022:AS, author = "Matej Babic and Dragan Marinkovic and Marco Bonfanti and Michele Cali", title = "Complexity Modeling of {Steel-Laser-Hardened} Surface Microstructures", journal = "Applied Sciences", year = "2022", volume = "12", number = "5", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/12/5/2458", DOI = "doi:10.3390/app12052458", abstract = "Nowadays, laser hardening is a consolidated process in many industrial sectors. One of the most interesting aspects to be considered when treating the surface-hardening process in steel materials by means of laser devices is undoubtedly the evaluation of the heat treatment quality and surface finish. In the present study, an innovative method based on fractal geometry was proposed to evaluate the quality of surface-steel-laser-hardened treatment. A suitable genetic programming study of SEM images (1280 × 950 pixels) was developed in order to predict the effect of the main laser process parameters on the microstructural geometry, assuming the microstructure of laser-hardened steel to be of a structurally complex geometrical nature. Specimens hardened by anthropomorphic laser robots were studied to determine an accurate measure of the process parameters investigated (surface temperature, laser beam velocity, laser beam impact angle). In the range of variation studied for these parameters, the genetic programming model obtained was in line with the complexity index calculated following the fractal theory. In particular, a percentage error less than 1percent was calculated. Finally, a preliminary study of the surface roughness was carried out, resulting in its strong correlation with complex surface microstructures. Three-dimensional voxel maps that reproduce the surface roughness were developed by automating a routine in Python virtual environment.", notes = "also known as \cite{app12052458}", } @Article{Babic:2022:FF, author = "Matej Babic and Dragan Marinkovic and Miha Kovacic and Branko Ster and Michele Cali", title = "A New Method of Quantifying the Complexity of Fractal Networks", journal = "Fractal and Fractional", year = "2022", volume = "6", number = "6", pages = "article number 282", keywords = "genetic algorithms, genetic programming", ISSN = "2504-3110", URL = "https://www.mdpi.com/2504-3110/6/6/282", DOI = "doi:10.3390/fractalfract6060282", abstract = "There is a large body of research devoted to identifying the complexity of structures in networks. In the context of network theory, a complex network is a graph with nontrivial topological features; features that do not occur in simple networks, such as lattices or random graphs, but often occur in graphs modeling real systems. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks, such as computer networks and logistic transport networks. Transport is of great importance for the economic and cultural cooperation of any country with other countries, the strengthening and development of the economic management system, and in solving social and economic problems. Provision of the territory with a well-developed transport system is one of the factors for attracting population and production, serving as an important advantage for locating productive forces and providing an integration effect. we introduce a new method for quantifying the complexity of a network based on presenting the nodes of the network in Cartesian coordinates, converting to polar coordinates, and calculating the fractal dimension using the ReScaled ranged (R/S) method. Our results suggest that this approach can be used to determine complexity for any type of network that has fixed nodes, and it presents an application of this method in the public transport system.", notes = "Also known as \cite{fractalfract6060282}", } @Article{babic:2023:FaF, author = "Matej Babic and Dragan Marinkovic", title = "A New Approach to Determining the Network Fractality with Application to {Robot-Laser-Hardened} Surfaces of Materials", journal = "Fractal and Fractional", year = "2023", volume = "7", number = "10", pages = "Article No. 710", keywords = "genetic algorithms, genetic programming", ISSN = "2504-3110", URL = "https://www.mdpi.com/2504-3110/7/10/710", DOI = "doi:10.3390/fractalfract7100710", abstract = "A new method to determine a fractal network in chaotic systems is presented together with its application to the microstructure recognition of robot-laser-hardened (RLH) steels under various angles of a laser beam. The method is based on fractal geometry. An experimental investigation was conducted by investigating the effect of several process parameters on the final microstructures of material that has been heat-treated. The influences of the surface temperature, laser speed, and different orientation angles of the laser beam on the microstructural geometry of the treated surfaces were considered. The fractal network of the microstructures of robot-laser-hardened specimens was used to describe how the geometry was changed during the heat treatment of materials. In order to predict the fractal network of robot-laser-hardened specimens, we used a method based on intelligent systems, namely genetic programming (GP) and a convolutional neural network (CNN). The proposed GP model achieved a prediction accuracy of 98.4percent, while the proposed CNN model reached 96.5percent. The performed analyses demonstrate that the angles of the robot laser cell have a noticeable effect on the final microstructures. The specimen laser-hardened under the conditions of 4 mm/s, 1000 ?C, and an impact angle of the laser beam equal to 75? presented the maximum fractal network. The minimum fractal network was observed for the specimen before the robot-laser-hardening process.", notes = "also known as \cite{fractalfract7100710}", } @Article{Babic:2024:sv-jme, author = "Matej Babic and Miha Kovacic and Cristiano Fragassa and Roman Sturm", title = "Selective Laser Melting: A Novel Method for Surface Roughness Analysis", journal = "Strojniski vestnik - Journal of Mechanical Engineering", year = "2024", volume = "70", number = "7-8", pages = "313--324", keywords = "genetic algorithms, genetic programming, additive manufacturing, selective laser melting, surface roughness, fractal geometry, network theory, ANN, kNN, SVM", ISSN = "0039-2480", URL = "https://www.sv-jme.eu/?ns_articles_pdf=/ns_articles/files/ojs30/1009/66cc2d6717e58.pdf&id=7068", DOI = "doi:10.5545/sv-jme.2024.1009", size = "12 pages", abstract = "The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requirements. Thus, the suitability of surfaces is a critical factor, emphasising the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method's practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing.", notes = "sv-jme tel:+386 1 4771 137 Faculty of Information Studies, Slovenia", } @InProceedings{babovic:1994:camh, author = "Vladan Babovic and A. W. Minns", title = "Use of computational adaptive methodologies in hydroinformatics", booktitle = "Proceedings of the first international conference on hydroinformatics, Delft, Netherlands", year = "1994", editor = "A. Verwey and A. W. Minns and V. Babovic and C. Maksimovic", pages = "201--210", publisher_address = "P. O. Box 1675, Rotterdam, Netherlands", month = "19--23 " # sep, publisher = "A. A. Balkema", keywords = "genetic algorithms, genetic programming", ISBN = "90-5410-512-7", URL = "http://www.amazon.co.uk/Hydroinformatics-Proceedings-International-Conference-Netherlands/dp/9054105127", abstract = "Summaries a study of the performance of artificial neural networks and GP compared to an empirically-based method using a problem of salt intrusion as an example.", notes = "Does not present clear winner (ANN, GP or traditional) upto reader to choose approriate to their problem. IHE-Delft, The Netherlands ", } @InProceedings{babovic:1995:gmibed, author = "Vladan Babovic", title = "Genetic Model Induction Based on Experimental Data", booktitle = "Proceedings of the XXVIth Congress of International Association for Hydraulics Research", year = "1995", editor = "J. Gardiner", address = "London, UK", month = "11--15 " # sep, organisation = "International Association of Hydraulic Research", publisher = "Thomas Telford Ltd", keywords = "genetic algorithms, genetic programming", ISBN = "0-7277-2059-7", URL = "http://www.amazon.co.uk/Hydra-2000-Development-Proceedings-International/dp/0727720597/ref=sr_1_4?s=books&ie=UTF8&qid=1324144161&sr=1-4", broken = "http://www.iahr.net/e-shop/store/viewItem.asp?idProduct=91", abstract = "GP used to perform an analysis of sediment transport data and to induce relationshop between bed concentration of suspended sediment and the hydraulic conditions. GP results similar accuracy to traditional techniques. IHE-Delft, The Netherlands", notes = " ", } @PhdThesis{babovic:thesis, author = "Vladan Babovic", title = "Emergence, Evolution, Intelligence: Hydroinformatics", school = "International Institute for Infrastructural, Hydraulic and Environmental Engineering and Technical University Delft", year = "1996", address = "The Netherlands", month = "20 " # mar, note = "Published by A. A. Balkema Publishers", keywords = "genetic algorithms, genetic programming", ISBN = "90-5410-404-X", URL = "http://repository.tudelft.nl/view/ir/uuid%3A58c50efe-4a6a-40b4-8c60-2b81d629b49c/", URL = "http://repository.tudelft.nl/assets/uuid:58c50efe-4a6a-40b4-8c60-2b81d629b49c/EMERGENCE__EVOLUTION__INTELLIGENCE_HYDROINFORMATICS.PDF", size = "348 pages", abstract = "The computer-controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually becoming more complex.The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind's most valuable natural resource, but one which is in increasingly limited supply. The fresh water is the vita! natural resource which supports all environmental activities, that is, natura! economy, and all human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of human- and hydro-economies, is not only a human, socio-economic pressure, but it is the question of life and death! Hydroinformatics - the nascent technology concerned with the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the waters, the very arteries and veins of the biosphere. This work addresses some of the central issues within hydroinformatics paradigm. It focuses on ttie analysis of decentralised and distributed computation, as well as the issues of design of individual computatiorial agents using evolutionary algorithms.", notes = "Promotor: Abbott, M.B. See also \cite{babovic:book}", } @Book{babovic:book, author = "Vladan Babovic", title = "Emergence, evolution, intelligence; Hydroinformatics - A study of distributed and decentralised computing using intelligent agents", publisher = "A. A. Balkema Publishers", year = "1996", address = "Rotterdam, Holland", keywords = "genetic algorithms, genetic programming", ISBN-13 = "978-90-5410-404-9", ISBN = "90-5410-404-X", URL = "https://www.amazon.com/Hydroinformatics-Emergence-Evolution-Intelligence-Thesis/dp/905410404X/ref=sr_1_1/165-1740647-7487049?s=books&ie=UTF8&qid=1477940894&sr=1-1&keywords=9789054104049", abstract = "The computer controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually becoming more complex. The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind's most valuable natural resource, but one which is in increasingly limited supply. The fresh water is the vital natural resource which supports all environmental activities, that is, natural economy, and all human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of human- & hydro-economies is not only a human, socio-economic pressure, but it is the question of life and death. Hydroinformatics - the nascent technology concerned with the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the waters, the very arteries and veins of the biosphere.", notes = "publication of \cite{babovic:thesis}", size = "344 pages", } @InCollection{babovic:1996:wmbAI, author = "Vladan Babovic", title = "Can water resources management benefit from artificial intelligence?", booktitle = "Computation Fluid Dynamics: Bunte Bilder in der Praxis", publisher = "Meinz Verlag", year = "1996", editor = "J. Kongeter", pages = "337--358", address = "Aachen, Germany", keywords = "genetic algorithms, genetic programming", URL = "http://www.dwa.de/dwa/sitemapping.nsf/literaturvorschau?openform&bestandsnr=36547", notes = "26. IWASA International Wasserbau-Symposium Aachen 1995/96 ", } @Article{babovic:1997:eehd1, author = "Vladan Babovic and Michael B. Abbott", title = "The evolution of equation from hydraulic data, Part {I}: Theory", journal = "Journal of Hydraulic Research", year = "1997", volume = "35", number = "3", pages = "397--410", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/00221689709498420", size = "14 pages", abstract = "Even as hydroinformatics continues to elaborate more advanced operational tools, languages and environments for engineering and management practice, it necessarily also promotes a number of concepts and methodologies that are eminently applicable within the more traditional areas of hydraulic research. Among the many new possibilities thereby introduced, that of evolving equations from hydraulic data using evolutionary algorithms has a particularly wide range of applications. The present paper is in two parts, the first of which introduces the subject and outlines its theory, while the second is given over to four representative applications and to some of the most immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologie model but provides equations with purely hydraulic interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of 'thresholds' in physical processes. It also provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover numerically generated data, in this case appertaining to the case of flow resistance in the presence of vegetation.", notes = "See also \cite{babovic:1997:eehd2}", } @Article{babovic:1997:eehd2, author = "Vladan Babovic and Michael B. Abbott", title = "The evolution of equation from hydraulic data, Part II: Applications", journal = "Journal of Hydraulic Research", year = "1997", volume = "35", number = "3", pages = "411--430", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1080/00221689709498421", size = "20 pages", abstract = "This second part of the paper \cite{babovic:1997:eehd1} is given over to describing four representative applications and to some of the most immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologic model but provides equations with purely hydraulic interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of 'thresholds' in physical processes. It also provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover numerically-generated data, in this case appertaining to the case of flow resistance in the presence of vegetation. In conclusion, this work is set within the context of other developments, such as those of data mining and knowledge discovery generally", } @InCollection{babovic:1997:mfnls, author = "Vladan Babovic", title = "On the Modelling and Forecasting of Non-linear Systems", booktitle = "Operational Water Management: Proceedings of the European Water Resources Association Conference, Copenhagen, Denmark, 3-6 September 1997", publisher = "Balkema", year = "1997", editor = "J. C. Refsgaard and E. A. Karalis", pages = "195--202", address = "Rotterdam", keywords = "genetic algorithms, genetic programming", ISBN = "90-5410-897-5", URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=9054108975", size = "8 pages", } @InProceedings{babovic:1998:stdlkm, author = "V. Babovic", title = "Sediment transport data - Large knowledge mine", booktitle = "Proceedings of the Third International Conference on Hydroscience and Engineering", year = "1998", address = "Cottbus, Germany", keywords = "genetic algorithms, genetic programming", } @InProceedings{babovic:1998:dmtsmf, author = "V. Babovic", title = "A data mining approach to time series modelling and forecasting", booktitle = "Proceeding of the Third International Conference on Hydroinformatics", year = "1998", editor = "Babovic and Larsen", pages = "847--856", address = "Copenhagen, Denmark", publisher_address = "Rotterdam", publisher = "Balkema", keywords = "genetic algorithms, genetic programming, Vltava River system, flood control and protection of Prague, artificial neural networks", ISBN = "90-5410-983-1", notes = "Hydroinformatics'98", } @InProceedings{babovic:1998:mstGP, author = "Vladan Babovic", title = "Mining sediment transport data with genetic programming", booktitle = "Proceedings of the First International Conference on New Information Technologies for Decision Making in Civil Engineering", year = "1998", pages = "875--886", address = "Montreal, Canada", month = "11-13 " # oct, keywords = "genetic algorithms, genetic programming", } @InProceedings{babovic:1999:cskd-veg, author = "Vladan Babovic and Maarten Keijzer", title = "Computer supported knowledge discovery - A case study in flow resistance induced by vegetation", booktitle = "Proceedings of the XXVIII Congress of International Association for Hydraulic Research", year = "1999", address = "Graz, Austria", month = "22-27 " # aug, keywords = "genetic algorithms, genetic programming", URL = "http://www.iahr.org/membersonly/grazproceedings99/pdf/C021.pdf", size = "7 pages", abstract = "Data Mining and Knowledge Discovery aims at providing tools to facilitate the conversion of data to a better understanding of processes that generated or produced those data. We call this the mining of data for knowledge. Data mining extracts patterns from data. It creates models from data, by using for example, genetic programming, polynomial or artificial neural networks, or even support vector machines. These new models, combined with the understanding of the physical processes - the theory - can result in an improved understanding and novel formulations of physical laws and an improved predictive capability. The present paper describes some of the very first efforts under the D2K (Data to Knowledge) Research Project currently conducted at Danish Hydraulic Institute with a support from the Danish Technical Research Council (STVF). The paper firstly outlines elementary data mining principles, particularly when applied to analysis of scientific data. In the second half of the contribution, results obtained through analysis of the data related to the additional resistance to the flow induced by flexible vegetation are presented. The data are analysed by the means of genetic programming (GP). Induced formulations and discussed in terms of accuracy and physical interpretability.", } @InProceedings{babovic:1999:d2k, author = "V. Babovic and M. Keijzer", title = "Data to knowledge - The new scientific paradigm", booktitle = "Water Industry Systems", year = "1999", editor = "Dragan Savic and Godfrey Walters", pages = "3--14", address = "Exeter, United Kingdom", month = "13-15 " # sep, publisher = "Research Studies Pr Ltd", keywords = "genetic algorithms, genetic programming", isbn13 = "9780863802485", URL = "http://bookweb.kinokuniya.co.jp/htmy/0863802486.html", notes = "CCWI-99", } @InProceedings{me15, author = "Vladan Babovic and Maarten Keijzer", title = "Evolutionary algorithms approach to induction of differential equations", booktitle = "Proceedings of the Fourth International Conference on Hydroinformatics", address = "Iowa City, USA", year = "2000", keywords = "genetic algorithms, genetic programming", month = jul # " 23-27", organisation = "IAHR/IWA/IAHS Committee on Hydroinformatics", publisher = "International Association for Hydro-Environment Engineering and Research", URL = "http://members.iahr.org/core/orders/product.aspx?catid=3&prodid=47", } @Article{babovic:1999:td2ksed, author = "Vladan Babovic", title = "Data Mining and Knowledge Discovery in Sediment Transport", journal = "Computer-Aided Civil and Infrastructure Engineering", year = "2000", volume = "15", number = "5", pages = "383--389", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1093-9687", DOI = "doi:10.1111/0885-9507.00202", size = "7 pages", abstract = "The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or artificial sources and that we shall mine for knowledge from the data so acquired. Data mining and knowledge discovery aim at providing tools to facilitate the conversion of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the already available understanding of the physical processes -- the theory -- result in an improved understanding and novel formulations of physical laws and improved predictive capability. This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport. Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability.", notes = "Article first published online: 17 DEC 2002", } @Article{babovic:1999:GPmie, author = "Vladan Babovic and Maarten Keijzer", title = "Genetic programming as a model induction engine", journal = "Journal of Hydroinformatics", year = "2000", volume = "1", number = "1", pages = "35--60", month = jan, keywords = "genetic algorithms, genetic programming, data mining, knowledge discovery", ISSN = "1464-7141", URL = "http://jh.iwaponline.com/content/2/1/35", DOI = "doi:10.2166/hydro.2000.0004", broken = "http://www.iwaponline.com/jh/002/jh0020035.htm", size = "26 pages", abstract = "Present day instrumentation networks already provide immense quantities of data, very little of which provides any insights into the basic physical processes that are occurring in the measured medium. This is to say that the data by itself contributes little to the knowledge of such processes. Data mining and knowledge discovery aim to change this situation by providing technologies that will greatly facilitate the mining of data for knowledge. In this new setting the role of a human expert is to provide domain knowledge, interpret models suggested by the computer and devise further experiments that will provide even better data coverage. Clearly, there is an enormous amount of knowledge and understanding of physical processes that should not be just thrown away. Consequently, we strongly believe that the most appropriate way forward is to combine the best of the two approaches: theory-driven, understanding-rich with data-driven discovery process. This paper describes a particular knowledge discovery algorithm Genetic Programming (GP). Additionally, an augmented version of GP - dimensionally aware GP - which is arguably more useful in the process of scientific discovery is described in great detail. Finally, the paper concludes with an application of dimensionally aware GP to a problem of induction of an empirical relationship describing the additional resistance to flow induced by flexible vegetation.", notes = "dimensionally aware GP. Additional river water flow resistance caused by flexible vegetation closure and strong typing (STGP). dimensionally aware brood selection. Kutija-Hong model.", } @InCollection{Babovic:2000:IAHR, author = "Vladan Babovic and H. Bergmann", title = "On Computer-Aided Discovery of Knowledge in Hydraulic Engineering", booktitle = "Advances in Hydraulic Research and Engineering", publisher = "Technical University Graz", year = "2000", editor = "H. Bergmann", address = "Graz", keywords = "genetic algorithms, genetic programming", } @InCollection{me25, author = "Vladan Babovic and Maarten Keijzer", title = "On the introduction of declarative bias in knowledge discovery computer systems", booktitle = "New paradigms in river and estuarine management", editor = "Peter Goodwin", publisher = "Kluwer", year = "2001", keywords = "genetic algorithms, genetic programming", notes = "Peter Goodwin gives a short description of the workshop J. Hydraul. Eng. 127, 792 (2001); http://dx.doi.org/10.1061/(ASCE)0733-9429(2001)127:10(792) (2 pages) Workshop description https://www.lib.uidaho.edu/digital/uinews/item/ui-assembles-international-experts-on-river-issues-for-boise-short-course-and-nato-research-workshop.html", } @InProceedings{me27, author = "Vladan Babovic and Maarten Keijzer and David {Rodriguez Aguilera} and Joe Harrington", title = "An evolutionary approach to knowledge induction: Genetic Programming in Hydraulic Engineering", booktitle = "Proceedings of the World Water and Environmental Resources Congress", year = "2001", editor = "Don Phelps and Gerald Sehlke", volume = "111", pages = "64--64", address = "Orlando, Florida, USA", month = "20-24 " # may, publisher = "ASCE", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.vu.nl/~mkeijzer/publications/ASCE_paper.pdf", broken = "http://link.aip.org/link/?ASC/111/64/1", DOI = "doi:10.1061/40569(2001)64", size = "10 pages", abstract = "The process of scientific discovery has long been viewed as the pinnacle of creative thought. Thus, to many people, including some scientists themselves is seems unlikely candidate for automation by computer. However, over the past two decades researchers in AI have repeatedly questioned this attitude. The paper describes a specific evolutionary algorithm technique, genetic programming, within a scientific discovery framework, as well as its application on real world data.", notes = "World Water Congress 2001 number = 40569", } @Article{me24, author = "Vladan Babovic and Jean-Philippe Drecourt and Maarten Keijzer and Peter Friis Hansen", title = "Modelling of water supply assets: a data mining approach", journal = "Urban Water", year = "2002", volume = "4", number = "4", pages = "401--414", publisher = "Elsevier", URL = "http://www.sciencedirect.com/science/article/B6VR2-4718F0J-1/2/e361659261f99d438f8f2207f67eedf8", keywords = "genetic algorithms, genetic programming", } @Article{NordicHy, author = "Vladan Babovic and Maarten Keijzer", title = "Rainfall Runoff Modelling based on Genetic Programming", journal = "Nordic Hydrology", year = "2002", volume = "33", number = "5", pages = "331--346", month = oct, keywords = "genetic algorithms, genetic programming", ISSN = "0029-1277", URL = "http://www.iwaponline.com/nh/033/0331/0330331.pdf", DOI = "doi:10.2166/nh.2002.0012", size = "16 pages", abstract = "The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).", notes = "2021 journal called Hydrology Research", } @Article{Babovic:2005:HP, author = "Vladan Babovic", title = "Data mining in hydrology", journal = "Hydrological Processes", year = "2005", volume = "19", number = "7", pages = "1511--1515", month = "30 " # apr, keywords = "genetic algorithms, genetic programming", ISSN = "1099-1085", DOI = "doi:10.1002/hyp.5862", size = "5 pages", abstract = "Present-day instrumentation networks already provide immense quantities of data, very little of which provide any insight into the basic physical phenomena that are occurring in the medium measured. In order to exploit fully the information contained in the data, scientists are developing a suite of techniques to 'mine the knowledge' from data.", notes = "Invited Commentary", } @InCollection{Babovic:2006:, author = "Vladan Babovic and Maarten Keijzer", title = "Rainfall-Runoff Modeling Based on Genetic Programming", booktitle = "Encyclopedia of Hydrological Sciences", publisher = "Wiley", year = "2006", editor = "Malcolm G. Anderson and Keith Beven and et al.", month = "15 " # apr, keywords = "genetic algorithms, genetic programming, Hydroinformatics, symbolic regression, empirical equations, rainfall-runoff", isbn13 = "9780470848944", URL = "http://onlinelibrary.wiley.com/doi/10.1002/0470848944.hsa017/abstract", DOI = "doi:10.1002/0470848944.hsa017", abstract = "The runoff formation process is believed to be highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases, it is difficult to collect all the data necessary for such a model. By using data-driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses the use of GP for creating rainfall-runoff (R-R) models both on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).", } @InProceedings{Babovic:2007:NMHS, author = "Vladan Babovic", title = "Data-Driven Knowledge Discovery: Four Roads to Vegetation-Induced Roughness Formulae", booktitle = "Numerical Modelling of Hydrodynamics for Water Resources: Proceedings of the International Workshop on Numerical Modelling of Hydrodynamic Systems", year = "2007", editor = "Pilar Garcia Navarro and Enrique Playan", pages = "67--76", address = "Zaragoza, Spain", month = "18-21 " # jun, publisher = "Taylor \& Franics, Balkema", keywords = "genetic algorithms, genetic programming", ISBN = "0-415-44056-4", broken = "http://www.docstoc.com/docs/36112150/Numerical-Modelling-of-Hydrodynamics-for-Water-Resources", URL = "http://www.amazon.com/Numerical-Modelling-Hydrodynamics-Water-Resources/dp/0415440564/ref=cm_cr_pr_pb_t", notes = "http://www.unizar.es/nmhs/programme/programme.htm published 2008?", } @Article{Babovic:2009:JH, author = "Vladan Babovic", title = "Introducing knowledge into learning based on genetic programming", journal = "Journal of Hydroinformatics", year = "2009", volume = "11", number = "3-4", pages = "181--193", keywords = "genetic algorithms, genetic programming, empirical equations, hydraulics, sediment transport, strong typing, symbolic regression, units of measurement", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/011/0181/0110181.pdf", DOI = "doi:10.2166/hydro.2009.041", size = "13 pages", abstract = "This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems reveals that the use of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best used as a hypothesis generator assisting scientists in the discovery process.", } @InCollection{Babovic:2010:ECinH, author = "Vladan Babovic and Raghuraj Rao", title = "Evolutionary Computing in Hydrology", booktitle = "Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting", publisher = "World Scientific Publishing Co.", year = "2010", editor = "Bellie Sivakumar and Ronny Berndtsson", chapter = "7", pages = "347--369", address = "Singapore", keywords = "genetic algorithms, genetic programming", ISBN = "981-4307-97-1", URL = "http://ebooks.worldscinet.com/ISBN/9789814307987/9789814307987_0007.html", DOI = "doi:10.1142/9789814307987_0007", abstract = "Many hydrologic processes are believed to be highly complex, nonlinear, time-varying, and spatially distributed. Hence, the governing mechanisms are not easily described by simple models. With unprecedented growth in instrumentation technology, recent investigations in hydrology are supported with immense quantities of data. In order to take full advantage of the information contained in such data, scientists are increasingly relying on a suite of data-driven techniques to understand the complex hydrologic processes. Evolutionary computing (EC) techniques, with a host of optimisation and modelling tools, can contribute significantly to achieve the objectives of this knowledge-discovery exercise in hydrology. This chapter discusses the utility of these EC techniques in attempting data analysis and modeling problems associated with hydrologic systems. It introduces the concept and working principle of EC techniques in general and reviews their applications to different domains of hydrology. The study also illustrates different case studies of genetic programming (GP) technique as a modelling, data assimilation, and model emulation tool", notes = "http://www.worldscibooks.com/environsci/7783.html", } @Article{Babu:2007:EL, author = "B. V. Babu and S. Karthik", title = "Genetic Programming for Symbolic Regression of Chemical Process Systems", journal = "Engineering Letters", volume = "14", number = "2", year = "2007", pages = "42--55", month = jun, publisher = "International Association of Engineers", keywords = "genetic algorithms, genetic programming, GPLAB", ISSN = "1816-0948", oai = "oai:CiteSeerXPSU:10.1.1.148.8378", URL = "https://www.engineeringletters.com/issues_v14/issue_2/index.html", URL = "http://www.engineeringletters.com/issues_v14/issue_2/EL_14_2_6.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.8378", size = "14 pages", abstract = "The novel evolutionary artificial intelligence formalism namely, genetic programming (GP) a branch of genetic algorithms is used to develop mathematical models based on input-output data, instead of conventional regression and neural network modeling techniques which are commonly used for this purpose. This paper summarizes the available MATLAB toolboxes and their features. Glucose to gluconic acid batch bioprocess has been modeled using both GPLAB and hybrid approach of GP and Orthogonal Least Square method (GP OLS). GP OLS which is capable of pruning of trees has generated parsimonious expressions simpler to GPLAB, with high fitness values and low mean square error which is an indicative of the good prediction accuracy. The capability of GP OLS to generate non-linear input-output dynamic systems has been tested using an example of fed-batch bioreactor. The simulation and GP model prediction results indicate GP OLS is an efficient and fast method for predicting the order and structure for non-linear input and output model.", notes = "Modeling of glucose to gluconic acid bioprocess. Orthogonal Least Square OLS. GPOLS. ANOVA. http://www.engineeringletters.com/ Chemical Engineering Department, Birla Institute of Technology and Science (BITS), Pilani-333 031. India", } @InProceedings{Babu:2016:ICCES, author = "Kagana.Sarath Babu and N. Balaji", booktitle = "2016 International Conference on Communication and Electronics Systems (ICCES)", title = "Approximation of digital circuits using cartesian genetic programming", year = "2016", abstract = "Digital circuits can be approximated in which the exact functionality can be relaxed. Approximate circuits are constructed such that the logic given by the user is not implemented completely and hence their functionality can be traded for area, delay and power consumption. An evolutionary approach like Cartesian Genetic programming (CGP) is used in this paper to make automatic design process of digital circuits. The quality of approximate circuits can be improved along with the reduction of evolution time by using a heuristic population seeding method which is embedded into CGP. In particular, digital circuits like full adder, 2 bit multiplier and 2 bit adder are addressed in this paper. Experimental results are given where random seeding mechanism is compared with heuristic seeding methods.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/CESYS.2016.7889978", month = oct, notes = "Also known as \cite{7889978}", } @InProceedings{Babu:2023:ICAEECI, author = "Nithish Babu M and Preethi P", booktitle = "2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)", title = "{OCR-Based} Multi-class Classification of Hate Speech in Images", year = "2023", month = "19-20 " # oct, address = "Tiruchengode, India", keywords = "genetic algorithms, genetic programming, Training, Social networking (online), Hate speech, User-generated content, Speech recognition, Prediction methods, Decision trees, Random Forest, Naive Bayes, Optical Character Recognition, Multiclass classification, Binary classification", isbn13 = "979-8-3503-4280-2", DOI = "doi:10.1109/ICAEECI58247.2023.10370942", size = "6 pages", abstract = "The depersonalization and anonymity afforded by ubiquitous social media platforms facilitate open discourse, yet also create a potential avenue for hate speech dissemination. The rising incidence of hate speech on these platforms necessitates vigilant monitoring and intervention. However, the sheer volume of user-generated content renders manual oversight infeasible. Technological solutions must be developed to efficiently identify and mitigate hate speech, striking a balance between maintaining open expression and safeguarding against harmful content. Additionally, when using traditional machine learning methodologies as prediction methods, the language being used and the length of the messages provide a barrier. In this study, a Genetic Programming (GP) model for identifying hate speech is presented, where each chromosome acts as a classifier with a universal sentence encoder feature. The performance of the GP model was enhanced by enriching the offspring pool with alternative solutions using a unique mutation strategy that only modifies the feature values in addition to the conventional one-point mutation technique. For the six categories of hate-type hate speech text datasets, the suggested GP model beat all cutting-edge solutions. In contrast to the machine learning models such as Random Forest, Decision Tree, and Naive Bayes which gave the following accuracy of 79.25percent, 77.88percent and 77.33percent whereas GP model outperformed with an accuracy of 97percent. The following evaluation matrices are considered as precision, recall training, and testing accuracy.", notes = "Also known as \cite{10370942}", } @InProceedings{Babuska:2019:GECCO, author = "Robert Babuska", title = "Genetic programming methods for reinforcement learning", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "2--2", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", note = "Invited keynote", keywords = "genetic algorithms, genetic programming, Reinforcement learning, Symbolic regression", isbn13 = "978-1-4503-6111-8", DOI = "doi:10.1145/3321707.3326935", size = "1 page", abstract = "Reinforcement Learning (RL) algorithms can be used to optimally solve dynamic decision-making and control problems. With continuous valued state and input variables, RL algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learnt, and they require significant trial and error tuning of their meta-parameters. In addition, results obtained with deep neural networks suffer from the lack of reproducibility. In this talk, we discuss a family of new approaches to constructing smooth approximators for RL by means of genetic programming and more specifically by symbolic regression. We show how to construct process models and value functions represented by parsimonious analytic expressions using state-of-the-art algorithms, such as Single Node Genetic Programming and Multi-Gene Genetic Programming. We will include examples of non-linear control problems that can be successfully solved by reinforcement learning with symbolic regression and illustrate some of the challenges this exciting field of research is currently facing.", notes = "Abstract of keynote. Also known as \cite{3326935} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Proceedings{Bacardit:2011:GECCOcomp, title = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation", year = "2011", editor = "Jaume Bacardit and Ivan Tanev and Joern Mehnen and Thomas Bartz-Beielstein and David Davis and Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen and Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz and Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis and Stephen L. Smith and Stefano Cagnoni and Robert Patton and William Rand and Forrest Stonedahl and Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward and Maria J. Blesa and Christian Blum and Steven Gustafson and Ekaterina Vladislavleva and Mark Hauschild and Martin Pelikan and Ender Ozcan and Andrew J. Parkes and Jonathan Rowe and Pascal Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro and Miguel Nicolau and Darrell Whitley", address = "Dublin, Ireland", publisher_address = "New York, NY, USA", month = "12-16 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Bioinformatics, computational, systems, and synthetic biology, Digital entertainment technologies and arts, Evolutionary combinatorial optimization and metaheuristics, Estimation of distribution algorithms, Evolutionary multiobjective optimization, Evolution strategies and evolutionary programming, Genetics based machine learning, Generative and developmental systems, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Evolutionary computation in practice, Evolutionary computation techniques for constraint handling, Fourteenth international workshop on learning classifier systems, Computational intelligence on consumer games and graphics hardware (CIGPU), Medical applications of genetic and evolutionary computation (MedGEC), Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop, 1st workshop on evolutionary computation for designing generic algorithms, Bio-inspired solutions for wireless sensor networks (GECCO BIS-WSN 2011), 3rd symbolic regression and modeling workshop for GECCO 2011, Optimization by building and using probabilistic models (OBUPM-2011), Scaling behaviours of landscapes, parameters and algorithms, GreenIT evolutionary computation, Graduate students workshop, Late breaking abstracts, Specialized techniques and applications, Tutorials", isbn13 = "978-1-4503-0690-4", URL = "http://dl.acm.org/citation.cfm?id=2001858", size = "approx 1508 pages", notes = "Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Bach:2019:ICEAA, author = "Thuan Bui Bach and Linh Ho Manh and Kiem Nguyen Khac and Michele Beccaria and Andrea Massaccesi and Riccardo Zich", booktitle = "2019 International Conference on Electromagnetics in Advanced Applications (ICEAA)", title = "Evolved Design of Microstrip Patch Antenna by Genetic Programming", year = "2019", pages = "1393--1397", month = sep, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICEAA.2019.8879155", abstract = "An evolved antenna is an antenna designed fully or substantially by an automatic computer design program that uses an evolutionary algorithm. In this article, we present our work in using evolutionary algorithms to an automated antenna design system. Based on the primal individual structure of genetic programming (GP) is a tree form, a new data-structure computer program which can be represented as entire parameters of an antenna has been explored. The first experiment has been done successfully for automated design the antenna for 5G mobile device which is microstrip patch antenna (MPA) that operates at 3.5 GHz with 50-160 MHz of bandwidth. The innovative MPAs are obtained by this software. This work shows a great potential of the development of the intelligent computer program for automated synthesis antenna as well as conformal antenna.", notes = "Hanoi University of Science and Technology, Hanoi, Vietnam Also known as \cite{8879155}", } @InCollection{bachman:2000:UGAVLGA, author = "Brandon M. Bachman", title = "Using the Genetic Algorithm with a Variable Length Genome for Architectural", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "33--39", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", notes = "part of \cite{koza:2000:gagp}", } @Article{back:1997:survey, author = "Thomas Back and U. Hammel and H.-P. Schwefel", title = "Evolutionary computation: comments on the history and current state", journal = "IEEE Transactions on Evolutionary Computation", year = "1997", volume = "1", number = "1", pages = "3--17", month = apr, keywords = "genetic algorithms, genetic programming, EA, CS, evolutionstrategies, EP", ISSN = "1089-778X", URL = "http://ls11-www.cs.uni-dortmund.de/people/schwefel/publications/BHS97.ps.gz", size = "15 pages", abstract = "Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete", notes = "Reference Cited: 220 CODEN: ITEVF5", } @InCollection{back:2000:EC1, author = "Thomas Back and David B. Fogel and Darrell Whitley and Peter J. Angeline", title = "Mutation operators", booktitle = "Evolutionary Computation 1 Basic Algorithms and Operators", publisher = "Institute of Physics Publishing", year = "2000", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "32", pages = "237--255", address = "Bristol", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0664-5", URL = "http://www.crcpress.com/product/isbn/9780750306645", URL = "https://www.routledge.com/Evolutionary-Computation-1-Basic-Algorithms-and-Operators/Baeck-Fogel-Michalewicz/p/book/9780750306645", notes = "Section 32.5 Parse trees p248--250. Grow, shrink, switch, cycle, Gaussian mutation of numbers, enforce size limit, enforce type match (STGP \cite{montana:stgpEC})", size = "19 pages", } @InProceedings{Back:2004:UPP, author = "Thomas Baeck and Ron Breukelaar and Lars Willmes", title = "Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm", booktitle = "Unconventional Programming Paradigms: International Workshop UPP 2004", year = "2004", editor = "Jean-Pierre Banatre and Pascal Fradet and Jean-Louis Giavitto and Olivier Michel", volume = "3566", series = "LNCS", pages = "161--172", address = "Le Mont Saint Michel, France", month = sep # " 15-17", publisher = "Springer", note = "Revised Selected and Invited Papers, 2005", keywords = "genetic algorithms, genetic programming, CA", isbn13 = "978-3-540-31482-0", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.535.7340", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.535.7340", broken = "http://www.liacs.nl/~rbreukel/publications/UPP.pdf", URL = "https://doi.org/10.1007/11527800_13", DOI = "doi:10.1007/11527800_13", size = "12 pages", abstract = "Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been known in computer science since more than 40 years, but their use to program other algorithms is a more recent invention. In this paper, we outline the approach by giving an example where evolutionary algorithms serve to program cellular automata by designing rules for their iteration. Three different goals of the cellular automata designed by the evolutionary algorithm are outlined, and the evolutionary algorithm indeed discovers rules for the CA which solve these problems efficiently.", } @InProceedings{Baeck:2023:GPTP, author = "Thomas Baeck", title = "Automatic Algorithm Configuration for Expensive Optimization Tasks", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Ting Hu and Charles Ofria and Leonardo Trujillo and Stephan Winkler", series = "Genetic and Evolutionary Computation", address = "Michigan State University, USA", month = jun # " 1-3", note = "Keynote", keywords = "genetic algorithms, genetic programming", notes = " Part of \cite{Hu:2023:GPTP} Not in published book", } @InProceedings{backer:1996:WSC, author = "Gerriet Backer", title = "Learning with missing data using Genetic Programming", booktitle = "The 1st Online Workshop on Soft Computing (WSC1)", year = "1996", broken = "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/", month = "19--30 " # aug, organisation = "Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)", publisher = "Nagoya University, Japan", keywords = "genetic algorithms, genetic programming, Machine learning, Missing data, Strongly Typed Genetic Programming, STGP", broken = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/d041.html", URL = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz", abstract = "Learning with imprecise or missing data has been a major challenge for machine learning. A new approach using Strongly Typed Genetic Programming is proposed here, which uses extra computations based on other input data to approximate the missing values. It eliminates the need for pre-processing and makes use of correlations between the input data. The decision process itself and the handling of unknown data can be extracted from the resulting program for an analysis afterwards. Comparing it to an alternative approach on a simple example shows the usefulness of this approach.", size = "5 pages", notes = "Adds {"}unknown{"} data type to STGP. demo on iris classification problem (see discussion on WSC1 pages) email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp", } @InProceedings{conf/evoW/BackmanD08, title = "A Generative Representation for the Evolution of Jazz Solos", author = "Kjell Backman and Palle Dahlstedt", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#BackmanD08", booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT}, Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops", publisher = "Springer", year = "2008", volume = "4974", editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni {Di Caro} and Rolf Drechsler and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang", isbn13 = "978-3-540-78760-0", pages = "371--380", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78761-7_40", address = "Naples", month = "26-28 " # mar, keywords = "genetic algorithms, genetic programming", abstract = "This paper describes a system developed to create computer based jazz improvisation solos. The generation of the improvisation material uses interactive evolution, based on a dual genetic representation: a basic melody line representation, with energy constraints ({"}rubber band{"}) and a hierarchic structure of operators that processes the various parts of this basic melody. To be able to listen to and evaluate the result in a fair way, the computer generated solos have been imported into a musical environment to form a complete jazz composition. The focus of this paper is on the data representations developed for this specific type of music. This is the first published part of an ongoing research project in generative jazz, based on probabilistic and evolutionary strategies.", } @InProceedings{Badan:2019:TPNC, author = "Filip Badan and Lukas Sekanina", title = "Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution", booktitle = "International Conference on Theory and Practice of Natural Computing, TPNC 2019", year = "2019", editor = "Carlos Martin-Vide and Geoffrey Pond and Miguel A. Vega-Rodriguez", volume = "11934", series = "LNCS", pages = "109--121", address = "Kingston, ON, Canada", month = "9-11 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithm Convolutional neural network Neuroevolution Embedded Systems Energy Efficiency", isbn13 = "978-3-030-34499-3", DOI = "doi:10.1007/978-3-030-34500-6_7", size = "13 pages", abstract = "Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems: MNIST and CIFAR-10.", } @InProceedings{1277299, author = "Mohamed Bahy Bader-El-Den and Riccardo Poli", title = "A GP-based hyper-heuristic framework for evolving 3-SAT heuristics", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1749--1749", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1749.pdf", DOI = "doi:10.1145/1276958.1277299", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming: Poster, heuristics, hyper heuristic, SAT", abstract = "We present, GP-HH, a framework for evolving local search 3-SAT heuristics based on GP. Evolved heuristics are compared against well-known SAT solvers with very encouraging results.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{bader-el-den07:_gener_sat_local_searc_heuris, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Generating SAT Local-Search Heuristics using a GP Hyper-Heuristic Framework", booktitle = "Evolution Artificielle, 8th International Conference", year = "2007", editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", volume = "4926", series = "Lecture Notes in Computer Science", pages = "37--49", address = "Tours, France", month = "29-31 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-79304-5", DOI = "doi:10.1007/978-3-540-79305-2_4", abstract = "We present GP-HH, a framework for evolving local-search 3-SAT heuristics based on GP. The aim is to obtain disposable heuristics which are evolved and used for a specific subset of instances of a problem. We test the heuristics evolved by GP-HH against well-known local-search heuristics on a variety of benchmark SAT problems. Results are very encouraging.", notes = "EA'07", } @InProceedings{Bader-El-Den:2008:evocop, title = "Inc*: An Incremental Approach for Improving Local Search Heuristics", author = "Mohamed Bahy Bader-El-Den and Riccardo Poli", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evocop2008.html#Bader-El-DenP08", booktitle = "Proceedings of the 8th European Conference, Evolutionary Computation in Combinatorial Optimization, Evo{COP}", publisher = "Springer", year = "2008", volume = "4972", editor = "Jano I. van Hemert and Carlos Cotta", isbn13 = "978-3-540-78603-0", pages = "194--205", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78604-7_17", address = "Naples, Italy", month = mar # " 26-28", keywords = "genetic algorithms, genetic programming", notes = "also known as \cite{conf/evoW/Bader-El-DenP08}", } @InProceedings{Bader-El-Den:2008:WCCI, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Analysis and Extension of the Inc* on the Satisfiability Testing Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3342--3349", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SAT", isbn13 = "978-1-4244-1823-7", file = "EC0725.pdf", DOI = "doi:10.1109/CEC.2008.4631250", abstract = "Inc (star) is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc*. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc* is a competitive approach. In this paper we enhance Inc* and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithm's behaviour.", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InCollection{Bader-El-Den:2008:GPTP, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Evolving Effective Incremental Solvers for {SAT} with a Hyper-Heuristic Framework Based on Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "11", pages = "163--179", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, hyper-heuristic, HH, Inc, SAT, heuristics", isbn13 = "978-0-387-87622-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.206.3331.pdf", DOI = "doi:10.1007/978-0-387-87623-8_11", size = "16 pages", abstract = "Hyper-heuristics could simply be defined as heuristics to choose other heuristics. In other words, they are methods for combining existing heuristics to generate new ones. we use a grammar-based genetic programming hyper-heuristic framework. The framework is used for evolving effective incremental solvers for SAT. The evolved heuristics perform very well against well-known local search heuristics on a variety of benchmark SAT problems.", notes = "part of \cite{Riolo:2008:GPTP} published in 2009. Also known as \cite{El-den:2008:GPTP} \cite{Bader-el-den_evolvingeffective} Department of Computing and Electronic Systems, University of Essex", } @InProceedings{Bader-El-Den:2008:gecco, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Evolving Heuristics with Genetic Programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "601--602", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p601.pdf", DOI = "doi:10.1145/1389095.1389212", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, heuristics, hyperheuristics, Inc*, SAT, Evolutionary combinatorial optimisation: Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389212}", } @InProceedings{BaderElDen:2009:cec, author = "Mohamed {Bader El Den} and Riccardo Poli", title = "Grammar-Based Genetic Programming for Timetabling", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2532--2539", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P677.pdf", DOI = "doi:10.1109/CEC.2009.4983259", abstract = "We present a grammar-based genetic programming framework for the solving the timetabling problem via the evolution of constructive heuristics. The grammar used for producing new generations is based on graph colouring heuristics that have previously proved to be effective in constructing timetables as well as different slot allocation heuristics. The framework is tested on a widely used benchmarks in the field of exam time-tabling and compared with highly-tuned state-of-the- art approaches. Results shows that the framework is very competitive with other constructive techniques.", keywords = "genetic algorithms, genetic programming, hyperheuristics", notes = "graph colouring, exam timetabling. Grammar used to control mixing of existing well established heuristics by GP to evolve a population of hyperheuristic. To cope with randomness in existing low level heuristics, each GP individual is run several times. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @PhdThesis{Bader-El-Den:thesis, author = "Mohamed Bahr {Bader El Den}", title = "Investigation of the role of Genetic Programming in a Hyper-Heuristic Framework for Combinatorial Optimization Problems", school = "School of Computer Science and Electronic Engineering, University of Essex", year = "2009", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=25&uin=uk.bl.ethos.510512", notes = "http://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=5691", } @InProceedings{conf/ijcci/Bader-El-DenF09, author = "Mohamed Bahy Bader-El-Den and Shaheen Fatima", title = "Evolving Effective Bidding Functions for Auction based Resource Allocation Framework", year = "2009", booktitle = "International Conference on Evolutionary Computation (ICEC 2009)", editor = "Agostinho Rosa", address = "Madeira, Portugal", month = "5-7 " # oct, publisher = "INSTICC Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-674-014-6", URL = "https://www.researchgate.net/publication/221616501_Evolving_Effective_Bidding_Functions_for_Auction_based_Resource_Allocation_Framework", URL = "https://researchportal.port.ac.uk/portal/en/publications/evolving-effective-bidding-functions-for-auction-based-resource-allocation-framework(5323b5d3-0e0a-446a-91d5-b64bb53a592f)/export.html", broken = "http://eprints.port.ac.uk/id/eprint/3738", bibdate = "2010-03-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2009.html#Salehi-AbariW09", abstract = "In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling.", notes = "IJCCI broken http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm Appears not to be in the book, Computational Intelligence ISBN:978-3-642-20205-6, published by springer.", } @Article{journals/memetic/Bader-El-DenPF09, title = "Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework", author = "Mohamed Bahy Bader-El-Den and Riccardo Poli and Shaheen Fatima", journal = "Memetic Computing", year = "2009", number = "3", volume = "1", pages = "205--219", keywords = "genetic algorithms, genetic programming, timetabling, Hyper-heuristics, Heuristics", DOI = "doi:10.1007/s12293-009-0022-y", bibdate = "2009-12-11", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/memetic/memetic1.html#Bader-El-DenPF09", abstract = "This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.", } @InProceedings{Bader-El-Den:2010:EuroGP, author = "Mohamed Bader-El-Den and Shaheen Fatima", title = "Genetic Programming for Auction Based Scheduling", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "256--267", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_22", abstract = "In this paper, we present a genetic programming (GP) framework for evolving agent's binding function (GPAuc) in a resource allocation problem. The framework is tested on the exam timetabling problem (ETP). There is a set of exams, which have to be assigned to a predefined set of slots and rooms. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam timetabling.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{1277272, author = "Khaled M. S. Badran and Peter I. Rockett", title = "The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1551--1558", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1551.pdf", DOI = "doi:10.1145/1276958.1277272", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, bloat, diversity preservation, multiobjective optimisation, population collapse", abstract = "It has been observed previously that genetic programming populations can collapse to all single node trees when a parsimony measure (tree node count) is used in a multiobjective setting. We have investigated the circumstances under which this can occur for both the 6-parity boolean learning task and a range of benchmark machine learning problems. We conclude that mutation is an important and we believe a hitherto unrecognised factor in preventing population collapse in multiobjective genetic programming; without mutation we routinely observe population collapse. From systematic variation of the mutation operator, we conclude that a necessary condition to avoid collapse is that mutation produces, on average, an increase in tree sizes (bloating) at each generation which is then counterbalanced by the parsimony pressure applied during selection. Finally, we conclude that the use of a genotype diversity preserving mechanism is ineffective at preventing population collapse.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{conf/eurogp/BadranR08, title = "Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction", author = "Khaled M. S. Badran and Peter Rockett", bibdate = "2008-04-15", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#BadranR08", booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008", address = "Naples", month = "26-28 " # mar, publisher = "Springer", year = "2008", volume = "4971", editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino", isbn13 = "978-3-540-78670-2", pages = "301--311", series = "Lecture Notes in Computer Science", DOI = "doi:10.1007/978-3-540-78671-9_26", keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008", } @Article{Badran:2009:GPEM, author = "Khaled Badran and Peter I. Rockett", title = "The influence of mutation on population dynamics in multiobjective genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2010", volume = "11", number = "1", pages = "5--33", month = mar, keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming, Population collapse, Mutation, Population dynamics, MOGP, bloat", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9084-3", abstract = "Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity.", notes = "Steady-state algorithm depth-fair crossover/depth-fair mutation", } @PhdThesis{Badran:thesis, author = "Khaled Badran", title = "Multi-objective genetic programming with an application to intrusion detection in computer networks", school = "University of Sheffield", year = "2009", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.505474", abstract = "The widespread connectivity of computers all over the world has encouraged intruders to threaten the security of computing systems by targeting the confidentiality and integrity of information, and the availability of systems. Traditional techniques such as user authentication, data encryption and firewalls have been implemented to defend computer security but still have problems and weak points. Therefore the development of intrusion detection systems (EDS) has aroused much research interest with the aim of preventing both internal and external attacks. In misuse-based, network-based IDS, huge history files of computer network usage are analysed hi order to extract useful information, and rules are extracted to judge future network usage as legal or illegal. This process is considered as data mining for intrusion detection in computer networks.", notes = "uk.bl.ethos.505474", } @Article{Badran:2011:GPEM, author = "Khaled Badran and Peter Rockett", title = "Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "1", pages = "33--63", month = mar, note = "Special Section on Evolutionary Algorithms for Data Mining", keywords = "genetic algorithms, genetic programming, Multi-class pattern classification, Feature extraction, Feature selection, Multi-objective genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-011-9143-4", size = "31 pages", abstract = "In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform the input space into a new, multi-dimensional decision space to increase the discrimination between all classes; the number of dimensions of this decision space is optimised as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single decision space has significant computational advantages compared to k -class-to-2-class decompositions; a key design requirement in this work has been the ability to incorporate changing priors and/or costs associated with mislabelling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating solution complexity as an independent objective to be minimised in addition to the main objective of the misclassification error. We thus give preference to simpler solutions which tend to generalise well on unseen data, in accordance with Occam's Razor. We obtain classification results on a series of benchmark problems which are essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly competitive with the KDD-99 Cup winner but with a significantly simpler classification framework.", affiliation = "Vision and Information Engineering Research Group, Department of Electronic and Electrical Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3D UK", } @Article{Badran:2017:IJCA, author = "Khaled Badran and Alaa Rohim", title = "Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection", journal = "International Journal of Computer Applications", year = "2017", volume = "168", number = "1", pages = "37--43", month = jun, keywords = "genetic algorithms, genetic programming, Pattern Recognition, Classification, Network Intrusion, Feature Extraction, Neural Network, ANN, Support Vector Machines, SVM, Decision Trees", publisher = "Foundation of Computer Science (FCS), NY, USA", ISSN = "0975-8887", URL = "https://www.ijcaonline.org/archives/volume168/number1/27841-2017914276", URL = "https://www.ijcaonline.org/archives/volume168/number1/badran-2017-ijca-914276.pdf", DOI = "doi:10.5120/ijca2017914276", size = "7 pages", abstract = "we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without using multi-objective genetic programming in the feature extraction phase. We argue that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion KDD-99 cup dataset.Then,the experiments were repeated by adding the feature extraction phase.Theresults ofthetwo approachesare compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced.", notes = "Also known as \cite{10.5120/ijca2017914276} www.ijcaonline.org Military Technical College, Cairo", } @InProceedings{Badura:2014:ELEKTRO, author = "Stefan Badura and Milan Fratrik and Ondrej Skvarek and Martin Klimo", title = "Bimodal vowel recognition using fuzzy logic networks - naive approach", booktitle = "ELEKTRO, 2014", year = "2014", month = may, pages = "22--25", keywords = "genetic algorithms, genetic programming, AVSR, bimodal, fusion, speech recognition, fuzzy logic", DOI = "doi:10.1109/ELEKTRO.2014.6847864", size = "4 pages", abstract = "We describe an audio visual speech recognition system (AVSR) based on fuzzy logic networks. Our system is able to recognise any time sequences and achieves positive results in the task of vowel recognition. Proposed design relies on new model and methods for training fuzzy logic circuits. We combine a simple combinatorial circuit, trained with genetic programming, with a fuzzy logic memory. Combinatorial structures are combined to a fuzzy logic network. This approach leads to design of a hierarchical, massive and layered structure for dynamic signal recognition. An AVSR system is effectively composed by fusion of such networks for audio and lip-reading parts.", notes = "Fac. of Manage. Sci. & Inf., Univ. of Zilina, Zilina, Slovakia Also known as \cite{6847864}", } @Article{journals/soco/BaeJKKKHM10, title = "Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling", author = "Hyeon Bae and Tae-Ryong Jeon and Sungshin Kim and Hyun-Soo Kim and DongSeop Kim and Seung Soo Han and Gary S. May", journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications", year = "2010", number = "2", volume = "14", pages = "161--169", keywords = "genetic algorithms, genetic programming, Neural network, Particle swarm optimization, Silicon solar cell fabrication", ISSN = "1432-7643", DOI = "doi:10.1007/s00500-009-0438-9", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco14.html#BaeJKKKHM10", abstract = "This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.", } @InProceedings{bael:1999:TJSPSSBESE, author = "Patrick Van Bael and Dirk Devogelaere and M. Rijckaert", title = "The Job Shop Problem Solved with Simple, Basic Evolutionary Search Elements", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "665--669", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Baele:2009:cec, author = "Guy Baele and Nicolas Bredeche and Evert Haasdijk and Steven Maere and Nico Michiels and Yves {Van de Peer} and Christopher Schwarzer and Ronald Thenius", title = "Open-Ended On-Board Evolutionary Robotics for Robot Swarms", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1123--1130", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P485.pdf", DOI = "doi:10.1109/CEC.2009.4983072", abstract = "The SYMBRION project stands at the crossroads of Artificial Life and Evolutionary Robotics: a swarm of real robots undergoes online evolution by exchanging information in a decentralized Evolutionary Robotics Scheme: the diffusion of each individual's genotype depends both on its ability to survive in an unknown environment as well as its ability to maximize mating opportunities during its lifetime, which suggests an implicit fitness. This paper presents early research and prospective ideas in the context of large-scale swarm robotics projects, focusing on the open-ended evolutionary approach in the SYMBRION project. One key issue of this work is to perform on-board evolution in a spatially distributed population of robots. A real-world experiment is also described which yields important considerations regarding open-ended evolution with real autonomous robots.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Baeta:2021:evoapplications, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "{TensorGP} - Genetic Programming Engine in {TensorFlow}", booktitle = "24th International Conference, EvoApplications 2021", year = "2021", month = "7-9 " # apr, editor = "Pedro Castillo and Juanlu Jimenez-Laredo", series = "LNCS", volume = "12694", publisher = "Springer Verlag", address = "virtual event", pages = "763--778", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU computing", isbn13 = "978-3-030-72698-0", DOI = "doi:10.1007/978-3-030-72699-7_48", abstract = "we resort to the TensorFlow framework to investigate the benefits of applying data vectorisation and fitness caching methods to domain evaluation in Genetic Programming. For this purpose, an independent engine was developed, TensorGP, along with a testing suite to extract comparative timing results across different architectures and amongst both iterative and vectorized approaches. Our performance benchmarks demonstrate that by exploiting the TensorFlow eager execution model, performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware when compared to a standard iterative approach.", notes = "http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021", } @InProceedings{Baeta:2021:GECCO, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "Speed Benchmarking of Genetic Programming Frameworks", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "768--775", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU Computing", isbn13 = "9781450383509", DOI = "doi:10.1145/3449639.3459335", size = "8 pages", abstract = "Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate this issue, data vectorisation, in particular, is arguably still the most attractive strategy due to the parallel nature of GP. In this work,we employ a series of benchmarks meant to compare both the performance and evolution capabilities of different vectorised and iterative implementation approaches across several existing frameworks. Namely, TensorGP, a novel open source engine written in Python, is shown to greatly benefit from the TensorFlow library to accelerate the domain evaluation phasein GP. The presented performance benchmarks demonstratethat the TensorGP engine manages to pull ahead, with relativespeedups above two orders of magnitude for problems with ahigher number of fitness cases. Additionally, as a consequenceof being able to compute larger domains, we argue that TensorGP performance gains aid the discovery of more accurate candidate solutions.", notes = "University of Coimbra GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @Article{baeta:2022:SN, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "Exploring Genetic Programming in {TensorFlow} with {TensorGP}", journal = "SN Computer Science", year = "2022", volume = "3", number = "2", keywords = "genetic algorithms, genetic programming, GPU", URL = "http://link.springer.com/article/10.1007/s42979-021-01006-8", DOI = "doi:10.1007/s42979-021-01006-8", } @InProceedings{baeta:2024:GECCOcomp, author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado", title = "Exploring Evolutionary Generators within Generative Adversarial Networks", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin", pages = "251--254", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary computation, generative adversarial networks, TGPGAN, Evolutionary Machine Learning: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654348", size = "4 pages", abstract = "Since their introduction, Generative Adversarial Networks (GANs) have represented the bulk of approaches used in image generation. Before GANs, such approaches used Machine Learning (ML) exclusively to tackle the training problems inherent to GANs. However, in recent years, evolutionary approaches have been making a comeback, not only across the field of ML but in generative modelling specifically. Successes in GPU-accelerated Genetic Programming (GP) led to the introduction of the TGPGAN framework, which used GP as a replacement for the deep convolutional network conventionally used as a GAN generator. In this paper, we delve further into the generative capabilities of evolutionary computation within adversarial models and extend the study performed in TGPGAN to analyse other evolutionary approaches. Similarly to TGPGAN, the presented approaches replace the generator component of a Deep Convolutional GAN (DCGAN): one with a line-drawing Genetic Algorithm (GA) and another with a Compositional Pattern Producing Network (CPPN). Our comparison of generative performance shows that the GA used manages to perform competitively with the original framework. More importantly, this work showcases the viability of other evolutionary approaches other than GP for the purpose of image generation.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{baeza-yates:2018:DESA, author = "Ricardo Baeza-Yates and Alfredo Cuzzocrea and Domenico Crea and Giovanni {Lo Bianco}", title = "Learning Ranking Functions by Genetic Programming Revisited", booktitle = "Database and Expert Systems Applications", year = "2018", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-319-98812-2_34", DOI = "doi:10.1007/978-3-319-98812-2_34", } @InProceedings{DBLP:conf/sac/Baeza-YatesCCB19, author = "Ricardo Baeza-Yates and Alfredo Cuzzocrea and Domenico Crea and Giovanni Lo Bianco", editor = "Chih-Cheng Hung and George A. Papadopoulos", title = "An effective and efficient algorithm for ranking web documents via genetic programming", booktitle = "Proceedings of the 34th {ACM/SIGAPP} Symposium on Applied Computing, {SAC} 2019, Limassol, Cyprus, April 8-12, 2019", pages = "1065--1072", publisher = "{ACM}", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3297280.3297385", DOI = "doi:10.1145/3297280.3297385", timestamp = "Sun, 22 Sep 2019 01:00:00 +0200", biburl = "https://dblp.org/rec/conf/sac/Baeza-YatesCCB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{DBLP:journals/peerj-cs/BaggioLCM23, author = "Cecilia Baggio and Carlos M. Lorenzetti and Rocio L. Cecchini and Ana Gabriela Maguitman", title = "Multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall", journal = "PeerJ Comput. Sci.", volume = "9", pages = "e1710", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.7717/peerj-cs.1710", DOI = "doi:10.7717/PEERJ-CS.1710", timestamp = "Sun, 31 Dec 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/peerj-cs/BaggioLCM23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{baghbani:2023:AS, author = "Abolfazl Baghbani and Minh Duc Nguyen and Ali Alnedawi and Nick Milne and Thomas Baumgartl and Hossam Abuel-Naga", title = "Improving Soil Stability with Alum Sludge: An {AI-Enabled} Approach for Accurate Prediction of California Bearing Ratio", journal = "Applied Sciences", year = "2023", volume = "13", number = "8", pages = "Article No. 4934", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/13/8/4934", DOI = "doi:10.3390/app13084934", abstract = "Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.", notes = "also known as \cite{app13084934}", } @Article{baghbani:2023:Geotechnics, author = "Abolfazl Baghbani and Amin Soltani and Katayoon Kiany and Firas Daghistani", title = "Predicting the Strength Performance of {Hydrated-Lime} Activated Rice Husk {Ash-Treated} Soil Using Two {Grey-Box} Machine Learning Models", journal = "Geotechnics", year = "2023", volume = "3", number = "3", pages = "894--920", keywords = "genetic algorithms, genetic programming", ISSN = "2673-7094", URL = "https://www.mdpi.com/2673-7094/3/3/48", DOI = "doi:10.3390/geotechnics3030048", abstract = "Geotechnical engineering relies heavily on predicting soil strength to ensure safe and efficient construction projects. This paper presents a study on the accurate prediction of soil strength properties, focusing on hydrated-lime activated rice husk ash (HARHA) treated soil. To achieve precise predictions, the researchers employed two grey-box machine learning models--classification and regression trees (CART) and genetic programming (GP). These models introduce innovative equations and trees that readers can readily apply to new databases. The models were trained and tested using a comprehensive laboratory database consisting of seven input parameters and three output variables. The results indicate that both the proposed CART trees and GP equations exhibited excellent predictive capabilities across all three output variables--California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (Rvalue) (according to the in-situ cone penetrometer test). The GP proposed equations, in particular, demonstrated a superior performance in predicting the UCS and Rvalue parameters, while remaining comparable to CART in predicting the CBR. This research highlights the potential of integrating grey-box machine learning models with geotechnical engineering, providing valuable insights to enhance decision-making processes and safety measures in future infrastructural development projects.", notes = "also known as \cite{geotechnics3030048}", } @Article{BAGHERI:2019:Measurement, author = "Ali Bagheri and Ali Nazari and Jay Sanjayan", title = "The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by {ANN} and {GP}", journal = "Measurement", volume = "141", pages = "241--249", year = "2019", ISSN = "0263-2241", DOI = "doi:10.1016/j.measurement.2019.03.001", URL = "http://www.sciencedirect.com/science/article/pii/S0263224119302106", keywords = "genetic algorithms, genetic programming, Boron-activated geopolymer, Artificial intelligence, Aluminosilicate, Machine learning, Energy and resources", abstract = "This paper employs artificial intelligence methods in order to create a function for compressive strength of the boroaluminosilicate geopolymers based on mixture proportion variables. Boroaluminosilicate geopolymers (BASGs), a group of boron-based alkali-activated materials, not only minimise the carbon footprint in the construction industry but also decrease the consumption of energy and natural resources. Australian fly ash and iron making slag are activated in sodium and boron-based alkaline medium in order to produce the geopolymer binders. The current study employs artificial neural network in order to classify the collected data into train, test, and validation followed by genetic programming for developing a function to approximate the compressive strength of BASGs. The independent variables comprise the percentage of fly ash and slag as well as ratios of boron, silicon, and sodium ions in the alkaline solution. The performance of each method is assessed by the acquired regression and the error parameters. The obtained results show that the percent of silicon and boron ions, with positive direct correlation and the largest power in the function respectively, have the most significant effects on the compressive strength of BASG. The assessment factors, including R-squared 0.95 and root-mean-square error 0.07 in the testing data, indicate that the model explains all the variability of the response data around its mean. It implies a high level of accuracy and reliability for the model", } @InProceedings{DBLP:conf/iscc/BagheriD06, author = "Ebrahim Bagheri and Hossein Deldari", title = "Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm", booktitle = "Proceedings of the 11th IEEE Symposium on Computers and Communications (ISCC 2006)", year = "2006", editor = "Paolo Bellavista and Chi-Ming Chen and Antonio Corradi and Mahmoud Daneshmand", pages = "675--680", address = "Cagliari, Sardinia, Italy", month = "26-29 " # jun, publisher = "IEEE Computer Society", keywords = "genetic algorithms", ISBN = "0-7695-2588-1", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1109/ISCC.2006.57", abstract = "Genetic Algorithms are very powerful search methods that are used in different optimisation problems. Parallel versions of genetic algorithms are easily implemented and usually increase algorithm performance [4]. Fuzzy control as another optimisation solution along with genetic algorithms can significantly increase algorithm performance. Two variations for genetic algorithm and fuzzy system composition exist. In the first approach Genetic algorithms are used to optimise and model the structure of fuzzy systems through knowledge base or membership function design while the second approach exploits fuzzy to dynamically supervise genetic algorithm performance by speedily reaching an optimal solution. In this paper we propose a new method for fuzzy parallel genetic algorithms, in which a parallel client-server single population fuzzy genetic algorithm is configured to optimise the performance of the first three Dejong functions in order to reach a global solution in the least possible iterations. Simulations show much improvement in genetic algorithm performance evaluation.", } @Article{journals/es/BagheriGBS13, author = "Mehdi Bagheri and Amir Hossein Gandomi and Mehrdad Bagheri and Mohcen Shahbaznezhad", title = "Multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials", journal = "Expert Systems", year = "2013", volume = "30", number = "1", pages = "66--78", month = feb, keywords = "genetic algorithms, genetic programming, nitro-energetic materials, multi-expression programming, formation enthalpy, QSPR", DOI = "doi:10.1111/j.1468-0394.2012.00623.x", size = "33 pages", abstract = "There has been considerable interest in predicting the properties of nitro-energetic materials to improve their performance. Not to mention insightful physical knowledge, computational-aided molecular studies can expedite the synthesis of novel energetic materials through cost reduction labours and risky experimental tests. In this paper, quantitative structure-property relationship based on multi-expression programming employed to correlate the formation enthalpies of frequently used nitro-energetic materials with their molecular properties. The simple yet accurate obtained model is able to correlate the formation enthalpies of nitro-energetic materials to their molecular structure with the accuracy comparable to experimental precision.", } @Article{Bagheri:2015:SAR_QSAR_ER, author = "M. Bagheri and T. N. G. Borhani and A. H. Gandomi and Z. A. Manan", title = "A simple modelling approach for prediction of standard state real gas entropy of pure materials", journal = "SAR and QSAR in Environmental Research", year = "2014", volume = "25", number = "9", pages = "695--710", keywords = "genetic algorithms, genetic programming, linear genetic programming (LGP), standard state absolute entropy of real gases (SSTD), feed forward neural network (FFNN), quantitative structure entropy relationship, exergy analysis", URL = "http://www.tandfonline.com/doi/abs/10.1080/1062936X.2014.942356", URL = "http://www.tandfonline.com/doi/full/10.1080/1062936X.2014.942356", DOI = "doi:10.1080/1062936X.2014.942356", abstract = "The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r2) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r-squared to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value.", } @Article{BAGHERI:2019:PSEP, author = "Majid Bagheri and Ali Akbari and Sayed Ahmad Mirbagheri", title = "Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review", journal = "Process Safety and Environmental Protection", volume = "123", pages = "229--252", year = "2019", keywords = "genetic algorithms, genetic programming, Membrane bioreactors, Membrane fouling, Artificial intelligence, Machine learning, Control system", ISSN = "0957-5820", DOI = "doi:10.1016/j.psep.2019.01.013", URL = "http://www.sciencedirect.com/science/article/pii/S0957582018310863", abstract = "This paper critically reviews all artificial intelligence (AI) and machine learning (ML) techniques for the better control of membrane fouling in filtration processes, with the focus on water and wastewater treatment systems. Artificial neural networks (ANNs), fuzzy logic, genetic programming and model trees were found to be four successfully employed modeling techniques. The results show that well-known ANNs such as multilayer perceptron and radial basis function can predict membrane fouling with an R2 equal to 0.99 and an error approaching zero. Genetic algorithm (GA) and particle swarm optimization (PSO) are optimization methods successfully applied to optimize parameters related to membrane fouling. These optimization techniques indicated high capabilities in tuning various parameters such as transmembrane pressure, crossflow velocity, feed temperature, and feed pH. The results of this survey demonstrate that hybrid intelligent models using intelligent optimization methods such as GA and PSO for adjusting their weights and functions perform better than single models. Clustering analysis, image recognition, and feature selection are other employed intelligent techniques with positive role in the control of membrane fouling. The application of AI and ML techniques in an advanced control system can reduce the costs of treatment by monitoring of membrane fouling, and taking the best action when necessary", keywords = "genetic algorithms, genetic programming, Membrane bioreactors, Membrane fouling, Artificial intelligence, Machine learning, Control system", } @InProceedings{baglioni:2000:eampaa, author = "Stefania Baglioni and Celia da Costa Pereira and Dario Sorbello and Andrea G. B. Tettamanzi", title = "An Evolutionary Approach to Multiperiod Asset Allocation", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "225--236", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://mago.crema.unimi.it/pub/BaglioniDaCostaPereiraSorbelloTettamanzi2000.ps", DOI = "doi:10.1007/978-3-540-46239-2_16", abstract = "Portfolio construction can become a very complicated problem, as regulatory constraints, individual investor's requirements, non-trivial indices of risk and subjective quality measures are taken into account, together with multiple investment horizons and cash-flow planning. This problem is approached using a tree of possible scenarios for the future, and an evolutionary algorithm is used to optimize an investment plan against the desired criteria and the possible scenarios. An application to a real defined benefit pension fund case is discussed.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{bagnall:1999:UAABSMUME, author = "A. J. Bagnall and G. D. Smith", title = "Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "774", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/Bagnall1999b.ps.gz", URL = "http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Bagnall1999b.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Bagula:2005:ciS, author = "Antoine B. Bagula and Hong F. Wang", title = "On the Relevance of Using Gene Expression Programming in Destination-Based Traffic Engineering", booktitle = "Computational Intelligence and Security", year = "2005", volume = "3801", series = "Lecture Notes in Computer Science", pages = "224--229", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn_13 = "978-3-540-30818-8", DOI = "doi:10.1007/11596448", abstract = "This paper revisits the problem of Traffic Engineering (TE) to assess the relevance of using Gene Expression Programming (GEP) as a new fine-tuning algorithm in destination-based TE. We present a new TE scheme where link weights are computed using GEP and used as fine-tuning parameters in destination-based path selection. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the GEP algorithm compared to a memetic and the Open Shortest Path First (OSPF) algorithms in a simulated routing environment using the NS packet level simulator. Preliminary results reveal the relative efficiency of GEP compared to the memetic algorithm and OSPF routing.", } @InProceedings{bagula_2006_NOMS, author = "Antoine B. Bagula", title = "Traffic Engineering Next Generation {IP} Networks Using Gene Expression Programming", booktitle = "10th IEEE/IFIP Network Operations and Management Symposium, NOMS 2006", year = "2006", pages = "230--239", address = "Vancouver", organisation = "IFIP", publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1109/NOMS.2006.1687554", size = "10 pages", abstract = "This paper addresses the problem of Traffic Engineering (TE) to evaluate the performance of evolutionary algorithms when used as IP routing optimisers and assess the relevance of using {"}Gene Expression Programming (GEP){"} as a new fine-tuning algorithm in destination- and flow-based TE. We consider a TE scheme where link weights are computed using GEP and used as either fine-tuning parameters in Open Shortest Path First (OSPF) routing or static routing cost in Constraint Based Rouiigg((CRR. Thh reeuutligg SPFa nd CBR algorithms are referred to as OSPFgepand CBRgep. The GEP algorithm is based on a hybrid optimisation model where local search complements the global search implemented by classical evolutionary algorithms to improve the genetic individuals fitness through hill-climbing. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23-, 28- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the OSPFgep, CBRgepalgorithms and OSPFmal, a destination-based routing algorithm where OSPF path selection is driven by the link weights computed by a Memetic Algorithm (MA). We compare the performance achieved by the OSPFgepalgorithm to the performance of the OSPFmaand OSPF algorithms in a simulated routing environment using NS. We also compare the quality of the paths found by the CBRgepalgorithm to the quality of the paths computed by the Constraint Shortest Path First (CSPF) algorithm when routing bandwidth-guaranteed tunnels using connection-level simulation.", } @PhdThesis{urn_nbn_se_kth_diva-4213-2__fulltext, author = "Antoine B. Bagula", title = "Hybrid Routing in Next Generation {IP} Networks: {QoS} Routing Mechanisms and Network Control Strategies", school = "Royal Institute of Technology (KTH)", year = "2006", type = "Doctor of Technology", address = "Stockholm, Sweden", month = dec, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", URL = "http://kth.diva-portal.org/smash/record.jsf?pid=diva2:11272", URL = "http://kth.diva-portal.org/smash/get/diva2:11272/FULLTEXT01.pdf", size = "78 pages", abstract = "Communication networks have evolved from circuit-switched and hop-by-hop routed systems into hybrid data/optical networks using the Internet as a common backbone carrying narrow- and broad-band traffic offered by a multitude of access networks. This data/optical backbone is built around a multi-technology/multi-protocol routing architecture which runs the IP protocols in a collapsed IP stack where ATM and SONET/SDH have been replaced by the suite of Generalised Multiprotocol Label Switching (GMPLS) protocols. A further evolution referred to as ``IP over Photons'' or ``All IP - All Optical'' is expected where ``redundant intermediate layers'' will be eliminated to run IP directly on top of optical cross-connects (OXCs) with the expectation of achieving savings on operation expenditures (OPEX) and capital expenditures (CAPEX). ``IP over Photons'' has been stalled by the immaturity in the control and data plane technologies leading to complex and time-consuming manual network planning and configurations which require a group of ``layer experts'' to operate and maintain a hybrid data/optical network. By making the status of each link and node of a data/optical network visible to a common control, GMPLS protocols have opened the way for automated operation and management allowing the different layers of an IP stack to be managed by a single network operator. GMPLS protocols provide the potential to make more efficient use of the IP backbone by having network management techniques such as Traffic Engineering (TE) and Network Engineering (NE), once the preserve of telecommunications, to be reinvented and deployed to effect different Quality of Service (QoS) requirements in the IP networks. NE moves bandwidth to where the traffic is offered to the network while TE moves traffic to where the bandwidth is available to achieve QoS agreements between the current and expected traffic and the available resources. However,several issues need to be resolved before TE and NE be effectively deployed in emerging and next generation IP networks. These include (1) the identification of QoS requirements of the different network layer interfaces of the emerging and next generation IP stack (2) the mapping of these QoS requirements into QoS routing mechanisms and network control strategies and (3) the deployment of these mechanisms and strategies within and beyond an Internet domain's boundaries to maximise the engineering and economic efficiency. Building upon different frameworks and research fields, this thesis revisits the issue of Traffic and Network Engineering (TE and NE) to present and evaluate the performance of different QoS routing mechanisms and network control strategies when deployed at different network layer interfaces of a hybrid data/optical network where an IP over MPLS network is layered above an MP lambdaS/Fibre infrastructure. These include mechanisms and strategies to be deployed at the IP/MPLS, MPLS/MP LS and MP lambdaS/Fiber network layer interfaces. The main contributions of this thesis are threefold. First we propose and compare the performance of hybrid routing approaches to be deployed in IP/MPLS networks by combining connectionless routing mechanisms used by classical IGP protocols and the connection oriented routing approach borrowed from MPLS. Second, we present QoS routing mechanisms and network control strategies to be deployed at the MPLS/MP lambdaS network layer interface with a focus on contention-aware routing and inter-layer visibility to improve multi-layer optimality and resilience. Finally, we build upon fiber transmission characteristics to propose QoS routing mechanisms where the routing in the MPLS and MP lS layers is conducted by Photonic characteristics of the fiber such as the availability of the physical link and its failure risk group probability.", notes = "Public defence: 2006-12-12, Aula, KTH-Forum, Isafjordsgatan 39, Kista, 13:00 Supervisor: Pehrson, Bjorn (KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS) Opponent: Stiller, Bukhard, Professor (ETH Zurich)", } @Article{BAHADORI:2024:jco2u, author = "Mohammad Keshavarz Bahadori and Reza Golhosseini and Mohammad Shokouhi and Ali T. Zoghi", title = "Mixing gamma-{Al2O3}, silica-{ZIF-8} and activated carbon nanoparticles in aqueous N-methyldiethanolamine+sulfolane as a nanofluid for application on {CO2} absorption", journal = "Journal of CO2 Utilization", volume = "79", pages = "102650", year = "2024", ISSN = "2212-9820", DOI = "doi:10.1016/j.jcou.2023.102650", URL = "https://www.sciencedirect.com/science/article/pii/S2212982023002615", keywords = "genetic algorithms, genetic programming, Hybrid alkanolamine solution, Si-ZIF-8, gamma-AlO, Super activated carbon, Nanofluid", abstract = "Mixing of Al2O3, superactive carbon (SAC), and Si-ZIF-8 nanoparticles in alkanol amine solvents with aqueous and hybrid context were used to study the absorption of carbon dioxide. Hybrid-based nanofluids composed of aqueous N- methyldiethanolamine (MDEA) + Sulfolane (SFL) + small amount of nano particles including-gamma-Al2O3, Si-ZIF-8 and SAC in three concentrations levels, were prepared by weighting each components. The Zeta potential of suspensions was found thatgamma-Al2O3, Si-ZIF-8 and super activated carbon is stable in aqueous solvents. Absorption measurements were performed using the static method at a temperature of T = 313.15 K, gas partial pressure ranges up to about 1.2 MPa, nanoparticle concentrations of 0.02 - 0.50 by weight percent, and fixed concentration of MDEA (40 wtpercent) and SFL (30 wtpercent). It was found that in the hybrid context (i.e., MDEA 40 wtpercent + SFL 30 wtpercent), the addition of gamma-Al2O3 (0.1 wtpercent), Si-ZIF-8 (0.02 wtpercent) and SAC (0.1 wtpercent) has maximum effect on CO2 absorption. In aqueous context (i.e., MDEA 40 wtpercent), nanofluids used in the present study have no significant effect on CO2 capacity. The importance of the addition of SFL in nanofluids was discussed, and the experimental solubility data was modeled using Genetic Programming approach. Average absolute relative deviation (AARD), Coefficient of determination R2, as well as Root mean square error (RMSE) were 10.43percent & 8.94percent, 0.985 & 0.983, 0.051 & 0.054 for training and validating data, respectively. It is found that the determined parameters give satisfactory predictions in the modeling of the solubility", } @Article{BAHADORI:2024:ceja, author = "Mohammad Keshavarz Bahadori and Mohammad Shokouhi and Reza Golhosseini", title = "Measurements of density and viscosity of carbon dioxide-loaded and -unloaded nano-fluids: Experimental, genetic programming and physical interpretation approaches", journal = "Chemical Engineering Journal Advances", volume = "18", pages = "100600", year = "2024", ISSN = "2666-8211", DOI = "doi:10.1016/j.ceja.2024.100600", URL = "https://www.sciencedirect.com/science/article/pii/S2666821124000188", keywords = "genetic algorithms, genetic programming, Density, Viscosity, Si-zif-8, Super activated carbon, Nano-fluid", abstract = "In the present study, the density and viscosity of the CO2-loaded and -unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.percent), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.percent), and super activated carbon (SAC) (0.1 wt.percent) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.percent) +Sulfolane (SFL), 30 wt.percent) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15-315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and gamma-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 percent in high CO2 loading, ~0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results", } @InProceedings{DBLP:conf/nafips/BaharZE16, author = "Hosein Hamisheh Bahar and Mohammad Hossein Fazel Zarandi and Akbar Esfahanipour", title = "Generating ternary stock trading signals using fuzzy genetic network programming", booktitle = "2016 Annual Conference of the North American Fuzzy Information Processing Society, {NAFIPS} 2016, El Paso, TX, USA, October 31 - November 4, 2016", pages = "1--6", publisher = "{IEEE}", year = "2016", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/NAFIPS.2016.7851630", DOI = "doi:10.1109/NAFIPS.2016.7851630", timestamp = "Wed, 16 Oct 2019 14:14:51 +0200", biburl = "https://dblp.org/rec/conf/nafips/BaharZE16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bahiraie:2009:AJAS, author = "Alireza Bahiraie and Noor {Akma bt Ibrahim} and A. K. M. Azhar", title = "On the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy Prediction", journal = "American Journal of Applied Sciences", year = "2009", volume = "6", number = "9", pages = "1748--1757", keywords = "genetic algorithms, genetic programming, ratios analysis, risk box, bankruptcy prediction", ISSN = "1546-9239", URL = "http://www.scipub.org/fulltext/ajas/ajas691748-1757.pdf", oai = "oai:doaj-articles:dbcbc387f7a40da02a20dffdfbef123f", size = "10 pages", abstract = "{\bf Problem statement: }Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. {\bf Approach:} This study presented new geometric technique for empirical analysis of bankruptcy risk using financial ratios. Within this framework, we proposed the use of a new ratio representation which named Risk Box measure (RB). We demonstrated the application of this geometric approach for variable representation, data visualization and financial ratios at different stages of corporate bankruptcy prediction models based on financial balance sheet ratios. These stages were the selection of variables (predictors), accuracy of each estimation model and the representation of each model for transformed and common ratios. {\bf Results: }We provided evidence of extent to which changes in values of this index were associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and direction of risk components. Results of Genetic Programming (GP) models were compared as different classification models and results showed the classifiers outperform by modified ratios.{\bf Conclusion/Recommendations:} In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. Genetic programming method is substantially superior to the traditional methods such as MDA or Logistic method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, GP will provide heuristic non linear regression as a tool in providing forecasting regression for studies associated with financial data. Genetic programming as one of the modern classification method out performs by the use of modified ratios. Our new method would be a general methodological guideline associated with financial data analysis.", ISSN = "15469239", bibsource = "OAI-PMH server at www.doaj.org", } @Article{Bahrami:2016:Fuel, author = "Peyman Bahrami and Pezhman Kazemi and Sedigheh Mahdavi and Hossein Ghobadi", title = "A novel approach for modeling and optimization of surfactant/polymer flooding based on Genetic Programming evolutionary algorithm", journal = "Fuel", volume = "179", pages = "289--298", year = "2016", ISSN = "0016-2361", DOI = "doi:10.1016/j.fuel.2016.03.095", URL = "http://www.sciencedirect.com/science/article/pii/S0016236116301375", abstract = "In this research, Genetic Programming (GP) as a novel method for modelling the Recovery Factor (RF) and the Net Present Value (NPV) in Surfactant-Polymer (SP) flooding is presented. The GP modelling, has the advantage that the created models did not require a fundamental description of the physical processes. The GP created mathematical functions for both outputs as a function of important parameters which involves in the SP flooding based on 202 different data. Moreover, 10-fold cross validation were employed to check the models overfitting. The Normalized Root Mean Squared Error (NRMSE) and the coefficient of determination (R2) of 4.83percent, 0.963 for the RF model, and 5.68percent, 0.946 for NPV model represented the accuracy of models. The importance and effect of variables on models were investigated, and simultaneous optimization was performed on both models to find the best results in terms of higher RF and NPV. The highest values of 55.03 and 7.3 Million US Dollars (MMUSD) for RF and NPV were achieved as a result of this optimization.", keywords = "genetic algorithms, genetic programming, RSM, Optimization, Polymer-surfactant flooding, 10-Fold cross validation", notes = "Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran Faculty of Pharmacy, Departments of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University, Krakow, Poland", } @Article{Bahreini-Toussi:2021:MCA, author = "Iman {Bahreini Toussi} and Abdolmajid Mohammadian and Reza Kianoush", title = "Prediction of Maximum Pressure at the Roofs of Rectangular Water Tanks Subjected to Harmonic Base Excitation Using the {Multi-Gene} Genetic Programming Method", journal = "Mathematical and Computational Applications", year = "2021", volume = "26", number = "1", keywords = "genetic algorithms, genetic programming", ISSN = "2297-8747", URL = "https://www.mdpi.com/2297-8747/26/1/6", DOI = "doi:10.3390/mca26010006", abstract = "Liquid storage tanks subjected to base excitation can cause large impact forces on the tank roof, which can lead to structural damage as well as economic and environmental losses. The use of artificial intelligence in solving engineering problems is becoming popular in various research fields, and the Genetic Programming (GP) method is receiving more attention in recent years as a regression tool and also as an approach for finding empirical expressions between the data. In this study, an OpenFOAM numerical model that was validated by the authors in a previous study is used to simulate various tank sizes with different liquid heights. The tanks are excited in three different orientations with harmonic sinusoidal loadings. The excitation frequencies are chosen as equal to the tanks natural frequencies so that they would be subject to a resonance condition. The maximum pressure in each case is recorded and made dimensionless; then, using Multi-Gene Genetic Programming (MGGP) methods, a relationship between the dimensionless maximum pressure and dimensionless liquid height is acquired. Finally, some error measurements are calculated, and the sensitivity and uncertainty of the proposed equation are analysed.", notes = "also known as \cite{mca26010006}", } @InProceedings{Bai:2010:ISDA, author = "Haiying Bai and Noriko Yata and Tomoharu Nagao", title = "Efficient evolutionary image processing using genetic programming: Reducing computation time for generating feature images of the Automatically Construction of Tree-Structural Image Transformation (ACTIT)", booktitle = "10th International Conference on Intelligent Systems Design and Applications (ISDA 2010)", year = "2010", month = nov # " 29-" # dec # " 1", pages = "302--307", abstract = "Using well-established techniques of Genetic Programming (GP), we automatically optimise image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved the algorithm feature filters in the process through GP, which are expressed by trees in Automatic Construction of Tree-Structural Image Transformation, to reduce computation time. Through our experimentation, we show that our new approach is accurate and requires less computation time by maintaining the feature images in conjunction with the original images.", keywords = "genetic algorithms, genetic programming, ACTIT, automatically construction of tree-structural image transformation, evolutionary image processing, image feature filters, transformation images, image processing", DOI = "doi:10.1109/ISDA.2010.5687249", notes = "Grad. Sch. of Environ. & Inf. Sci., Yokohama Nat. Univ., Yokohama, Japan. Also known as \cite{5687249}", } @InProceedings{Bai:2008:ieeeSMI, author = "Linge Bai and Manolya Eyiyurekli and David E. Breen", title = "Self-organizing primitives for automated shape composition", booktitle = "IEEE International Conference on Shape Modeling and Applications, SMI 2008", year = "2008", month = jun, pages = "147--154", keywords = "genetic algorithms, genetic programming, automated shape composition, cell behavior, chemical-field-driven aggregation, chemotaxis-driven aggregation behavior, cumulative chemical field, evolutionary computing process, fitness measure, macroscopic shape, mathematical function, morphogenic primitives, self-organizing primitive, shape formation, shape modeling, structure formation, computational geometry", DOI = "doi:10.1109/SMI.2008.4547962", abstract = "Motivated by the ability of living cells to form into specific shapes and structures, we present a new approach to shape modeling based on self-organizing primitives whose behaviors are derived via genetic programming. The key concept of our approach is that local interactions between the primitives direct them to come together into a macroscopic shape. The interactions of the primitives, called morphogenic primitives (MP), are based on the chemotaxis-driven aggregation behaviors exhibited by actual living cells. Here, cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The explicit mathematical form of the chemical field functions are derived via genetic programming (GP), an evolutionary computing process that evolves a population of functions. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and the GP-based method used to define the chemical field functions needed to produce user- specified shapes from simple aggregating primitives.", notes = "Also known as \cite{4547962}", } @InProceedings{Bai:2008:gecco, author = "Linge Bai and Manolya Eyiyurekli and David E. Breen", title = "Automated shape composition based on cell biology and distributed genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1179--1186", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1179.pdf", DOI = "doi:10.1145/1389095.1389329", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, chemotaxis, distributed genetic programming, morphogenesis, self-organisation, shape composition", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389329}", } @InProceedings{Bai:2008:SASO, author = "Linge Bai and Manolya Eyiyurekli and David E. Breen", title = "An Emergent System for Self-Aligning and Self-Organizing Shape Primitives", booktitle = "Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO '08", year = "2008", month = oct, pages = "445--454", keywords = "genetic algorithms, genetic programming, direct morphogenetic primitives, emergent behavior, emergent system, evolutionary computing, living cells, local interaction rules, natural phenomenon, self-aligning shape primitives, self-organizing shape primitives, simulation system, user-defined shape, computational geometry", DOI = "doi:10.1109/SASO.2008.54", abstract = "Motivated by the natural phenomenon of living cells self-organizing into specific shapes and structures, we present an emergent system that uses evolutionary computing methods for designing and simulating self-aligning and self-organizing shape primitives.Given the complexity of the emergent behavior, genetic programming is employed to control the evolution of our emergent system. The system has two levels of description. At the macroscopic level, a user-specified, pre-defined shape is given as input to the system. The system outputs local interaction rules that direct morphogenetic primitives (MP) to aggregate into the shape. At the microscopic level, MPs follow interaction rules based only on local interactions. All MPs are identical and do not know the final shape to be formed. The aggregate is then evaluated at the macroscopic level for its similarity to the user-defined shape. In this paper, we present (1) an emergent system that discovers local interaction rules that direct MPs to form user-defined shapes, (2) the simulation system that implements these rules and causes MPs to self-align and self-organize into a user-defined shape, and (3) the robustness and scalability qualities of the overall approach.", notes = "Also known as \cite{4663447}", } @InCollection{Bai:2012:ME, author = "Linge Bai and David E. Breen", title = "Chemotaxis-Inspired Cellular Primitives for Self-Organizing Shape Formation", booktitle = "Morphogenetic Engineering", publisher = "Springer", year = "2012", editor = "Rene Doursat and Hiroki Sayama and Olivier Michel", series = "Understanding Complex Systems", chapter = "9", pages = "209--237", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-33901-1", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.306.4523", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.4523", URL = "http://dx.doi.org/10.1007/978-3-642-33902-8_9", DOI = "doi:10.1007/978-3-642-33902-8_9", size = "29 pages", abstract = "Motivated by the ability of living cells to form specific shapes and structures, we are investigating chemotaxis-inspired cellular primitives for self-organising shape formation. This chapter details our initial effort to create Morphogenetic Primitives (MPs), software agents that may be programmed to self-organise into user specified 2D shapes. The interactions of MPs are inspired by chemotaxis-driven aggregation behaviours exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. The artificial chemical fields of individual MPs are explicitly defined as mathematical functions. Genetic programming is used to discover the chemical field functions that produce an automated shape formation capability. We describe the cell-based behaviours of MPs and a distributed genetic programming method that discovers the chemical fields needed to produce macroscopic shapes from simple aggregating primitives. Several examples of aggregating MPs demonstrate that chemotaxis is an effective paradigm for spatial self-organization algorithms.", } @PhdThesis{Bai_LingePhD, author = "Linge Bai", title = "Chemotaxis-based Spatial Self-Organization Algorithms", school = "Department of Computer Science, Drexel University", year = "2014", address = "Philadelphia, USA", month = aug, keywords = "genetic algorithms, genetic programming, Chemotaxis, Self-organizing systems", URL = "https://www.cs.drexel.edu/~david/Abstracts/bai_phd-abs.html", URL = "http://hdl.handle.net/1860/idea:6006", URL = "https://idea.library.drexel.edu/islandora/object/idea%3A6006/datastream/OBJ/download/Chemotaxis-based_spatial_self-organization_algorithms.pdf", size = "164 pages", abstract = "Self-organization is a process that increases the order of a system as a result of local interactions among low-level, simple components, without the guidance of an outside source. Spatial self-organization is a process in which shapes and structures emerge at a global level from collective movements of low level shape primitives. Spatial self-organization is a stochastic process, and the outcome of the aggregation cannot necessarily be guaranteed. Despite the inherent ambiguity, self-organizing complex systems arise everywhere in nature. Motivated by the ability of living cells to form specific shapes and structures, we develop two self-organizing systems towards the ultimate goal of directing the spatial self-organizing process. We first develop a self-sorting system composed of a mixture of cells. The system consistently produces a sorted structure. We then extend the sorting system to a general shape formation system. To do so, we introduce morphogenetic primitives (MP), defined as software agents, which enable self-organizing shape formation of user-defined structures through a chemotaxis paradigm. One challenge that arises from the shape formation process is that the process may form two or more stable final configurations. In order to direct the self-organizing process, we find a way to characterize the macroscopic configuration of the MP swarm. We demonstrate that statistical moments of the primitives locations can successfully capture the macroscopic structure of the aggregated shape. We do so by predicting the final configurations produced by our spatial self-organization system at an early stage in the process using features based on the statistical moments. At the next stage, we focus on developing a technique to control the outcome of bifurcating aggregations. We identify thresholds of the moments and generate biased initial conditions whose statistical moments meet the thresholds. By starting simulations with biased, random initial configurations, we successfully control the aggregation for a number of swarms produced by the agent-based shape formation system. This thesis demonstrates that chemotaxis can be used as a paradigm to create an agent-based spatial self-organization system. Furthermore, statistical moments of the swarm can be used to robustly predict and control the outcomes of the aggregation process.", notes = "3.5.2 Chemical Field Evolution via Genetic Programming https://www.cs.drexel.edu/~david/geom_biomed_comp.html#morph_prim Supervisor David Breen", } @Article{BAI:2024:ress, author = "Rui Bai and Khandaker Noman and Yu Yang2 and Yongbo Li and Weiguo Guo", title = "Towards trustworthy remaining useful life prediction through multi-source information fusion and a novel {LSTM-DAU} model", journal = "Reliability Engineering \& System Safety", year = "2024", volume = "245", pages = "110047", month = may, keywords = "genetic algorithms, genetic programming, Health index (HI), Trustworthy remaining useful life prediction, Multi-source fusion, LSTM, Dual attention unit", ISSN = "0951-8320", URL = "https://www.sciencedirect.com/science/article/pii/S0951832024001224", DOI = "doi:10.1016/j.ress.2024.110047", abstract = "Remaining useful life (RUL) prediction is a key part of the prognostic and health management of machines, which can effectively prevent catastrophic faults and decrease expensive unplanned maintenance. A good health indicator (HI) can ensure the accuracy and reliability of RUL prediction. However, most of the existing HI construction methods use only a single signal and rely heavily on prior knowledge, making it difficult to capture critical information about mechanical degradation, which in turn affects the performance of RUL prediction. To solve the above problems, a novel adaptive multi-source fusion method based on genetic programming is proposed for building a HI that can effectively reflect the health state of machines. Subsequently, a new LSTM model with a dual-attention mechanism is developed, which differentially handles the network input information and the recurrent information to improve the prediction performance and reduce the time complexity at the same time. Experimental validation is carried out on the real PRONOSTIA bearing dataset. The comparative results validate that the constructed fusion HI has better comprehensive performance than other fusion HIs, and the proposed prediction method is competitive with the current state-of-the-art methods", } @InProceedings{Bailey:2012:GECCO, author = "Alexander Bailey and Mario Ventresca and Beatrice Ombuki-Berman", title = "Automatic generation of graph models for complex networks by genetic programming", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "711--718", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", URL = "http://cs.adelaide.edu.au/~brad/papers/alexanderThielPeacock.pdf", DOI = "doi:10.1145/2330163.2330263", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Complex networks have attracted a large amount of research attention, especially over the past decade, due to their prevalence and importance in our daily lives. Numerous human-designed models have been proposed that aim to capture and model different network structures, for the purpose of improving our understanding the real-life phenomena and its dynamics in different situations. Groundbreaking work in genetics, medicine, epidemiology, neuroscience, telecommunications, social science and drug discovery, to name some examples, have directly resulted. Because the graph models are human made (a very time consuming process) using a small subset of example graphs, they often exhibit inaccuracies when used to model similar structures. This paper represents the first exploration into the use of genetic programming for automating the discovery and algorithm design of graph models, representing a totally new approach with great interdisciplinary application potential. We present exciting initial results that show the potential of GP to replicate existing complex network algorithms.", notes = "alexanderThielPeacock.pdf corrected version (fixed typo in background resistivity) Entered for 2013 HUMIES GECCO 2013 Also known as \cite{2330263} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @InProceedings{Bailey:2013:GECCO, author = "Alexander Bailey and Beatrice Ombuki-Berman and Mario Ventresca", title = "Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "893--900", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463498", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The pathways that relay sensory information within the brain form a network of connections, the precise organisation of which is unknown. Communities of neurons can be discerned within this tangled structure, with inhomogeneously distributed connections existing between cortical areas. Classification and modelling of these networks has led to advancements in the identification of unhealthy or injured brains, however, the current models used are known to have major deficiencies. Specifically, the community structure of the cortex is not accounted for in existing algorithms, and it is unclear how to properly design a more representative graph model. It has recently been demonstrated that genetic programming may be useful for inferring accurate graph models, although no study to date has investigated the ability to replicate community structure. In this paper we propose the first GP system for the automatic inference of algorithms capable of generating, to a high accuracy, networks with community structure. We use a common cat cortex data set to highlight the efficacy of our approach. Our experiments clearly show that the inferred graph model generates a more representative network than those currently used in scientific literature.", notes = "Also known as \cite{2463498} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Bailey:2014:ieeeTEC, author = "Alexander Bailey and Mario Ventresca and Beatrice Ombuki-Berman", title = "Genetic Programming for the Automatic Inference of Graph Models for Complex Networks", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "3", pages = "405--419", month = jun, keywords = "genetic algorithms, genetic programming, complex networks, Evolutionary Computation", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2281452", size = "15 pages", abstract = "Complex networks are becoming an integral tool for our understanding of an enormous variety of natural and artificial systems. A number of human-designed network generation procedures have been proposed that reasonably model specific real-life phenomena in structure and dynamics. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. A graph model is an algorithm capable of constructing arbitrarily sized networks, whose end structure will exhibit certain statistical and structural properties. The process of deriving an accurate graph model is very time intensive and challenging and may only yield highly accurate models for very specific phenomena. An automated approach based on Genetic Programming was recently proposed by the authors. However, this initial system suffered from a number of drawbacks, including an under-emphasis on creating hub vertices, the requirement of user intervention to determine objective weights and the arbitrary approach to selecting the most representative model from a population of candidate models. In this paper we propose solutions to these problems and show experimentally that the new system represents a significant improvement and is very capable of reproducing existing common graph models from even a single small initial network.", notes = "also known as \cite{6595618}", } @InProceedings{bain:2004:eafcs, title = "Evolving Algorithms for Constraint Satisfaction", author = "Stuart Bain and John Thornton and Abdul Sattar", pages = "265--272", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Combinatorial \& numerical optimization", URL = "http://stuart.multics.org/publications/CEC2004.pdf", URL = "https://research-repository.griffith.edu.au/bitstream/handle/10072/2138/27736.pdf", DOI = "doi:10.1109/CEC.2004.1330866", size = "8 pages", abstract = "This paper proposes a framework for automatically evolving constraint satisfaction algorithms using genetic programming. The aim is to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. A representation is introduced that is suitable for genetic programming and that can handle both complete and local search heuristics. In addition, the representation is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics. In a preliminary empirical study it is shown that the new framework is capable of evolving algorithms for solving the well-studied problem of boolean satisfiability testing.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{bain04methods, author = "Stuart Bain and John Thornton and Abdul Sattar", title = "Methods of Automatic Algorithm Generation", booktitle = "8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004", year = "2004", editor = "Chengqi Zhang and Hans W. Guesgen and Wai-Kiang Yeap", volume = "3157", series = "Lecture Notes in Computer Science", pages = "144--153", address = "Auckland, New Zealand", month = aug # " 9-13", publisher = "Springer", keywords = "genetic algorithms, genetic programming, CSP", isbn13 = "978-3-540-22817-2", URL = "http://www.ict.griffith.edu.au/~johnt/publications/PRICAI2004stuart.pdf", DOI = "doi:10.1007/978-3-540-28633-2_17", size = "10 pages", abstract = "Many methods have been proposed to automatically generate algorithms for solving constraint satisfaction problems. The aim of these methods has been to overcome the difficulties associated with matching algorithms to specific constraint satisfaction problems. This paper examines three methods of generating algorithms: a randomised search, a beam search and an evolutionary method. The evolutionary method is shown to have considerably more flexibility than existing alternatives, being able to discover entirely new heuristics and to exploit synergies between heuristics.", notes = "uf100-01.cnf", } @InProceedings{bain05evolving, author = "Stuart Bain and John Thornton and Abdul Sattar", title = "Evolving variable-ordering heuristics for constrained optimisation", booktitle = "Principles and Practice of Constraint Programming: CP'05", year = "2005", editor = "Peter {van Beek}", volume = "3709", series = "Lecture Notes in Computer Science", pages = "732--736", address = "Sitges, Spain", month = oct # " 1-5", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SAT, DIMACS", isbn13 = "978-3-540-29238-8", URL = "http://www.ict.griffith.edu.au/~johnt/publications/CP2005stuart.pdf", DOI = "doi:10.1007/11564751_54", size = "5 pages", abstract = "we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-SAT problems, we experimentally demonstrate that algorithms evolved with this method exhibit superior performance in comparison to general purpose methods.", notes = "uuf100, jnh, quasigroup, MAX-2-SAT, GSAT An extended version of this paper is available from the authors homepage at http://stuart.multics.org", } @InProceedings{bain05comparison, author = "Stuart Bain and John Thornton and Abdul Sattar", title = "A Comparison of Evolutionary Methods for the Discovery of Local Search Heuristics", booktitle = "Australian Conference on Artificial Intelligence: AI'05", year = "2005", editor = "Shichao Zhang and Ray Jarvis", volume = "3809", series = "Lecture Notes in Computer Science", pages = "1068--1074", address = "Sydney", month = dec # " 5-9", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-30462-3", URL = "http://www.ict.griffith.edu.au/~s661641/publications/AI2005stuart.pdf", DOI = "doi:10.1007/11589990_142", size = "6 pages", abstract = "Methods of adaptive constraint satisfaction have recently become of interest to overcome the limitations imposed on black-box search algorithms by the no free lunch theorems. Two methods that each use an evolutionary algorithm to adapt to particular classes of problem are the CLASS system of Fukunaga and the evolutionary constraint algorithm work of Bain et al. We directly compare these methods, demonstrating that although special purpose methods can learn excellent algorithms, on average standard evolutionary operators perform even better, and are less susceptible to the problems of bloat and redundancy.", notes = "UBCSAT, GSAT, WALKSAT and NOVELTY. uf100-01 through uf100-050 from SATLIB An appendix is available on the author homepage at http://stuart.multics.org", } @PhdThesis{Bain:thesis, author = "Stuart Iain Bain", title = "Evolving Algorithms for Over-Constrained and Satisfaction Problems", school = "School of Information and Communication Technology, Griffith University", year = "2006", address = "Brisbane, Queensland, Australia", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://stuart.freeshell.org/pubs/bain06evolving.pdf", broken_url = "https://experts.griffith.edu.au/publication/n0c3cebffccba31781b944d6c54e6049b", URL = "http://hdl.handle.net/10072/365848", DOI = "doi:10.25904/1912/1794", size = "131 pages", abstract = "The notion that a universally effective problem solver may still exist, and is simply waiting to be found, is slowly being abandoned in the light of a growing body of work reporting on the narrow applicability of individual heuristics. As the formalism of the constraint satisfaction problem remains a popular choice for the representation of problems to be solved algorithmically, there exists an ongoing need for new algorithms to efficiently handle the disparate range of problems that have been posed in this representation. Given the costs associated with manually applying human algorithm development and problem solving expertise, methods that can automatically adapt to the particular features of a specific class of problem have begun to attract more attention. Whilst a number of authors have developed adaptive systems, the field, and particularly with respect to their application to constraint satisfaction problems, has seen only limited discussion as to what features are desirable for an adaptive constraint system. This may well have been a limiting factor with previous implementations, which have exhibited only subsets of the five features identified in this work as important to the utility of an adaptive constraint satisfaction system. Whether an adaptive system exhibits these features depends on both the chosen representation and the method of adaptation. In this thesis, a three-part representation for constraint algorithms is introduced, which defines an algorithm in terms of contention, preference and selection functions. An adaptive system based on genetic programming is presented that adapts constraint algorithms described using the mentioned three-part representation. This is believed to be the first use of standard genetic programming for learning constraint algorithms. Finally, to further demonstrate the efficacy of this adaptive system, its performance in learning specialised algorithms for hard, real-world problem instances is thoroughly evaluated. These instances include random as well as structured instances from known-hard benchmark distributions, industrial problems (specifically, SAT-translated planning and cryptographic problems) as well as over-constrained problem instances. The outcome of this evaluation is a set of new algorithms, valuable in their own right, specifically tailored to these problem classes. Partial results of this work have appeared in the following publications: [1] Stuart Bain, John Thornton, and Abdul Sattar (2004) Evolving algorithms for constraint satisfaction. In Proc. of the 2004 Congress on Evolutionary Computation, pages 265-272. [2] Stuart Bain, John Thornton, and Abdul Sattar (2004) Methods of automatic algorithm generation. In Proc. of the 9th Pacific Rim Conference on AI, pages 144-153. [3] Stuart Bain, John Thornton, and Abdul Sattar. (2005) A comparison of evolutionary methods for the discovery of local search heuristics. In Australian Conference on Artificial Intelligence: AI'05, pages 1068-1074. [4] Stuart Bain, John Thornton, and Abdul Sattar (2005) Evolving variable-ordering heuristics for constrained optimisation. In Principles and Practice of Constraint Programming: CP'05, pages 732-736.", notes = "Supervisors: Abdul Sattar and John Thornton", } @Article{bains:2002:CODDD, author = "William Bains and Richard Gilbert and Lilya Sviridenko and Jose-Miguel Gascon and Robert Scoffin and Kris Birchall and Inman Harvey and John Caldwell", title = "Evolutionary computational methods to predict oral bioavailability {QSPRs}", journal = "Current Opinion in Drug Discovery and Development", year = "2002", volume = "5", number = "1", pages = "44--51", month = jan, keywords = "genetic algorithms, genetic programming", ISSN = "1367-6733", broken = "http://www.labome.org/expert/bains/w-bains-570643.html", abstract = "This review discusses evolutionary and adaptive methods for predicting oral bioavailability (OB) from chemical structure. Genetic Programming (GP), a specific form of evolutionary computing, is compared with some other advanced computational methods for OB prediction. The results show that classifying drugs into 'high' and 'low' OB classes on the basis of their structure alone is solvable, and initial models are already producing output that would be useful for pharmaceutical research. The results also suggest that quantitative prediction of OB will be tractable. Critical aspects of the solution will involve the use of techniques that can: (i) handle problems with a very large number of variables (high dimensionality); (ii) cope with 'noisy' data; and (iii) implement binary choices to sub-classify molecules with behavior that are qualitatively different. Detailed quantitative predictions will emerge from more refined models that are hybrids derived from mechanistic models of the biology of oral absorption and the power of advanced computing techniques to predict the behavior of the components of those models in silico.", notes = "PMID: 11865672 [PubMed - indexed for MEDLINE] Amedis Pharmaceuticals. Review, Tutorial http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1367-6733[ISSN] Oct 2016 Curr Opin Drug Discov Devel seems to have published from 1998 to 2010. ", } @Article{bains:2004:PBMB, author = "William Bains and Antranig Basman and Cat White", title = "{HERG} binding specificity and binding site structure: Evidence from a fragment-based evolutionary computing {SAR} study", journal = "Progress in Biophysics and Molecular Biology", year = "2004", volume = "86", pages = "205--233", number = "2", month = oct, keywords = "genetic algorithms, genetic programming, HERG, IKr, QSAR, Molecular descriptors, Prediction", DOI = "doi:10.1016/j.pbiomolbio.2003.09.001", abstract = "We describe the application of genetic programming, an evolutionary computing method, to predicting whether small molecules will block the HERG cardiac potassium channel. Models based on a molecular fragment-based descriptor set achieve an accuracy of 85-90% in predicting whether the IC50 of a 'blind' set of compounds is <1 [mu]M. Analysis of the models provides a 'meta-SAR', which predicts a pharmacophore of two hydrophobic features, one preferably aromatic and one preferably nitrogen-containing, with a protonatable nitrogen asymmetrically situated between them. Our experience of the approach suggests that it is robust, and requires limited scientist input to generate valuable predictive results and structural understanding of the target.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TBN-4BS4DJM-1/2/2bd8833742e401378469ee988d571705", size = "29 pages", notes = "Amedis, lilgp, Fixed weighted sum of ROC and Akaike fitness criterion (AIC) Many descriptors including not only description of compound but who and how measurements were made. GP run many times (1028+). http://www.elsevier.com/wps/find/journaldescription.cws_home/408/description#description Amedis Pharmaceuticals, Unit 162 Cambridge Science Park, Milton Road, Cambridge, UK p206 {"}...Fatal cardiac arrhythmias...{"} {"}HERG binds to many compounds.{"} {"}Testing for HERG...effectively mandatory...{"} p207/p221 empirically derived penalty for {"}over-complex models, in order to prevent over-fitting{"} p211 {"}GP can select efficiently from a large number of input variables{"}. p215 big difference between Training and validation ROC. Extracting chemically meaningful reasoning from evolved solutions. Relationship with possible mechanisms in HERG gap in cell wall. p226 {"}we belive it is efficient use of the machine{"} [ie computer time].", } @Article{Baird:2006:RNA, author = "Stephen D. Baird and Marcel Turcotte and Robert G. Korneluk and Martin Holcik", title = "Searching for IRES", journal = "RNA", year = "2006", volume = "12", number = "10", pages = "1755--1785", month = oct, publisher = "RNA Society", keywords = "genetic algorithms, genetic programming, IRES, RNA, secondary structure, prediction software", DOI = "doi:10.1261/rna.157806", abstract = "The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream AUGs, and %GC content of 5'-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are on the average longer, almost half of all 5'-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible.", notes = "Paragraph on \cite{Yuh-JyhHu:2003:NAR} PMCID: PMC1581980", } @InProceedings{Bajurnow:aspgp03, author = "Andrei Bajurnow and Vic Ciesielski", title = "Function and terminal Set Selection for Evolving Goal Scoring Behaviour in Soccer Players", booktitle = "Proceedings of The First Asian-Pacific Workshop on Genetic Programming", year = "2003", editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan", pages = "38--44", address = "Rydges (lakeside) Hotel, Canberra, Australia", month = "8 " # dec, keywords = "genetic algorithms, genetic programming", ISBN = "0-9751724-0-9", notes = "\cite{aspgp03}", } @InProceedings{bajurnow:2004:llfegsbisp, title = "Layered Learning for Evolving Goal Scoring Behavior in Soccer Players", author = "Andrei Bajurnow and Vic Ciesielski", pages = "1828--1835", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", URL = "http://goanna.cs.rmit.edu.au/~vc/papers/cec2004-bajurnow.pdf", DOI = "doi:10.1109/CEC.2004.1331118", size = "8 pages", keywords = "genetic algorithms, genetic programming, Evolutionary intelligent agents, Evolutionary Computation and Games", abstract = "Layered learning allows decomposition of the stages of learning in a problem domain. We apply this technique to the evolution of goal scoring behavior in soccer players and show that layered learning is able to find solutions comparable to standard genetic programs more reliably. The solutions evolved with layers have a higher accuracy but do not make as many goal attempts. We compared three variations of layered learning and find that maintaining the population between layers as the encapsulated learnt layer is introduced to be the most computationally efficient. The quality of solutions found by layered learning did not exceed those of standard genetic programming in terms of goal scoring ability.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Baker:2010:ieeeICWITS, author = "James Baker and Nuri Celik and Nobutaka Omaki and Jill Kobashigawa and Hyoung-Sun Youn and Magdy F. Iskander", title = "On the design of integrated HF radar systems for Homeland Security applications", booktitle = "2010 IEEE International Conference on Wireless Information Technology and Systems (ICWITS)", year = "2010", month = "28 " # oct # "-" # sep # " 3", abstract = "In this paper, HCAC's research and development efforts on the development of integrated and low cost HF radar for coastal surveillance and other Homeland Security applications are summarised. The proposed design incorporates electrically small antenna for rapid deployment, supports operation on floating platforms by using enhanced DSP algorithms to mitigate clutter, incorporates improved propagation modelling to more accurately select optimum frequency channels based on atmospheric conditionxs and overcome the errors due to terrain effects, uses Genetic Programming for automatic target recognition and classification, and provides for passive radar operation using existing broadcast transmitters to enable covert operation.", keywords = "genetic algorithms, genetic programming, DSP algorithm, broadcast transmitter, clutter, coastal surveillance, electrically small antenna, homeland security, integrated HF radar system, optimum frequency channel, propagation modeling, terrain effect, military radar, national security, radar antennas, radar clutter, signal processing", DOI = "doi:10.1109/ICWITS.2010.5611859", notes = "Also known as \cite{5611859}", } @InProceedings{Baker:2014:DBV:2638404.2638521, author = "Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier", title = "Detecting Bacterial Vaginosis Using Machine Learning", booktitle = "Proceedings of the 2014 ACM Southeast Regional Conference", year = "2014", pages = "46:1--46:4", address = "Kennesaw, Georgia, USA", month = mar # " 28-29", publisher = "ACM", keywords = "genetic algorithms, genetic programming", acmid = "2638521", isbn13 = "978-1-4503-2923-1", DOI = "doi:10.1145/2638404.2638521", size = "4 pages", abstract = "Bacterial Vaginosis (BV) is the most common of vaginal infections diagnosed among women during the years where they can bear children. Yet, there is very little insight as to how it occurs. There are a vast number of criteria that can be taken into consideration to determine the presence of BV. The purpose of this paper is two-fold; first to discover the most significant features necessary to diagnose the infection, second is to apply various classification algorithms on the selected features. It is observed that certain feature selection algorithms provide only a few features; however, the classification results are as good as using a large number of features.", notes = "ACM SE '14", } @InProceedings{Baker:2014:ICMLC, author = "Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier", title = "Applying machine learning techniques in detecting Bacterial Vaginosis", booktitle = "2014 International Conference on Machine Learning and Cybernetics", year = "2014", volume = "1", pages = "241--246", address = "Lanzhou, China", month = "13-16 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Bacterial Vaginosis, Machine learning, Feature selection, Classification", isbn13 = "978-1-4799-4216-9", DOI = "doi:10.1109/ICMLC.2014.7009123", size = "6 pages", abstract = "There are several diseases which arise because of changes in the microbial communities in the body. Scientists continue to conduct research in a quest to find the catalysts that provoke these changes in the naturally occurring microbiota. Bacterial Vaginosis (BY) is a disease that fits the above criteria. BV afflicts approximately 29percent of women in child bearing age. Unfortunately, its causes are unknown. This paper seeks to uncover the most important features for diagnosis and in turn employ classification algorithms on those features. In order to fulfill our purpose, we conducted two experiments on the data. We isolated the clinical and medical features from the full set of raw data, we compared the accuracy, precision, recall and F-measure and time elapsed for each feature selection and classification grouping. We noticed that classification results were as good or better after performing feature selection although there was a wide range in the number of features produced from the feature selection process. After comparing the experiments, the algorithms performed best on the medical dataset.", notes = "Weka, Java. PubMed ID: 25914861", } @PhdThesis{bakkoury:thesis, author = "Zohra Bakkoury", title = "Feasibility Assessement and Optimal Scheduling of Water Supply Projects", school = "School of Engineering and Computer Science, Exeter University", year = "2002", keywords = "genetic algorithms", } @Article{Bakshi:2012:ijetae, author = "Ankit Bakshi and Pallavi Pandit and Santosh Easo", title = "To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming", journal = "International Journal of Emerging Technology and Advanced Engineering", year = "2012", volume = "2", number = "12", pages = "319--322", month = dec, keywords = "genetic algorithms, genetic programming, elitism, double tournament, plain crossover", ISSN = "2250--2459", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.414.3468", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3468", URL = "http://www.ijetae.com/Volume2Issue12.html", URL = "http://www.ijetae.com/files/Volume2Issue12/IJETAE_1212_59.pdf", size = "4 pages", abstract = "A phenomenon for designing classifier for three or more classes (Multiclass) problem using genetic programming (GP) is multiclass classifier. In this scenario we purported three methods named Double Tournament Method, Gene-Mutation Method and a Plain Crossover method. In Double Tournament Method, we pick out two idiosyncratic for the crossover operation on the basis of size and fitness. In Gene-Mutation tactic we are propagating two child from single parent and selecting one of them on the basis of fitness and also bring into play elitism on the child so that the mutation operation does not degrade the fitness of the distinct, whereas in Plain Crossover we select the two child for the succeeding generation on the basis of size, depth and fitness along with elitism on each step from the six child which is generated during crossover. To exhibit our approach we have designed a Multiclass Classifier using GP by taking some standard datasets. The results attained show that by applying Plain crossover together with Gene-Mutation refined the performance of the classifier.", notes = "Article 59.", } @InProceedings{Bakurov:2018:evoMusArt, author = "Illya Bakurov and Brian J. Ross", title = "Non-photorealistic Rendering with Cartesian Genetic Programming Using Graphics Processing Units", booktitle = "7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018", year = "2018", editor = "Juan Romero and Antonios Liapis and Aniko Ekart", series = "LNCS", volume = "10783", publisher = "Springer", pages = "34--49", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Evolutionary art, Non-photorealistic rendering, Graphics processing units, GPU", isbn13 = "978-3-319-77582-1", DOI = "doi:10.1007/978-3-319-77583-8_3", abstract = "A non-photorealistic rendering system implemented with Cartesian genetic programming (CGP) is discussed. The system is based on Baniasadi's NPR system using tree-based GP. The CGP implementation uses a more economical representation of rendering expressions compared to the tree-based system. The system borrows their many objective fitness evaluation scheme, which uses a model of aesthetics, colour testing, and image matching. GPU acceleration of the paint stroke application results in up to 6 times faster rendering times compared to CPU-based renderings. The convergence dynamics of CGP's mu+lambda evolutionary strategy was more unstable than conventional GP runs with large populations. One possible reason may be the sensitivity of the smaller mu+lambda population to the many-objective ranking scheme, especially when objectives are in conflict with each other. This instability is arguably an advantage as an exploratory tool, especially when considering the subjectivity inherent in evolutionary art.", notes = "EvoMusArt2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evomusart.php", } @InProceedings{Bakurov:2019:evoapplications, author = "Illya Bakurov and Leonardo Vanneschi and Mauro Castelli and Maria Joao Freitas", title = "Supporting Medical Decisions for Treating Rare Diseases through Genetic Programming", booktitle = "22nd International Conference, EvoApplications 2019", year = "2019", month = "24-26 " # apr, editor = "Paul Kaufmann and Pedro A. Castillo", series = "LNCS", volume = "11454", publisher = "Springer Verlag", address = "Leipzig, Germany", pages = "187--203", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Medical decisions, Rare diseases", isbn13 = "978-3-030-16691-5", URL = "http://hdl.handle.net/10362/91519", URL = "https://run.unl.pt/bitstream/10362/91519/1/Supporting_medical_decisions_treating_rare_diseases_through_genetic_programming.pdf", DOI = "doi:10.1007/978-3-030-16692-2_13", size = "16 pages", abstract = "Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centres like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.", notes = "http://www.evostar.org/2019/cfp_evoapps.php EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019", } @InProceedings{DBLP:conf/ijcci/BakurovCFV19, author = "Illya Bakurov and Mauro Castelli and Francesco Fontanella and Leonardo Vanneschi", editor = "Juan Julian Merelo Guervos and Jonathan M. Garibaldi and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick", title = "A Regression-like Classification System for Geometric Semantic Genetic Programming", booktitle = "Proceedings of the 11th International Joint Conference on Computational Intelligence, {IJCCI} 2019, Vienna, Austria, September 17-19, 2019", pages = "40--48", publisher = "ScitePress", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0008052900400048", DOI = "doi:10.5220/0008052900400048", timestamp = "Mon, 27 Apr 2020 13:47:06 +0200", biburl = "https://dblp.org/rec/conf/ijcci/BakurovCFV19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bakurov:2021:AS, author = "Illya Bakurov and Marco Buzzelli and Mauro Castelli and Leonardo Vanneschi and Raimondo Schettini", title = "General Purpose Optimization Library ({GPOL}): A Flexible and Efficient Multi-Purpose Optimization Library in Python", journal = "Applied Sciences", year = "2021", volume = "11", number = "11", pages = "Article--number 4774", month = "1 " # jun, keywords = "genetic algorithms, genetic programming, optimization, evolutionary computation, swarm intelligence, local search, continuous optimisation, combinatorial optimization, inductive programming, supervised machine learning", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/11/11/4774", DOI = "doi:10.3390/app11114774", code_url = "https://gitlab.com/ibakurov/general-purpose-optimization-library", size = "34 pages", abstract = "Several interesting libraries for optimisation have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimisation and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).", notes = "Also known as \cite{app11114774} Nova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide,1070-312 Lisboa, Portugal", } @Article{BAKUROV:2021:SEC, author = "Illya Bakurov and Mauro Castelli and Olivier Gau and Francesco Fontanella and Leonardo Vanneschi", title = "Genetic programming for stacked generalization", journal = "Swarm and Evolutionary Computation", volume = "65", pages = "100913", year = "2021", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2021.100913", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221000742", keywords = "genetic algorithms, genetic programming, Stacking, Ensemble Learning, Stacked Generalization", abstract = "In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine the models generated by different learning algorithms, typically trained on different data subsets or with different parameters, to obtain more accurate models. Ensemble strategies range from simple voting rules to more complex and effective stacked approaches. They are based on adopting a meta-learner, i.e. a further learning algorithm, and are trained on the predictions provided by the single algorithms making up the ensemble. The paper aims at exploiting some of the most recent genetic programming advances in the context of stacked generalization. In particular, we investigate how the evolutionary demes despeciation initialization technique, ?-lexicase selection, geometric-semantic operators, and semantic stopping criterion, can be effectively used to improve GP-based systems' performance for stacked generalization (a.k.a. stacking). The experiments, performed on a broad set of synthetic and real-world regression problems, confirm the effectiveness of the proposed approach", } @Article{BAKUROV:2022:SEC, author = "I. Bakurov and M. Castelli and F. Fontanella and A. {Scotto di Freca} and L. Vanneschi", title = "A novel binary classification approach based on geometric semantic genetic programming", journal = "Swarm and Evolutionary Computation", volume = "69", pages = "101028", year = "2022", ISSN = "2210-6502", DOI = "doi:10.1016/j.swevo.2021.101028", URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001905", keywords = "genetic algorithms, genetic programming, Binary classification, Geometric semantic genetic programming", abstract = "Geometric semantic genetic programming (GSGP) is a recent variant of genetic programming. GSGP allows the landscape of any supervised regression problem to be transformed into a unimodal error surface, thus it has been applied only to this kind of problem. In a previous paper, we presented a novel variant of GSGP for binary classification problems that, taking inspiration from perceptron neural networks, uses a logistic-based activation function to constrain the output value of a GSGP tree in the interval [0,1]. This simple approach allowed us to use the standard RMSE function to evaluate the train classification error on binary classification problems and, consequently, to preserve the intrinsic properties of the geometric semantic operators. The results encouraged us to investigate this approach further. To this aim, in this paper, we present the results from 18 test problems, which we compared with those achieved by eleven well-known and widely classification schemes. We also studied how the parameter settings affect the classification performance and the use of the F-score function to deal with imbalanced data. The results confirmed the effectiveness of the proposed approach", } @InProceedings{bakurov:2022:GECCO, author = "Illya Bakurov and Marco Buzzelli and Mauro Castelli and Raimondo Schettini and Leonardo Vanneschi", title = "Genetic Programming for Structural Similarity Design at Multiple Spatial Scales", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference", year = "2022", editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto", pages = "911--919", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, multi-scale context, image quality assessment, image processing, spatially-varying kernels, structural similarity, multi-scale structural similarity index, multi-scale processing, evolutionary computation, dilated convolutions", isbn13 = "978-1-4503-9237-2", DOI = "doi:10.1145/3512290.3528783", size = "9 pages", abstract = "The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.", notes = "'We use EDDA 9, \cite{vanneschi:2017:CEC} to seed the GP population' Demes. Hoist mutation. Fitness uses Spearman correlation. TID2013, VDID2014, GPOL Python \cite{Bakurov:2021:AS} See also \cite{Bakurov:2023:GPEM} GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @PhdThesis{Bakurov:thesis, author = "Illya Olegovich Bakurov", title = "Soft computing for Ill Posed Problems in Computer Vision", school = "NOVA Information Management School (NOVA IMS), NOVA University, Lisbon", year = "2022", address = "Portugal", month = "19 " # sep, keywords = "genetic algorithms, genetic programming, ANN, Evolutionary Computation, Swarm Intelligence, Ensemble Learning, Stacking, Computer Vision, Full Reference Image Quality Assessment, Semantic Segmentation", language = "English", URL = "http://hdl.handle.net/10362/144500", URL = "https://run.unl.pt/bitstream/10362/144500/1/D0071.pdf", size = "177 pages", abstract = "Soft computing (SC) includes computational techniques that are tolerant of approximations, missing information, and uncertainty, and aim at providing effective and efficient solutions to problems which may be unsolvable, or too time-consuming to solve, with exhaustive techniques. SC has found many applications in various domains of research and industry, including computer vision (CV). This dissertation focuses on tasks of full reference image quality assessment (FR-IQA) and fast scene understanding (FSU). The former consists of assessing images visual quality in regard to some pristine reference. The latter consists of classifying each pixel of a scene assuming a rapidly changing environment like, for instance, in a self-driving car. The current state-of-the-art (SOTA) in both FR-IQA and FSU rely upon convolutional neural networks (CNNs), which can be seen as a computational metaphor of the human visual cortex. Although CNNs achieved unprecedented results in many CV tasks, they also present several drawbacks: massive amounts of data and processing resources for training; the difficulty of outputs interpretation; reduced usability for compact battery-powered devices... This dissertation addresses FR-IQA and FSU using SC techniques other than CNNs. Initially, we created a flexible and efficient library to support our endeavors; it is publicly available and implements a wide range of metaheuristics to solve different problems. Then, we used swarm and evolutionary computation to optimize the parameters of several traditional FR-IQA measures (FR-IQAMs) that integrate the so called structural similarity paradigm; the novel parameters improve measures' precision without affecting their complexity. Afterward, we applied genetic programming (GP) to automatically formulate novel FR-IQAMs that are simultaneously simple, accurate, and interpretable. Lastly, we used GP as a meta-model for stacking efficient CNNs for FSU; the approach allowed us to obtain simple and interpretable models that did not exceed processing preconditions for real-time applications while achieving high levels of precision.", notes = "In English Supervisors: Mauro Castelli, Leonardo Vanneschi", } @Article{DBLP:journals/tip/BakurovBSCV23, author = "Illya Bakurov and Marco Buzzelli and Raimondo Schettini and Mauro Castelli and Leonardo Vanneschi", title = "Full-Reference Image Quality Expression via Genetic Programming", journal = "{IEEE} Trans. Image Process.", volume = "32", pages = "1458--1473", year = "2023", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1109/TIP.2023.3244662", DOI = "doi:10.1109/TIP.2023.3244662", timestamp = "Sat, 11 Mar 2023 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/tip/BakurovBSCV23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.", } @Article{Bakurov:2023:GPEM, author = "Illya Bakurov and Marco Buzzelli and Raimondo Schettini and Mauro Castelli and Leonardo Vanneschi", title = "Semantic segmentation network stacking with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 15", month = dec, note = "Special Issue on Highlights of Genetic Programming 2022 Events", keywords = "genetic algorithms, genetic programming, Stacking, Semantic segmentation, Ensemble learning, Deep learning, ANN", ISSN = "1389-2576", URL = "https://rdcu.be/drZeF", DOI = "doi:10.1007/s10710-023-09464-0", size = "37 pages", } @Article{Bakurov:2024:GPEM, author = "Illya Bakurov and Jose Manuel {Munoz Contreras} and Mauro Castelli and Nuno Rodrigues and Sara Silva and Leonardo Trujillo and Leonardo Vanneschi", title = "Geometric semantic genetic programming with normalized and standardized random programs", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 6", note = "Online first", keywords = "genetic algorithms, genetic programming, Geometric semantic mutation, Internal covariate shift, Sigmoid distribution bias, Model simplification", ISSN = "1389-2576", URL = "https://rdcu.be/dysci", DOI = "doi:10.1007/s10710-024-09479-1", size = "29 pages", abstract = "Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the last decade. The results achieved by incorporating semantic awareness in the evolutionary process demonstrate the impact that geometric semantic operators have brought to the field of EC. An improvement to the geometric semantic mutation (GSM) operator is proposed, inspired by the results achieved by batch normalization in deep learning. While, in one of its most used versions, GSM relies on the use of the sigmoid function to constrain the semantics of two random programs responsible for perturbing the parent semantics, here a different approach is followed, which allows reducing the size of the resulting programs and overcoming the issues associated with the use of the sigmoid function, as commonly done in deep learning. The idea is to consider a single random program and use it to perturb the parent's semantics only after standardization or normalization. The experimental results demonstrate the suitability of the proposed approach: despite its simplicity, the presented GSM variants outperform standard GSGP on the studied benchmarks, with a difference in terms of performance that is statistically significant. Furthermore, the individuals generated by the new GSM variants are easier to simplify, allowing us to create accurate but significantly smaller solutions.", } @InProceedings{Bakurov:2024:GPTP, author = "Illya Bakurov and Nathan Haut and Wolfgang Banzhaf", title = "Sharpness-Aware Minimization in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "151--175", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SAM", isbn13 = "978-981-96-0076-2", URL = "https://arxiv.org/abs/2405.10267", DOI = "doi:10.1007/978-981-96-0077-9_8", size = "22 pages", abstract = "Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves as a measure of the nonlinear behaviour of a solution and does so by finding solutions that lie in neighborhoods having uniformly similar loss values across all fitness cases. we adapt SAM for tree Genetic Programming (TGP) by exploring the semantic neighborhoods of solutions using two simple approaches. By capitalising upon perturbing input and output of program trees, sharpness can be estimated and used as a second optimization criterion during the evolution. To better understand the impact of this variant of SAM on TGP, we collect numerous indicators of the evolutionary process, including generalization ability, complexity, diversity, and a recently proposed genotype-phenotype mapping to study the amount of redundancy in trees. The experimental results demonstrate that using any of the two proposed SAM adaptations in TGP allows (i) a significant reduction of tree sizes in the population and (ii) a decrease in redundancy of the trees. When assessed on real-world benchmarks, the generalization ability of the elite solutions does not deteriorate.", notes = "Published in 2025 after the workshop", } @InProceedings{balakrishnan:1996:ser, author = "Karthik Balakrishnan and Vasant Honavar", title = "On Sensor Evolution in Robotics", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "455--460", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap75.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{Balakrishnan:1997:slrl, author = "Karthik Balakrishnan and Vasant Honavar", title = "Spatial Learning for Robot Localization", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Artifical life and evolutionary robotics", pages = "389--397", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @Article{Balandina:2017:PCS, author = "G. I. Balandina", title = "Control System Synthesis by Means of Cartesian Genetic Programming", journal = "Procedia Computer Science", volume = "103", pages = "176--182", year = "2017", note = "\{XII\} International Symposium Intelligent Systems 2016, \{INTELS\} 2016, 5-7 October 2016, Moscow, Russia", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Optimal control synthesis, nonlinear control systems", ISSN = "1877-0509", DOI = "doi:10.1016/j.procs.2017.01.051", URL = "http://www.sciencedirect.com/science/article/pii/S1877050917300522", abstract = "Cartesian Genetic Programming (CGP) is a type of Genetic Programming based on a program in a form of a directed graph. It also belongs to the methods of Symbolic Regression allowing to receive the optimal mathematical expression for a problem. Nowadays it becomes possible to use computers very effectively for symbolic regression calculations. CGP was developed by Julian Miller in 1999-2000. It represents a program for decoding a genotype (string of integers) into the phenotype (graph). The nodes of that graph contain references to functions from a function table, which could contain arithmetic, logical operations and/or user-defined functions. The inputs of those functions are connected to the node inputs, which itself could be connected to a node output or a graph input. As a result, it's possible to construct several mathematical expressions for the outputs and calculate them for the given inputs. This CGP implementation use point mutation to form new mathematical expressions. Steady-state genetic algorithm is chosen as a search engine. Solution solving the control system synthesis problem is presented in a form of the Pareto set, which contains a set of satisfactory control functions. Nonlinear Duffing oscillator is taken as a dynamic object.", } @Article{Balasubramaniam:2009:GPEM, author = "P. Balasubramaniam and A. Vincent Antony Kumar", title = "Solution of matrix {Riccati} differential equation for nonlinear singular system using genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "1", pages = "71--89", month = mar, keywords = "genetic algorithms, genetic programming, Matrix Riccati differential equation, Nonlinear, Optimal control, Runge Kutta method, Singular system", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-008-9072-z", size = "19 pages", abstract = "In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear singular systems using genetic programming (GP). The goal is to provide optimal control with reduced calculation effort by comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta (RK) method to those obtained from the GP method. We show that the GP approach to the problem is qualitatively better in terms of accuracy. Numerical examples are provided to illustrate the proposed method.", } @InProceedings{Balazs:2010:ieee-fuzz, author = "Krisztian Balazs and Janos Botzheim and Laszlo T. Koczy", title = "Hierarchical fuzzy system modeling by Genetic and Bacterial Programming approaches", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, pages = "1866--1871", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6920-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_ieee-fuzz.pdf", DOI = "doi:10.1109/FUZZY.2010.5584220", size = "6 pages", abstract = "In this paper a method is proposed for constructing hierarchical fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resultant hierarchical rule base is the knowledge base, which is constructed by using structure constructing evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical fuzzy rule bases is a way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.", notes = "WCCI 2010. Also known as \cite{5584220}", } @InProceedings{Balazs:2010:WAC, author = "Krisztian Balazs and Janos Botzheim and Laszlo T. Koczy", title = "Hierarchical fuzzy system construction applying genetic and bacterial programming algorithms with expression tree building restrictions", booktitle = "World Automation Congress (WAC 2010)", year = "2010", month = "19-23 " # sep, address = "Kobe, Japan", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_WAC.pdf", URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5665326", abstract = "In this paper various restrictions are proposed in the construction of hierarchical fuzzy rule bases by using Genetic and Bacterial Programming algorithms in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The properties (learning speed, accuracy) of the established systems are observed based on simulation results and they are compared to each other.", keywords = "genetic algorithms, genetic programming, bacterial programming algorithm, black box system, genetic programming algorithm, hierarchical fuzzy rule system construction, input-output pairs, supervised machine learning problem, tree building restriction, fuzzy set theory, learning (artificial intelligence)", ISSN = "2154-4824", notes = "Also known as \cite{5665326}", } @InProceedings{Balazs:2011:ieeeFUZZ, author = "Krisztian Balazs and Laszlo T. Koczy", title = "Hierarchical-interpolative fuzzy system construction by Genetic and Bacterial Programming Algorithms", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ 2011)", year = "2011", month = "27-30 " # jun, pages = "2116--2122", address = "Taipei, Taiwan", size = "7 pages", abstract = "In this paper a method is proposed for constructing hierarchical-interpolative fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resulting hierarchical rule base is the knowledge base, which is constructed by using evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical-interpolative fuzzy rule bases is an advanced way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.", keywords = "genetic algorithms, genetic programming, bacterial programming, black box system, evolutionary technique, hierarchical-interpolative fuzzy rule bases construction, knowledge base, supervised machine learning problem, fuzzy logic, knowledge based systems, learning (artificial intelligence), mathematical programming", DOI = "doi:10.1109/FUZZY.2011.6007594", ISSN = "1098-7584", notes = "Also known as \cite{6007594}", } @InProceedings{balazs:1999:AE, author = "Marton E. Balazs and Daniel L. Richter", title = "A genetic algorithm with dynamic population: Experimental results", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "25--30", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{conf/egice/BaldockS06, title = "Structural Topology Optimization of Braced Steel Frameworks Using Genetic Programming", author = "Robert Baldock and Kristina Shea", booktitle = "Intelligent Computing in Engineering and Architecture, 13th {EG}-{ICE} Workshop", publisher = "Springer", year = "2006", volume = "4200", editor = "Ian F. C. Smith", ISBN = "3-540-46246-5", pages = "54--61", series = "Lecture Notes in Computer Science", address = "Ascona, Switzerland", month = jun # " 25-30", note = "Revised Selected Papers", bibdate = "2006-12-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/egice/egice2006.html#BaldockS06", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/11888598", abstract = "This paper presents a genetic programming method for the topological optimisation of bracing systems for steel frameworks. The method aims to create novel, but practical, optimally-directed design solutions, the derivation of which can be readily understood. Designs are represented as trees with one-bay, one-story cellular bracing units, operated on by design modification functions. Genetic operators (reproduction, crossover, mutation) are applied to trees in the development of subsequent populations. The bracing design for a three-bay, 12-story steel framework provides a preliminary test problem, giving promising initial results that reduce the structural mass of the bracing in comparison to previous published benchmarks for a displacement constraint based on design codes. Further method development and investigations are discussed.", notes = "(1) Engineering Design Centre, University of Cambridge, Cambridge, CB2 1PZ, UK (2) Product Development, Technical University of Munich, Boltzmannstrasse 15, D-85748 Garching, Germany", } @InProceedings{Baldominos:2016:GECCOcomp, author = "Alejandro Baldominos and Carmen {del Barrio} and Yago Saez", title = "Exploring the Application of Hybrid Evolutionary Computation Techniques to Physical Activity Recognition", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "1377--1384", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931732", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "his paper focuses on the problem of physical activity recognition, i.e., the development of a system which is able to learn patterns from data in order to be able to detect which physical activity (e.g. running, walking, ascending stairs, etc.) a certain user is performing. While this field is broadly explored in the literature, there are few works that face the problem with evolutionary computation techniques. In this case, we propose a hybrid system which combines particle swarm optimization for clustering features and genetic programming combined with evolutionary strategies for evolving a population of classifiers, shaped in the form of decision trees. This system would run the segmentation, feature extraction and classification stages of the activity recognition chain. For this paper, we have used the PAMAP2 dataset with a basic preprocessing. This dataset is publicly available at UCI ML repository. Then, we have evaluated the proposed system using three different modes: a user-independent, a user-specific and a combined one. The results in terms of classification accuracy were poor for the first and the last mode, but it performed significantly well for the user-specific case. This paper aims to describe work in progress, to share early results an discuss them. There are many things that could be improved in this proposed system, but overall results were interesting especially because no manual data transformation took place.", notes = "Distributed at GECCO-2016.", } @Article{Baldominos:2018:Sensors, author = "Alejandro Baldominos and Yago Saez and Pedro Isasi", title = "Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments", journal = "Sensors", year = "2018", volume = "18", number = "4", month = apr, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, ANN", ISSN = "1424-8220", URL = "http://www.mdpi.com/1424-8220/18/4/1288", DOI = "doi:10.3390/s18041288", size = "24 pages", abstract = "Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.", article-number = "1288", notes = "Also known as \cite{s18041288} PMCID: PMC5948523 PMID: 29690587", } @InProceedings{Baldwin:1999:SIF, author = "James F. Baldwin and Trevor P. Martin and James G. Shanahan", title = "System Identification of Fuzzy Cartesian Granules Feature Models Using Genetic Programming", booktitle = "Fuzzy Logic in Artificial Intelligence, IJCAI'97 Workshop, Selected and Invited Papers", year = "1997", editor = "Anca L. Ralescu and James G. Shanahan", volume = "1566", series = "Lecture Notes in Artificial Intelligence", pages = "91--116", address = "Nagoya, Japan", month = aug # " 23-24", publisher = "Springer", note = "Published 1999", keywords = "genetic algorithms, genetic programming, artificial intelligence, fuzzy logic, IJCAI", CODEN = "LNCSD9", ISBN = "3-540-66374-6", ISSN = "0302-9743", bibdate = "Tue Sep 14 06:09:05 MDT 1999", acknowledgement = ack-nhfb, URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-164-22-1637718-0,00.html", notes = "DBLP, http://dblp.uni-trier.de DBLP:conf/ijcai/1997fl", } @Article{Baldwin:1999:IJAR, author = "James F. Baldwin and Trevor P. Martin and James G. Shanahan", title = "Controlling with words using automatically identified fuzzy Cartesian granule feature models", journal = "International Journal of Approximate Reasoning", volume = "22", pages = "109--148", year = "1999", number = "1-2", keywords = "genetic algorithms, genetic programming", URL = "http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e", abstract = "We present a new approach to representing and acquiring controllers based upon Cartesian granule features - multidimensional features formed over the cross product of words drawn from the linguistic partitions of the constituent input features - incorporated into additive models. Controllers expressed in terms of Cartesian granule features enable the paradigm {"}controlling with words{"} by translating process data into words that are subsequently used to interrogate a rule base, which ultimately results in a control action. The system identification of good, parsimonious additive Cartesian granule feature models is an exponential search problem. In this paper we present the G_DACG constructive induction algorithm as a means of automatically identifying additive Cartesian granule feature models from example data. G_DACG combines the powerful optimisation capabilities of genetic programming with a novel and cheap fitness function, which relies on the semantic separation of concepts expressed in terms of Cartesian granule fuzzy sets, in identifying these additive models. We illustrate the approach on a variety of problems including the modelling of a dynamical process and a chemical plant controller.", } @Book{balic:book, author = "Joze Balic", title = "Flexible Manufacturing Systems; Development - Structure - Operation - Handling - Tooling", publisher = "DAAAM International", year = "1999", series = "Manufacturing technology", series_editor = "B. Katalinic", address = "Vienna", email = "joze.balic@uni-mb.si", keywords = "genetic algorithms, genetic programming", ISBN = "3-901509-03-8", broken = "http://www.daaam.com/daaam/IMS%20Seite/DAAAMInternationalBooks.html", URL = "http://www.amazon.com/Contribution-integrated-manufacturing-Publishing-Manufacturing/dp/3901509038/ref=sr_1_1?ie=UTF8&s=books&qid=1254069037&sr=1-1", notes = "Chapter 9.4 Using genetic algorithms for modelling of manufacturing processes. Described are: - overview of GP - modelling of forming process by GP - GA approach for optimisation of cutting conditions copies can be obtained from publication@daaam.com", size = "223 pages", } @Misc{oai:CiteSeerPSU:316448, title = "Modeling Of Mechanical Parts Compositions Using Genetic Programming", author = "Joze Balic and Miran Brezocnik and Franci Cus", abstract = "The paper is a contribution to introducing biologically oriented ideas in conceiving a new and innovative idea in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the micro and macro environment. Simulation of self-organizing (genetic) composition of elementary (basic) mechanical parts into the product is presented as an example of the intelligent system. The genetic programming method is used. For conceiving the genetically based composition of parts, the parallels from the living systems are used. Composition takes place on the basis of the genetic content in the basic components and the influence of the environment. The genetically based composition takes place in a distributed way, non-deterministically, buttom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program Intelligent Manufacturing Systems which is one of the biggest projects ever introduced.", citeseer-isreferencedby = "oai:CiteSeerPSU:116960; oai:CiteSeerPSU:38303; oai:CiteSeerPSU:462740", citeseer-references = "oai:CiteSeerPSU:212034", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:316448", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/316448.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13061/http:zSzzSzwww.faim2000.isr.umd.eduzSzfaimzSzexportzSz27e8am-b.pdf/modeling-of-mechanical-parts.pdf", keywords = "genetic algorithms, genetic programming", size = "9 pages", year = "2000", notes = "not verified University of Maribor, Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems, SI-2000 Maribor, Slovenia; University of Maribor, Faculty of Mechanical Engineering, Laboratory for Researches in Cutting, SI-2000 Maribor, Slovenia", } @Article{Balic:2002:EAAI, author = "J. Balic and M. Nastran", title = "An on-line predictive system for steel wire straightening using genetic programming", journal = "Engineering Applications of Artificial Intelligence", year = "2002", volume = "15", pages = "559--565", number = "6", month = dec, keywords = "genetic algorithms, genetic programming, Production, Control, Prediction, Accuracy", ISSN = "0952-1976", owner = "wlangdon", URL = "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=43335&lang=slv", broken = "http://www.sciencedirect.com/science/article/B6V2M-48BKR53-2/2/4a53f22927ad32b0580540322d7c8868", DOI = "doi:10.1016/S0952-1976(03)00021-6", size = "7 pages", abstract = "Dimensional stability of forming processes is becoming more and more important in the modern production world. Especially when mass production is concerned, the technological system has to be reliable and accurate. Growing market demands are forcing production engineers towards process optimisation in order to achieve high machinery efficiency and reduce the production costs. An important precondition for improving the process chain is the prediction of process behaviour in advance. The paper is presenting the use of genetic programming to predict the wire geometry after forming. The results can be used as the basis for later optimisation of forming processes.", notes = "Miha Nastran and Joze Balic)", } @Article{Balic20021171, author = "Joze Balic and Marjan Korosec", title = "Intelligent tool path generation for milling of free surfaces using neural networks", journal = "International Journal of Machine Tools and Manufacture", volume = "42", number = "10", pages = "1171--1179", year = "2002", email = "joze.balic@uni-mb.si", keywords = "Neural network, CAD/CAM system, CAPP, ICAM, Milling strategy", ISSN = "0890-6955", DOI = "doi:10.1016/S0890-6955(02)00045-7", URL = "http://www.sciencedirect.com/science/article/B6V4B-45YG41B-6/2/09eff48a04f9b22be6b2ed2dd0e6d3b1", abstract = "The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented, and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness, Ra, is also being done, and compared with the NN predicted results [COBISS.SI-ID 7318550]", notes = "Not on GP", } @Article{Balic:2006:JIM, author = "Joze Balic and Miha Kovacic and Bostjan Vaupotic", title = "Intelligent Programming of {CNC} Turning Operations using Genetic Algorithm", journal = "Journal of intelligent manufacturing", year = "2006", volume = "17", number = "3", pages = "331--340", month = jun, keywords = "genetic algorithms, genetic programming, CNC programming, GA, Intelligent CAM, Turning, Tool path generation", ISSN = "0956-5515", DOI = "doi:10.1007/s10845-005-0001-1", abstract = "CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16percent. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced.", } @Article{Balicki:2006:IJCSNS, author = "Jerzy Balicki", title = "Multicriterion Genetic Programming for Trajectory Planning of Underwater Vehicle", journal = "IJCSNS International Journal of Computer Science and Network Security", year = "2006", volume = "6", number = "12", month = dec, keywords = "genetic algorithms, genetic programming, remote operating vehicle, multi-criterion optimisation", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.385.5889", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.5889", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.5889.pdf", URL = "http://digilib.unsri.ac.id/download/200612A01.pdf", size = "6 pages", abstract = "An autonomous underwater vehicle is supposed to find its trajectory, systematically. It can be obtained by using genetic programming for multi-criterion optimisation of the set of alternative paths. For assessment of an underwater vehicle trajectory, three crucial criteria can be used: a total length of a path, a smoothness of a trajectory, and a measure of safety.", } @InProceedings{conf/icsoft/Balicki07, author = "Jerzy Marian Balicki", title = "Multi-Criterion Genetic Programming With Negative Selection for Finding {Pareto} Solutions", booktitle = "Proceedings of the Second International Conference on Software and Data Technologies, ICSOFT 2007", year = "2007", editor = "Joaquim Filipe and Boris Shishkov and Markus Helfert", pages = "120--127", address = "Barcelona, Spain", month = "22-25 " # jul, publisher = "INSTICC Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-8111-05-0", URL = "http://www.icsoft.org/Abstracts/2007/ICSOFT_2007_Abstracts.htm", DOI = "doi:10.5220/0001336201200127", abstract = "Multi-criterion genetic programming (MGP) is a relatively new approach for a decision making aid and it can be applied to determine the Pareto solutions. This purpose can be obtained by formulation of a multi-criterion optimization problem that can be solved by genetic programming. An improved negative selection procedure to handle constraints in the MGP has been proposed. In the test instance, both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of the distributed system is maximized.", notes = "http://www.icsoft.org/ICSOFT2007/Area3.htm Distributed and Parallel Systems ICSOFT 2007 was held in conjunction with ENASE 2007 Others www.lania.mx/~ccoello/EMOObib.html ?", bibdate = "2009-02-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icsoft/icsoft2007-1.html#Balicki07", } @InProceedings{Balicki:2013:HSI, author = "Jerzy Balicki and Waldemar Korlub and Henryk Krawczyk and Jacek Paluszak", title = "Genetic Programming with Negative Selection for Volunteer Computing System Optimization", booktitle = "The 6th International Conference on Human System Interaction (HSI 2013)", year = "2013", month = "6-8 " # jun, pages = "271--278", keywords = "genetic algorithms, genetic programming, volenteer grid systems, Internet", DOI = "doi:10.1109/HSI.2013.6577835", ISSN = "2158-2246", size = "8 pages", abstract = "Volunteer computing systems like BOINC or Comcute are strongly supported by a great number of volunteers who contribute resources of their computers via the Web. So, the high efficiency of such grid system is required, and that is why we have formulated a multi-criterion optimisation problem for a volunteer grid system design. In that dilemma, both the cost of the host system and workload of a bottleneck host are minimised. On the other hand, a reliability of this grid structure is maximised. Moreover, genetic programming has been applied to determine the Pareto solutions. Finally, a negative selection procedure to handle constraints has been discussed.", notes = "Also known as \cite{6577835}", } @InCollection{series/sci/BalickiKKP14, author = "Jerzy Balicki and Waldemar Korlub and Henryk Krawczyk and Jacek Paluszak", title = "Genetic Programming for Interaction Efficient Supporting in Volunteer Computing Systems", booktitle = "Issues and Challenges in Artificial Intelligence", year = "2014", publisher = "Springer", editor = "Zdzislaw S. Hippe and Juliusz L. Kulikowski and Teresa Mroczek and Jerzy Wtorek", volume = "559", series = "Studies in Computational Intelligence", pages = "129--139", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-06883-1", bibdate = "2015-07-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci559.html#BalickiKKP14", DOI = "doi:10.1007/978-3-319-06883-1_11", URL = "http://dx.doi.org/10.1007/978-3-319-06883-1_11", URL = "http://dx.doi.org/10.1007/978-3-319-06883-1", size = "11 pages", abstract = "Volunteer computing systems provide a middleware for interaction between project owners and great number volunteers. In this chapter, a genetic programming paradigm has been proposed to a multi-objective scheduler design for efficient using some resources of volunteer computers via the web. In a studied problem, genetic scheduler can optimise both a workload of a bottleneck computer and cost of system. Genetic programming has been applied for finding the Pareto solutions by applying an immunological procedure. Finally, some numerical experiment outcomes have been discussed.", } @InProceedings{conf/icaisc/BalickiKSZ14, author = "Jerzy Balicki and Waldemar Korlub and Julian Szymanski and Marcin Zakidalski", title = "Big Data Paradigm Developed in Volunteer Grid System with Genetic Programming Scheduler", bibdate = "2014-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2014-1.html#BalickiKSZ14", booktitle = "Artificial Intelligence and Soft Computing - 13th International Conference, {ICAISC} 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part {I}", publisher = "Springer", year = "2014", volume = "8467", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-07172-5", pages = "771--782", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-07173-2", } @InProceedings{Balicki:2015:HSI, author = "Jerzy Balicki and Michal Beringer and Waldemar Korlub and Piotr Przybylek and Maciej Tyszka and Marcin Zadroga", booktitle = "8th International Conference on Human System Interaction (HSI)", title = "Collective citizens' behavior modelling with support of the Internet of Things and Big Data", year = "2015", pages = "61--67", abstract = "In this paper, collective human behaviours are modelled by a development of Big Data mining related to the Internet of Things. Some studies under MapReduce architectures have been carried out to improve an efficiency of Big Data mining. Intelligent agents in data mining have been analysed for smart city systems, as well as data mining has been described by genetic programming. Furthermore, artificial neural networks have been discussed in data mining as well as an analysis of Tweeter's blogs for citizens has been proposed. Finally, some numerical experiments with fire spread around Tricity, Poland have been submitted.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/HSI.2015.7170644", ISSN = "2158-2246", month = jun, notes = "Also known as \cite{7170644}", } @Article{BALLANDIES:2021:MM, author = "Mark C. Ballandies and Evangelos Pournaras", title = "Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs", journal = "Microprocessors and Microsystems", volume = "87", pages = "104335", year = "2021", ISSN = "0141-9331", DOI = "doi:10.1016/j.micpro.2021.104335", URL = "https://www.sciencedirect.com/science/article/pii/S0141933121004944", keywords = "genetic algorithms, genetic programming, Knowledge graph, Ontology, Cyber-physical-social system, Link prediction, Crowdsourcing", abstract = "Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not validated by users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder for mobile devices that brings together automation, experts' and crowdsourced citizens' knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed methodology of knowledge graph building outperforms a baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowdsource and operate pervasive reasoning systems for cyber-physical social systems in Smart Cities", } @InProceedings{Baltes:2020:GI9, author = "Sebastian Baltes and Markus Wagner", title = "An Annotated Dataset of Stack Overflow Post Edits", booktitle = "9th edition of GI @ GECCO 2020", year = "2020", month = jul # " 8-12", editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Internet", pages = "1923--1925", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Software documentation, software evolution, patches, mining soft-ware repositories, stack overflow, SOTorrent", isbn13 = "978-1-4503-7127-8", URL = "https://dl.acm.org/doi/abs/10.1145/3377929.3398108", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp144s2-file1.pdf", URL = "https://arxiv.org/abs/2004.08193", DOI = "doi:10.1145/3377929.3398108", code_url = "https://doi.org/10.5281/zenodo.3754159", size = "3 pages", abstract = "To improve software engineering, software repositories have been mined for code snippets and bug fixes. Typically, this mining takes place at the level of files or commits. To be able to dig deeper and to extract insights at a higher resolution, we hereby present an annotated dataset that contains over 7 million edits of code and text on Stack Overflow. Our preliminary study indicates that these edits might be a treasure trove for mining information about fine-grained patches, e.g., for the optimisation of non-functional properties.", notes = "Dataset https://doi.org/10.5281/zenodo.3754159 Broken Nov 2024 https://gi-gecco-20.gi-workshops.org/ Nov 2020 GECCO 2020 companion DOI not working also known as \cite{Baltes:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398108} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Baltes:2023:GI, author = "Sebastian Baltes", title = "All about the money: Cost modeling and optimization of cloud applications", booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023", year = "2023", editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke", pages = "x", address = "Melbourne, Australia", month = "20 " # may, publisher = "IEEE", note = "Invited Keynote", keywords = "genetic algorithms, genetic programming, Genetic Improvement, cloud computing, IaC, IfC, Gartner, AWS, nonfunctional cost optimisation, software engineering, SBSE", isbn13 = "979-8-3503-1232-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2023/keynote_2023_gi.pdf", DOI = "doi:10.1109/GI59320.2023.00008", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Sebastian_Baltes_Keynote_Cloud_Cost.pdf", slides_url = "https://empirical-software.engineering/talks/#2023", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/GI_ICSE2023_Keynote_Sebastian_Baltes.mp4", video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/GI_ICSE2023_Keynote_Sebastian_Baltes.mov", video_url = "https://www.youtube.com/watch?v=M8gH0qPcJHQ&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=5", size = "1 page", abstract = "Cost is an essential non-functional property of cloud applications and is often a primary reason for companies to move to the cloud. One significant advantage of cloud platforms is the possibility to scale compute, storage, and networking resources up and down based on demand. However, as an application scales, so does the cost. Cost transparency of cloud applications is a common problem, and cloud providers have responded by providing means for detecting cost anomalies. However, detecting anomalies after billing is a workaround rather than a solution addressing the core problem. After introducing central cloud computing concepts and typical pricing approaches in the cloud, this talk outlines our vision of a vendor-agnostic cost model enabling reasoning about cost-optimal infrastructure and platform configurations based on expected workloads. The overall goal is to shift cost transparency left, i.e., to the developers and platform engineers who frequently provision cloud environments using web portals or Infrastructure-as-Code (IaC) files. The talk concludes by summarising the current trend towards Infrastructure-from-Code (IfC), where programming languages and cloud infrastructure descriptions converge into one paradigm, intending to automate infrastructure provisioning as much as possible. This area has huge potential for genetic improvement to optimize the IfC code and the provisioning mechanisms while balancing nonfunctional properties such as performance and cost.", notes = "GI @ ICSE 2023, part of \cite{Nowack:2023:GI} Amazon web services: you have to pay for that, internal cloud networking costs. SAP. duckbillgroup.com, stackoverflow monolithic application, blog post. IaaS Infrastructure as a Service: compute, storage and networking. PaaS Platform as a Service. SaaS Software as a Service (just applications that are hosted in the cloud). AWS lambda: pay only when used. Use lock-in, that is how they make money. Google Cloud: configure by web user interface. Terraform: file based configuration (ideal for GI to optimise?) GitOps: ArgoCD push IaC file, automatically updates your cloud configuration Mitigation (guard rails, prevent doing something really stupid) Infracost. AWS power tuning (optimisation). OpenCost (eg Microsoft). Infrastructure-from-Code IfC api.cost. puppet. Serverless, eg JSON. Evolution of workload. SAP database. (so far) little work in software engineering research. Some in cloud research computing, eg Google cloud v. Apache Finc (IC2E 2022) Future: mining GitHub. Takeaways 41:00 shifted left, always about tradeoffs 43:00 Q&A. Myra B Cohen. Need to simplify to start research. Light weight search. Bradley Alexander, spot instances. Handle interruptions Amazon EC2. Easier to model spot prices? Hyper-scalars Very complex. You pay for everything. Erik Frederics, offline testing, test out before actually run on the cloud. Serverless. Estimate cost before actually running. How frequently do prices. Scrape prices from the internet www web. Monolithic. SAP. Importance of stable compute platform. micro-services not a solution in themselves. Latency, no easy solutions.", } @Article{baluja:1994:taaecgi, author = "Shumeet Baluja and Dean Pomerleau and Todd Jochem", title = "Towards Automated Artificial Evolution for Computer-generated Images", journal = "Connection Science", year = "1994", volume = "6", number = "2 and 3", pages = "325--354", keywords = "genetic algorithms, genetic programming, artificial neural networks (ANN), simulated evolution, computer graphics", URL = "http://www.ri.cmu.edu/pubs/pub_1718.html", URL = "http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf", size = "30 pages", abstract = " In 1991, Karl Sims presented work on artificial evolution in which he used genetic algorithms to evolve complex structures for use in computer generated images and animations. The evolution of the computer generated images progressed from simple, randomly generated shapes to interesting images which the users interactively created. The evolution advanced under the constant guidance and supervision of the user. This paper describes attempts to automate the process of image evolution through the use of artificial neural networks. The central objective of this study is to learn the user's preferences, and to apply this knowledge to evolve aesthetically pleasing images which are similar to those evolved through interactive sessions with the user. This paper presents a detailed analysis of both the shortcomings and successes encountered in the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed.", notes = "also CMU techical report CMU//CS-93-198 ", } @Article{Balzer:1985:ieeeTSE, author = "Robert Balzer", title = "A 15 Year Perspective on Automatic Programming", journal = "IEEE Transactions on Software Engineering", year = "1985", volume = "SE-11", number = "11", pages = "1257--1268", month = nov, keywords = "genetic algorithms, genetic programming, genetic improvement, Automatic programming, evolution, explanation, knowledge base, maintenance, prototyping, specification, transformation", ISSN = "0098-5589", DOI = "doi:10.1109/TSE.1985.231877", size = "12 pages", abstract = "Automatic programming consists not only of an automatic compiler, but also some means of acquiring the high-level specification to be compiled, some means of determining that it is the intended specification, and some (interactive) means of translating this high-level specification into a lower-level one which can be automatically compiled. We have been working on this extended automatic programming problem for nearly 15 years, and this paper presents our perspective and approach to this problem and justifies it in terms of our successes and failures. Much of our recent work centers on an operational testbed incorporating usable aspects of this technology. This testbed is being used as a prototyping vehicle for our own research and will soon be released to the research community as a framework for development and evolution of Common Lisp systems.", notes = "Cited by \cite{Balzer:2010:FoSER} Also known as \cite{1701945}", } @InProceedings{Balzer:2010:FoSER, author = "Robert Balzer", title = "Why Haven't We Automated Programming", booktitle = "Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research", year = "2010", pages = "13--16", address = "Santa Fe, New Mexico, USA", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic improvement, automatic programming, interactive, refinement", isbn13 = "978-1-4503-0427-6", URL = "http://doi.acm.org/10.1145/1882362.1882366", DOI = "doi:10.1145/1882362.1882366", acmid = "1882366", size = "4", notes = "Not GP, FSE18, http://fse18.cse.wustl.edu/foserprogram.html", } @Article{bamshad:2022:Machines, author = "Hamid Bamshad and Seongwon Jang and Hyemi Jeong and Jaesung Lee and Hyunseok Yang", title = "Comparison between Genetic Programming and Dynamic Models for Compact Electrohydraulic Actuators", journal = "Machines", year = "2022", volume = "10", number = "10", pages = "Article No. 961", keywords = "genetic algorithms, genetic programming", ISSN = "2075-1702", URL = "https://www.mdpi.com/2075-1702/10/10/961", DOI = "doi:10.3390/machines10100961", abstract = "A compact electrohydraulic actuator (C-EHA) is an innovative hydraulic system with a wide range of applications, particularly in automation, robotics, and aerospace. The actuator provides the benefits of hydraulics without the expense and space requirements of full-sized hydraulic systems and in a much cleaner manner. However, this actuator is associated with some disadvantages, such as a high level of nonlinearity, uncertainty, and a lack of studies. The development of a robust controller requires a thorough understanding of the system behaviour as well as an accurate dynamic model of the system; however, finding an accurate dynamic model of a system is not always straightforward, and it is considered a significant challenge for engineers, particularly for a C-EHA because the critical parameters inside cannot be accessed. Our research aims to evaluate and confirm the ability of genetic programming (GP) to model a nonlinear system for a C-EHA. In our paper, we present and develop a GP model for the C-EHA system. Furthermore, our study presents a dynamic model of the system for comparison with the GP model. As a result, by using this actuator in the 1-DOF arm system and conducting experiments, we confirmed that the GP model has a better performance with less positional error compared with the proposed dynamic model. The model can be used to conduct further studies, such as designing controllers or system simulations.", notes = "also known as \cite{machines10100961}", } @InProceedings{conf/emo/BandaruD13, author = "Sunith Bandaru and Kalyanmoy Deb", title = "A Dimensionally-Aware Genetic Programming Architecture for Automated Innovization", booktitle = "Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013", year = "2013", editor = "Robin C. Purshouse and Peter J. Fleming and Carlos M. Fonseca and Salvatore Greco and Jane Shaw", volume = "7811", series = "Lecture Notes in Computer Science", pages = "513--527", address = "Sheffield, UK", month = mar # " 19-22", publisher = "Springer", keywords = "genetic algorithms, genetic programming, dimensional awareness, automated innovization, multi-objective optimization, design principles, NSGA-II, Matlab, GA SmallGP", bibdate = "2013-03-13", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/emo/emo2013.html#BandaruD13", isbn13 = "978-3-642-37139-4", URL = "https://www.egr.msu.edu/~kdeb/papers/k2012015.pdf", URL = "http://dx.doi.org/10.1007/978-3-642-37140-0", DOI = "doi:10.1007/978-3-642-37140-0_39", size = "15 pages", abstract = "Automated innovization is an unsupervised machine learning technique for extracting useful design knowledge from Pareto-optimal solutions in the form of mathematical relationships of a certain structure. These relationships are known as design principles. Past studies have shown the applicability of automated innovization on a number of engineering design optimisation problems using a multiplicative form for the design principles. In this paper, we generalise the structure of the obtained principles using a tree-based genetic programming framework. While the underlying innovisation algorithm remains the same, evolving multiple trees, each representing a different design principle, is a challenging task. We also propose a method for introducing dimensionality information in the search process to produce design principles that are not just empirical in nature, but also meaningful to the user. The procedure is illustrated for three engineering design problems.", notes = "See also KanGAL Report Number 2012015 NB k2012015.pdf not identical to EMO 2013 publication", } @PhdThesis{Bandaru_thesis, author = "Sunith Bandaru", title = "Automated Innovization: Knowledge discovery through multi-objective optimization", school = "Indian Institute of Technology Kanpur", year = "2013", address = "India", keywords = "genetic algorithms, genetic programming, Innovization, MEMS", URL = "https://drive.google.com/file/d/0B8WHZC_8VuhxZ3FWenBfa19MSDQ/view", URL = "https://www.iitk.ac.in/kangal/deb_phd.shtml", size = "227 pages", abstract = "In recent years, there has been a growing interest in the field of post-optimality analysis. In a single objective scenario, this usually concerns optimality, sensitivity and robustness studies on the obtained solution. Multi-objective optimization on the other hand, poses an additional challenge in that there are a multitude of possible solutions (when the objectives are conflicting) which are all said to be Pareto-optimal. These solutions may collectively hold crucial design information. Properties that are common to all or most Pareto-optimal solutions can be considered as characteristic features that make good designs. Knowledge of such features, in addition to providing better insights into the problem at hand, enables the designer to hand craft solutions for other optimization tasks which are structurally similar to it; thus eliminating the need to actually optimize. Innovization is the process of extracting this information in the form of design principles, which are basically mathematical expressions relating various problem entities like variables, objectives and constraint functions. Manual innovization involves the visual identification of correlations between problem entities through two and three-dimensional plots. Thereafter, appropriate functions are used for regression and the design principles are obtained. Though the procedure has been applied to many engineering design problems, the human element involved in it limits its use in most practical applications. The present thesis firstly proposes automated innovization, an unsupervised machine learning technique that can identify correlations in any multi-dimensional space formed by variables, objectives, etc. specified by the user and subsequently performs a selective regression on the correlated part of the Pareto-optimal dataset to obtain a design principle. The correlations are automatically identified by a customized grid-based clustering algorithm and the design principle is evolved using a genetic algorithm. Next, the procedure is extended so that design principles hidden in all possible Euclidean spaces formed by the variables and objectives (and any other user-defined functions) can be obtained simultaneously, without any human interaction, in a single run of the algorithm. This is accomplished by introducing a niching strategy to evolve different species of design principles in the same population of a genetic algorithm. Automation in innovization is achieved at the cost of restricting the mathematical structure of the design principles to a certain form, the significance of which becomes clear by observing physical laws in nature. Later in this thesis, a tree-based genetic programming framework is integrated into automated innovization to obtain design principles of any generic mathematical structure. Dimensionality information is introduced in the search process to produce design principles that are meaningful to the designer. Next, the proposed automated innovization technique is used to obtain design principles for four real-world multi-objective design optimization problems from varied fields. They are: noise barrier design optimization, polymer extrusion process optimization, friction stir welding process optimization and MEMS (MicroElectroMechanical Systems) resonator design optimization. In each case the obtained design principles are presented to experts of the respective fields for interpretation to gain insights. Secondly, this thesis introduces two new innovization concepts, namely higher-level innovization and lower-level innovization. Multi-objective optimization problem formulations involve many settings that are not changed during the solution process. However, once the trade-off front is generated, the designer may wish to change them and rerun the optimization, thus obtaining more fronts. This happens in many real-world situations where the designer is initially unsure about problem elements such as constraints, variable bounds, parameters and even objective functions. Higher-level innovization answers questions like: Are the features of the original problem still valid for other generated fronts? If not, how do they change with the modified setting?. The name reflects the fact that higher-level design knowledge is gained in the process. Sometimes lower-level design knowledge may also be desired. Consider the situation when after obtaining a set of trade-off solutions for a multi-objective design problem, a posteriori decision-making approach is used to identify a region of preference on the trade-off front. Now the designer may be interested in knowing features that are common to solutions only in this partial set and are not seen in rest of the trade-off solutions, so that the designer is specifically aware of properties associated with the chosen solutions. In this thesis, the automated innovization technique is extended to perform both higher and lower-level innovization. Thirdly, this thesis studies the temporal evolution of design principles obtained using automated innovization during the course of optimization. Results on a few engineering design problems reveal that certain important design features start to evolve early on, whereas some detailed design features appear later during optimization. Interestingly, there exists a simile between evolution of design principles in engineering and human evolution. Such information about the hierarchy of key design principles should enable designers to have a deeper understanding of their problems.", notes = "SunithBandaru_PhDThesis.pdf", } @Article{Bandaru:2015:EJOR, author = "Sunith Bandaru and Tehseen Aslam and Amos H. C. Ng and Kalyanmoy Deb", title = "Generalized higher-level automated innovization with application to inventory management", journal = "European Journal of Operational Research", volume = "243", number = "2", pages = "480--496", year = "2015", ISSN = "0377-2217", DOI = "doi:10.1016/j.ejor.2014.11.015", URL = "http://www.sciencedirect.com/science/article/pii/S0377221714009199", abstract = "This paper generalises the automated innovization framework using genetic programming in the context of higher-level innovisation. Automated innovization is an unsupervised machine learning technique that can automatically extract significant mathematical relationships from Pareto-optimal solution sets. These resulting relationships describe the conditions for Pareto-optimality for the multi-objective problem under consideration and can be used by scientists and practitioners as thumb rules to understand the problem better and to innovate new problem solving techniques; hence the name innovization (innovation through optimisation). Higher-level innovization involves performing automated innovization on multiple Pareto-optimal solution sets obtained by varying one or more problem parameters. The automated innovization framework was recently updated using genetic programming. We extend this generalisation to perform higher-level automated innovization and demonstrate the methodology on a standard two-bar bi-objective truss design problem. The procedure is then applied to a classic case of inventory management with multi-objective optimisation performed at both system and process levels. The applicability of automated innovization to this area should motivate its use in other avenues of operational research.", keywords = "genetic algorithms, genetic programming, Automated innovization, Higher-level innovization, Inventory management, Knowledge discovery", } @InProceedings{Banerjee:2020:SSCI, author = "Subhashis Banerjee and Sushmita Mitra", title = "Evolving Optimal Convolutional Neural Networks", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2677--2683", abstract = "Among the different Deep Learning (DL) models, the deep Convolutional Neural Networks (CNNs) have demonstrated impressive performance in a variety of image recognition or classification tasks. Although CNNs do not require feature engineering or manual extraction of features at the input level, yet designing a suitable CNN architecture necessitates considerable expert knowledge involving enormous amount of trial-and-error activities. In this paper we attempt to automatically design a competitive CNN architecture for a given problem while consuming reasonable machine resource(s) based on a modified version of Cartesian Genetic Programming (CGP). As CGP uses only the mutation operator to generate offsprings it typically evolves slowly. We develop a new algorithm which introduces crossover to the standard CGP to generate an optimal CNN architecture. The genotype encoding scheme is changed from integer to floating-point representation for this purpose. The function genes in the nodes of the CGP are chosen as the highly functional modules of CNN. Typically CNNs use convolution and pooling, followed by activation. Rather than using each of them separately as a function gene for a node, we combine them in a novel way to construct highly functional modules. Five types of functions, called ConvBlock, average pooling, max pooling, summation, and concatenation, were considered. We test our method on an image classification dataset CIFAR10, since it is being used as the benchmark for many similar problems. Experiments demonstrate that the proposed scheme converges fast and automatically finds the competitive CNN architecture as compared to state-of-the-art solutions which require thousands of generations or GPUs involving huge computational burden.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308201", month = dec, notes = "Also known as \cite{9308201}", } @Article{Bang:2013:IJCAS, author = "Sung-Woo Bang and Jaekwang Kim and Jee-Hyong Lee", title = "An Approach of Genetic Programming for Music Emotion Classification", journal = "International Journal of Control, Automation and Systems", year = "2013", volume = "11", number = "6", pages = "1290--1299", month = dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Classification algorithm, emotion recognition, music information retrieval", ISSN = "1598-6446", language = "English", DOI = "doi:10.1007/s12555-012-9407-7", size = "10 pages", abstract = "In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer's arousal-valence plane which is one of representative human emotion models. Thayer's plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others.", } @Article{Banga:2013:ijset, author = "Manu Banga", title = "Computational Hybrids Towards Software Defect Predictions", journal = "International Journal of Scientific Engineering and Technology", year = "2013", volume = "2", number = "5", pages = "311--316", ISSN = "2277-1581", bibsource = "OAI-PMH server at doaj.org", language = "English", oai = "oai:doaj.org/article:12a6cd2f16e947d7969f01df7e2544d9", rights = "CC by-nc-nd", keywords = "genetic algorithms, genetic programming, MLR, SVR, CART, MARS, MPFF, RBF", URL = "http://ijset.com/ijset/publication/v2s5/paper1.pdf", URL = "http://ijset.com/archive/v2i5", abstract = "In this paper, new computational intelligence sequential hybrid architectures involving Genetic Programming (GP) and Group Method of Data Handling (GMDH) viz. GPGMDH. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are also developed. We also performed GP based feature selection. The efficacy of Multiple Linear Regression (MLR), Polynomial Regression, Support Vector Regression (SVR), Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), Multilayer FeedForward Neural Network (MLFF), Radial Basis Function Neural Network (RBF), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro--Fuzzy Inference System (DENFIS), TreeNet, Group Method of Data Handling and Genetic Programming is tested on the NASA dataset. Ten-fold cross validation and t-test are performed to see if the performances of the hybrids developed are statistically significant.", } @MastersThesis{Brock_Baniasadi_Maryam_2013, author = "Maryam Baniasadi", title = "Genetic Programming for Non-Photorealistic Rendering", school = "Department of Computer Science, Brock University", year = "2013", address = "St. Catharines, Ontario, Canada L2S 3A1", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10464/4304", URL = "https://dr.library.brocku.ca/handle/10464/4304", URL = "https://dr.library.brocku.ca/bitstream/handle/10464/4304/Brock_Baniasadi_Maryam_2013.pdf", URL = "http://www.cosc.brocku.ca/archive/sites/all/files/downloads/research/cs1308.pdf", size = "193 pages", abstract = "This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.", notes = "Also available as Technical Report # CS-13-08", } @Article{Baniasadi:2015:GPEM, author = "Maryam Baniasadi and Brian J. Ross", title = "Exploring non-photorealistic rendering with genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "2", pages = "211--239", month = jun, keywords = "genetic algorithms, genetic programming, Evolutionary art", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9234-0", size = "29 pages", abstract = "The field of evolutionary art focuses on using artificial evolution as a means for generating and exploring artistic images and designs. Here, we use evolutionary computation to generate painterly styles of images. A source image is read into the system, and a genetic program is evolved that will re-render the image with non-photorealistic effects. A main contribution of this research is that the colour mixing expression is evolved, which permits a variety of interesting NPR effects to arise. The mixing expression evaluates mathematical properties of the dynamically changing canvas, which results in the evolution of adaptive NPR procedures. Automatic fitness evaluation includes Ralph's aesthetic model, colour matching, and direct luminosity matching. A few simple techniques for economical brush stroke application on the canvas are supported, which produce different stylistic effects. Using our approach, a number of established, as well as innovative, non-photorealistic painting effects were produced.", } @InProceedings{Banik:2015:ICCIT, author = "Shipra Banik and A. F. M. Khodadad Khan", booktitle = "2015 18th International Conference on Computer and Information Technology (ICCIT)", title = "Forecasting US NASDAQ stock index values using hybrid forecasting systems", year = "2015", pages = "282--287", abstract = "Capability to predict precise future stock values is the most important factor in financial market to make profit. Because of virtual trading, now a day this market has turn into one of the hot targets where any person can earn profit. Thus, predicting the correct future value of a stock has become an area of hot interest. This paper attempt to forecast NASDAQ stock index values using novel hybrid forecasting models based on widely used soft computing models and time series models. The daily historical US NASDAQ closing stock index for the periods of 08 February 1971 to 24 July 2015 is used and is applied our proposed hybrid forecasting models to see whether considered forecasting models can closely forecast daily NASDAQ stock index values. Mean absolute error and root mean square error between observed and predicted NASDAQ stock index are considered as evaluation criteria. The result is compared on the basis of selected individual forecasting time series model and individual soft computing forecasting models and the proposed hybrid forecasting models. Our experimental evidences show that the proposed hybrid back-propagation artificial neural network and genetic algorithm forecasting model has outperformed as compare to other considered forecasting models for forecasting daily US NASDAQ stock index. We trust that daily US NASDAQ stock index forecasts will be notice for a number of spectators who wish to construct strategies about this index.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCITechn.2015.7488083", month = dec, notes = "School of Engineering and Computer Science, Independent University, Bangladesh, Dhaka, Bangladesh Also known as \cite{7488083}", } @Article{Bankhead:2007:GPEM, author = "Armand {Bankhead III} and Robert B. Heckendorn", title = "Using evolvable genetic cellular automata to model breast cancer", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "4", pages = "381--393", month = dec, note = "special issue on medical applications of Genetic and Evolutionary Computation", keywords = "genetic algorithms, Genetic cellular automata, DCIS, Progenitor hierarchy, Ductal simulation, Hereditary genetic predisposition, Hereditary breast cancer, CA", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9042-x", abstract = "Cancer is an evolutionary process. Mutated cells undergo selection for abnormal growth and survival creating a tumour. We model this process with cellular automata that use a simplified genetic regulatory network simulation to control cell behaviour and predict cancer etiology. Our genetic model gives us the ability to relate genetic mutation to cancerous outcomes. The simulation uses known histological morphology, cell types, and stochastic behavior to specifically model ductal carcinoma in situ (DCIS), a common form of non-invasive breast cancer. Using this model we examine the effects of hereditary predisposition on DCIS incidence and aggressiveness. Results show that we are able to reproduce in vivo pathological features to hereditary forms of breast cancer: earlier incidence and increased aggressiveness. We also show that a contributing factor to the different pathology of hereditary breast cancer results from the ability of progenitor cells to pass cancerous mutations on to offspring.", notes = "155 node beowulf cluster", } @InProceedings{banks:2004:lbp, author = "Edwin Roger Banks and James Hayes and Edwin Nunez", title = "Parametric Regression Through Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP001.pdf", abstract = "Parametric regression in genetic programming can substantially speed up the search for solutions. Paradoxically, the same technique has difficulty finding a true optimum solution. The parametric formulation of a problem results in a fitness landscape that looks like an inverted brush with many bristles of almost equal length (individuals of high fitness), but with only one bristle that is very slightly longer than the rest, the optimum solution. As such it is easy to find very good, even outstanding solutions, but very difficult to locate the optimum solution. In this paper parametric regression is applied to a minimum-time-to-target problem. The solution is equivalent to the classical brachistochrone. Two formulations were tried: a parametric regression and the classical symbolic regression formulation. The parametric approach was superior without exception. We speculate the parametric approach is more generally applicable to other problems and suggest areas for more research.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{banks:2004:msa:erban, author = "E. R. Banks and J. C. Hayes and E. Nunez", title = "Parametric Regression Through Genetic Programming", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WMSA003.pdf", notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{Banks:gecco05lbp, author = "Edwin Roger Banks and Edwin Nunez and Paul Agarwal and Claudette Owens and Marshall McBride and Ron Liedel", title = "Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}", year = "2005", month = "25-29 " # jun, editor = "Franz Rothlauf", address = "Washington, D.C., USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/66-banks.pdf", keywords = "genetic algorithms, genetic programming", abstract = "According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect and discriminate buried UXO from other objects. We describe the results of work performed on data gathered by the GeoPhex GEM-3 electromagnetic sensor during their attempts to discriminate buried UXO at the U.S. Army Jefferson Proving Ground (JPG). We used a variety of evolutionary computing (EC) approaches that included genetic programming, genetic algorithms, and decision-tree methods. All approaches were essentially formulated as regression problems whereby the EC algorithms used sensor data to evolve buried UXO discrimination chromosomes. Predictions were then compared with a ground-truth file and the number of false positives and negatives determined", notes = "Distributed on CD-ROM at GECCO-2005", } @InProceedings{Banks:gecco06lbp, author = "Edwin Roger Banks and Edwin Nunez and Paul Agarwal and Marshall McBride and Ronald Liedel and Claudette Owens", title = "A Comparison of Evolutionary Computing Techniques Used to Model Bi-Directional Reflectance Distribution Functions", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}", year = "2006", month = "8-12 " # jul, editor = "J{\"{o}}rn Grahl", address = "Seattle, WA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006etc/papers/lbp128.pdf", notes = "Distributed on CD-ROM at GECCO-2006", keywords = "genetic algorithms, genetic programming, evolutionary computation, hybrid genetic programming, symbolic regression, Bi-directional reflectance distribution function, BRDF, parsimony, Phong model", abstract = "Bi-Directional Reflectance Distribution Functions are used in many fields including computer animation modeling, military defense (radar, lidar, etc.), and others. This paper explores a variety of approaches to modelling BRDFs using different evolutionary computing (EC) techniques. We concentrate on genetic programming (GP) and in hybrid GP approaches, obtaining very close correspondence between models and data. The problem of obtaining parameters that make particular BRDF models fit to laboratory-measured reflectance data is a classic symbolic regression problem. The goal of this approach is to discover the equations that model laboratory-measured data according to several criteria of fitness. These criteria involve closeness of fit, simplicity or complexity of the model (parsimony), form of the result, and speed of discovery. As expected, free form, unconstrained GP gave the best results in terms of minimising measurement errors. However, it also yielded the most complex model forms. Certain constrained approaches proved to be far superior in terms of speed of discovery. Furthermore, application of mild parsimony pressure resulted in not only simpler expressions, but also improved results by yielding small differences between the models and the corresponding laboratory measurements.", } @InProceedings{DBLP:conf/gecco/BanksAMO09, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasks", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "1971--1976", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570261", abstract = "How does one choose an initial set of parameters for an evolutionary computing algorithm? Clearly some choices are dictated by the problem itself, such as the encoding of a problem solution, or how much time is available for running the evolution. Others, however, are frequently found by trial-and-error. These may include population sizes, number of populations, type of selection, recombination and mutation rates, and a variety of other parameters. Sometimes these parameters are allowed to co-evolve along with the solutions rather than by trial-and-error. But in both cases, an initial setting is needed for each parameter. When there are hundreds of parameters to be adjusted, as in some evolutionary computation tools, one would like to just spend time adjusting those that are believed to be most important, or sensitive, and leave the rest to start with an initial default value. Thus the primary goal of this paper is to establish the relative importance of each parameter. Establishing general guidance to assist in the determination of these initial default values is another primary goal of this paper. We propose to develop this guidance by studying the solutions resulting from variations around the default starting parameters applied across fifteen different application types.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{DBLP:conf/gecco/BanksAMO09a, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "Lessons learned in application of evolutionary computation to a set of optimization tasks", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "1977--1982", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570262", abstract = "Many GECCO papers discuss lessons learned in a particular application, but few papers discuss lessons learned over an ensemble of problem areas. A scan of the tables of contents of the Proceedings from GECCO 2005 and 2006 showed no paper title stressing lessons learned although the term {"}pitfall{"} appeared occasionally in abstracts, typically applying to a particular practice. We present in this paper a set of broadly applicable {"}lessons learned{"} in the application of evolutionary computing (EC) techniques to a variety of problem areas and present advice related to encoding, running, monitoring, and managing an evolutionary computing task.", notes = "Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @InProceedings{Banks:2009:HPCMP-UGC, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "Evolving Image Noise Filters through Genetic Programming", booktitle = "DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC), 2009", year = "2009", month = "15-18 " # jun, pages = "307--312", abstract = "A form of Evolutionary Computation (EC) called Genetic Programming (GP) was used to automatically discover sequences of image noise filters to remove two types of image noise and a type of communications noise associated with a remotely sensed imagery. Sensor noise was modelled by the addition of salt-and-pepper and grayscale noise to the image. Communication noise was modeled by inserting a series of blank pixels in selected image rows to replicate dropped pixel segments occurring during communication interruptions of sequential uncompressed image information. A known image was used for training the evolver. Heavy amounts of noise were added to the known image, and a filter was evolved. (The filtered image was compared to the original with the average image-to-image pixel error establishing the fitness function.). The evolved filter derived for the noisy image was then applied to never-before-seen imagery affected by similar noise conditions to judge the universal applicability of the evolved GP filter. Examples of all described images are included in the presentation. A variety of image filter primitives were used in this experiment. The evolved sequences of primitives were each then sequentially applied to produce the final filtered image. These filters were evolved over a typical run length of one week each on a small Linux cluster. Once evolved, the filters were then transported to a PC for application to the never-before-seen images, using an evolve-once, apply-many-times approach. The results of this image filtering experiment were quite dramatic.", keywords = "genetic algorithms, genetic programming, Linux cluster, communication interruptions, communications noise, evolutionary computation, grayscale noise, image filtering, image noise filters, remotely sensed imagery, salt-and-pepper noise, sensor noise, sequential uncompressed image information, Linux, filtering theory, image denoising, image resolution, image segmentation, image sequences", DOI = "doi:10.1109/HPCMP-UGC.2009.50", notes = "COLSA Corp., Huntsville, AL, USA Also known as \cite{5729481}", } @InProceedings{DBLP:conf/gecco/BanksAMO09b, author = "Edwin Roger Banks and Paul Agarwal and Marshall McBride and Claudette Owens", title = "Toward a universal operator encoding for genetic programming", booktitle = "GECCO-2009 Late-Breaking Papers", year = "2009", editor = "Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore", pages = "1983--1986", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1570256.1570263", size = "4 pages", abstract = "The 2002 CEC paper entitled {"}Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition{"} by Ursem and Krink \cite{ursem:2002:gpwsofaedas} proposed to blend certain arithmetic operators by interpolation to smooth the transition from one operator to another in the fitness landscape. Inspired by their idea, herein it is shown how to generalise further by using combinations of more than two operators, requiring log(N) additional parameters for each N operators so combined. Comparative results are reported for the application of this methodology to a variety of optimisation tasks including symbolic regression, an aspherical lens system design, a UAV path optimization, and a remote sensor image noise filter design.", notes = "cites \cite{page:1999:smuxspmGP}. Parsimony via fitness penalty, non-spherical lens design. Worse except for image noise filter where universal function set does best. Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.", } @Article{Bannister:2014:VH, author = "C. A. Bannister and C. J. Currie and A. Preece and I. Spasic", title = "Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease", journal = "Value in Health", volume = "17", number = "3", pages = "A200--A201", year = "2014", note = "ISPOR 19th Annual International Meeting Research Abstracts", keywords = "genetic algorithms, genetic programming", ISSN = "1098-3015", DOI = "doi:10.1016/j.jval.2014.03.1171", URL = "http://www.sciencedirect.com/science/article/pii/S1098301514012224", size = "0.1 pages", abstract = "Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this study was to demonstrate the utility of genetic programming for the automatic development of clinical prediction models using cardiovascular disease as a case study.", notes = "PRM115", } @PhdThesis{phd/ethos/Bannister15, title = "Automated Development of Clinical Prediction Models Using Genetic Programming", author = "Christian A. Bannister", year = "2015", school = "School of Computer Science \& Informatics, Cardiff University", address = "UK", month = sep, keywords = "genetic algorithms, genetic programming", URL = "http://orca.cf.ac.uk/90825/", URL = "http://orca.cf.ac.uk/90825/1/2016bannistercaphd.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685486", size = "410 pages", abstract = "Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method (Cox regression) in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of other cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it used fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach.", notes = "British Library, EThOS Supervisor: Irena Spasic", } @TechReport{banzhaf:mrl:tech, author = "Wolfgang Banzhaf", title = "Genetic Programming for Pedestrians", institution = "Mitsubishi Electric Research Laboratories, Inc.", year = "1993", type = "MERL Technical Report", number = "TR93-03", address = "Cambridge, MA, USA", month = dec, keywords = "genetic algorithms, genetic programming", URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/pedes93.ps.gz", URL = "https://merl.com/publications/docs/TR93-03.pdf", size = "14 pages", abstract = "We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to introduce mechanisms like transscription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for the prediction of sequences of integer numbers.", notes = "As \cite{banzhaf:mrl}", } @InProceedings{banzhaf:mrl, author = "Wolfgang Banzhaf", title = "Genetic Programming for Pedestrians", institution = "Mitsubishi Electrical Research Laboratories, Cambridge Research Center", year = "1993", booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93", editor = "Stephanie Forrest", publisher = "Morgan Kaufmann", pages = "628", address = "University of Illinois at Urbana-Champaign", month = "17-21 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-299-2", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GenProg_forPed.ps.Z", abstract = "We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to introduce mechanisms like transcription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for the prediction of sequences of integer numbers.", notes = "Also available as MRL Technical Report 93-03 11 pages. \cite{banzhaf:mrl:tech} 225 bit GA, 5 bit grouping encode terminal or two arg function, clean up by editing and repair to produce variable length tree shaped prog. No looping, recursion or memory. Demonstrated on learning sequences of small integers, fails on primes. One-Page Summary. Also known as \cite{DBLP:conf/icga/Banzhaf93}", } @InProceedings{banzhaf:1994:ppsn3, author = "Wolfgang Banzhaf", title = "Genotype-Phenotype-Mapping and Neutral Variation -- A case study in Genetic Programming", booktitle = "Parallel Problem Solving from Nature III", year = "1994", editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner", series = "LNCS", volume = "866", pages = "322--332", address = "Jerusalem", publisher_address = "Berlin, Germany", month = "9-14 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-58484-6", URL = "http://www.cs.mun.ca/~banzhaf/papers/ppsn94.pdf", URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/ppsn94.ps.gz", broken = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6", DOI = "doi:10.1007/3-540-58484-6_276", size = "11 pages", abstract = "We propose the application of a genotype-phenotype mapping to the solution of constrained optimization problems. The method consists of strictly separating the search space of genotypes from the solution space of phenotypes. A mapping from genotypes into phenotypes provides for the appropriate expression of information represented by the genotypes. The mapping is constructed as to guarantee feasibility of phenotypic solutions for the problem under study. This enforcing of constraints causes multiple genotypes to result in one and the same phenotype. Neutral variants are therefore frequent and play an important role in maintaining genetic diversity. As a specific example, we discuss Binary Genetic Programming (BGP), a variant of Genetic Programming that uses binary strings as genotypes and program trees as phenotypes.", notes = "PPSN3 Tested on symbolic regression of 0.5x**2 and exp(-3.0*x**2) Produces high level code (FORTRAN, C?) which is compiled, claims this gives huge speedup. ", } @InProceedings{banzhaf:1997:gabrrfr, author = "Wolfgang Banzhaf and Peter Nordin and Markus Olmer", title = "Generating Adaptive Behavior for a Real Robot using Function Regression within Genetic Programming", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "35--43", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.mun.ca/~banzhaf/papers/robot_over.pdf", size = "11 pages", abstract = "We discuss the generation of adaptive behaviour for an autonomous robot within the framework of a special kind of function regression used in compiling Genetic Programming (GP). The control strategy for the robot is derived, using an evolutionary algorithm, from a continuous improvement of machine language programs which are varied and selected against each other. We give an overview of our recent work on several fundamental behaviors like obstacle avoidance and object following adapted from programs that were originally random sequences of commands. It is argued that the method is generally applicable where there is a need for quick adaptation within real-time problem domains", notes = "GP-97", } @InCollection{Banzhaf:1997:HEC, author = "Wolfgang Banzhaf", title = "Interactive Evolution", booktitle = "Handbook of Evolutionary Computation", publisher = "Oxford University Press", publisher_2 = "Institute of Physics Publishing", year = "1997", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "section C2.9", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0392-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf", DOI = "doi:10.1201/9781420050387.ptc", size = "6 pages", abstract = "We present a different approach to directing the evolutionary process through interactive selection of solutions by the human user. First the general context of interactive evolution is set, then the standard interactive evolution algorithm is discussed together with more complicated variants. Finally, several application areas are discussed and uses for the new method are exemplified using design from the literature.", } @Book{banzhaf:1997:book, author = "Wolfgang Banzhaf and Peter Nordin and Robert E. Keller and Frank D. Francone", title = "Genetic Programming -- An Introduction; On the Automatic Evolution of Computer Programs and its Applications", publisher = "Morgan Kaufmann", publisher2 = "dpunkt.verlag", year = "1998", address = "San Francisco, CA, USA", address2 = "Heidelberg", month = jan, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-510-X", ISBN = "3-920993-58-6", URL = "https://www.amazon.co.uk/Genetic-Programming-Introduction-Artificial-Intelligence/dp/155860510X", broken = "http://www.elsevier.com/wps/find/bookdescription.cws_home/677869/description#description", notes = "details from banzhaf Tue, 23 Sep 1997 12:58:06 PDT updated banzhaf Fri, 23 Jun 2006 11:40:44 -0230 FROM THE FOREWORD BY J.R. KOZA Genetic programming addresses the problem of automatic programming, namely the problem of how to enable a computer to do useful things without instructing it, step by step, on how to do it. The rapid growth of the field of genetic programming reflects the growing recognition that, after half a century of research in the fields of artificial intelligence, machine learning, adaptive systems, automated logic, expert systems, and neural networks, we may finally have a way to achieve automatic programming. Genetic programming is fundamentally different from other approaches in terms of (i) its representation (namely, programs), (ii) the role of knowledge (none), (iii) the role of logic (none), and (iv) its mechanism (gleaned from nature) for getting to a solution within the space of possible solutions. FROM THE FIRST SECTION OF THE BOOK Automated programming will be one of the most important areas of computer science research over the next twenty years. Hardware speed and capability has leapt forward exponentially. Yet software consistently lags years behind the capabilities of the hardware. The gap appears to be ever increasing. Demand for computer code keeps growing but the process of writing code is still mired in the modern day equivalent of the medieval ``guild'' days. Like swords in the 15th century, muskets before the early 19th century and books before the printing press, each piece of computer code is, today, handmade by a craftsman for a particular purpose. The history of computer programming is a history of attempts to move away from the ``craftsman'' approach -- structured programming, object oriented programming, object libraries, rapid prototyping. But each of these advances leaves the code that does the real work firmly in the hands of a craftsman, the programmer. The ability to enable computers to learn to program themselves is of the utmost importance in freeing the computer industry and the computer user from code that is obsolete before it is released. ", size = "480 pages", } @Proceedings{banzhaf:1998:GP, title = "Genetic Programming", year = "1998", editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty", volume = "1391", series = "LNCS", address = "Paris", publisher_address = "Berlin", month = "14-15 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-64360-5", DOI = "doi:10.1007/BFb0055923", size = "232 pages", notes = "EuroGP'98 See also \cite{Poli:1998:egplb} Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming", } @Article{lemonde:1998:23apr, key = "lemonde", title = "Les Robots inventeent la vie", journal = "Le Monde", year = "1998", month = "23 Avril", keywords = "genetic algorithms, genetic programming", notes = "in french, Description of EvoRobot'98 in particular: Stefanio Nolfi and Dario Floreano, Jean Arcady-Meyer, Henrik Lund, \cite{dittrich:1998:lmrrm}, Nick Jakobi", } @Proceedings{banzhaf:1999:gecco99, title = "GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523", size = "2 volumes", notes = "GECCO-99", } @Misc{oai:CiteSeerPSU:400591, title = "Artificial Intelligence: Genetic Programming", author = "Wolfgang Banzhaf", year = "2000", month = jul # "~04", note = "Contract no: 20851A2/2/102", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:54843; oai:CiteSeerPSU:537988; oai:CiteSeerPSU:536890; oai:CiteSeerPSU:275725", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:400591", rights = "unrestricted", URL = "http://web.cs.mun.ca/~banzhaf/papers/ency.pdf", URL = "http://citeseer.ist.psu.edu/400591.html", size = "13 pages", abstract = "The term Genetic Programming describes a research area within the general field of Artificial Intelligence that deals with the evolution of computer code. This area is an out growth of two independent efforts in Artificial Intelligence, namely automatic programming and machine learning. Automatic programming is concerned with the induction of computer code which precisely fulfills certain functions, whereas machine learning studies improvement of computer programs through training and experience.", notes = "Survey/introduction to GP. See also \cite{Banzhaf2001789}", } @TechReport{oai:CiteSeerPSU:324880, author = "Wolfgang Banzhaf and Dirk Banscherus and Peter Dittrich", title = "Hierarchical Genetic Programming Using Local Modules", institution = "University of Dortmund", address = "Dortmund, Germany", year = "1998", type = "Technical Report", number = "50/98", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/2003/5365", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5365/1/ci56.pdf", citeseer-isreferencedby = "oai:CiteSeerPSU:39828", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:324880", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/324880.html", abstract = "This paper presents detailed experimental results for a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural hierarchy in that local modules themselves may define local sub-modules.", notes = "see also \cite{banzhaf:2000:IJ}", size = "pages", } @Article{banzhaf:2000:IJ, author = "Wolfgang Banzhaf and Dirk Banscherus and Peter Dittrich", title = "Hierarchical Genetic Programming using Local Modules", journal = "InterJournal Complex Systems", year = "2000", volume = "228", keywords = "genetic algorithms, genetic programming", URL = "http://www.interjournal.org/manuscript_abstract.php?44691", broken = "http://web.cs.mun.ca/~banzhaf/papers/iccs98.html", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5365/1/ci56.pdf", size = "18 pages", abstract = "This paper presents a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach allows for a natural recursiveness in that local modules themselves may define local sub-modules.", notes = "Category: Brief Article Status: Accepted Manuscript Number: [228] Submission Date: 981210 Revised On: 815 Subject(s): CX, CX.66 See also \cite{oai:CiteSeerPSU:324880}", } @Article{banzhaf:2000:genpletter, author = "W. Banzhaf and W. B. Langdon", title = "Some considerations on the reason for bloat", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "1", pages = "81--91", month = mar, email = "banzhaf@tarantoga.cs.uni-dortmund.de", keywords = "genetic algorithms, genetic programming, linear genomes, effective fitness, neutral variations", ISSN = "1389-2576", URL = "http://web.cs.mun.ca/~banzhaf/papers/genp_bloat.pdf", DOI = "doi:10.1023/A:1014548204452", abstract = "A representation-less model for genetic programming is presented. The model is intended to examine the mechanisms that lead to bloat in genetic programming (GP). We discuss two hypotheses (fitness causes bloat and neutral code is protective) and perform simulations to examine the predictions deduced from these hypotheses. Our observation is that predictions from both hypotheses are realized in the simulated model.", notes = "Article ID: 395990", } @Article{banzhaf:2000:ei, author = "Wolfgang Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "1/2", pages = "5--6", month = apr, ISSN = "1389-2576", DOI = "doi:10.1023/A:1010026829303", notes = "Article ID: 253701", } @Article{banzhaf:2000:IS, author = "Wolfgang Banzhaf", title = "The artificial evolution of computer code", journal = "IEEE Intelligent Systems", year = "2000", volume = "15", number = "3", pages = "74--76", month = may # "-" # jun, keywords = "genetic algorithms, genetic programming, machine code GP", ISSN = "1094-7167", URL = "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf", broken = "http://web.cs.mun.ca/~banzhaf/papers/ieee_intelligentsystems.pdf", URL = "http://citeseer.ist.psu.edu/399369.html", DOI = "doi:10.1109/5254.846288", size = "3 pages", abstract = "Over the past decade, the artificial evolution of computer code has become a rapidly spreading technology with many ramifications. Originally conceived as a means to enforce computer intelligence, it has now spread to all areas of machine learning and is starting to conquer pattern-recognition applications such as data mining and the human-computer interface.", notes = "part of \cite{hirsh:2000:GP}", } @Article{banzhaf:2000:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2000", volume = "1", number = "4", pages = "307", month = oct, ISSN = "1389-2576", DOI = "doi:10.1023/A:1010022522223", notes = "Article ID: 273809", } @InCollection{Banzhaf2001789, author = "W. Banzhaf", title = "Artificial Intelligence: Genetic Programming", editor = "Neil J. Smelser and Paul B. Baltes", booktitle = "International Encyclopedia of the Social \& Behavioral Sciences", publisher = "Pergamon", address = "Oxford", year = "2001", pages = "789--792", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-08-043076-8", DOI = "doi:10.1016/B0-08-043076-7/00557-X", URL = "http://www.sciencedirect.com/science/article/B7MRM-4MT09VJ-403/2/fa4e06852750b95eb2734f9ca37ae6ad", abstract = "Genetic Programming is a new method to generate computer programs. It was derived from the model of biological evolution. Programs are 'bred' through continuous improvement of an initially random population of programs. Improvements are made possible by stochastic variation of programs and selection according to prespecified criteria for judging the quality of a solution. Programs of Genetic Programming systems evolve to solve predescribed automatic programming and machine learning problems. In this contribution the origins and the context of Genetic Programming are discussed. The primary mechanisms behind the working of the method are then outlined. Next is a review of the state-of-the-art of Genetic Programming, including the major achievements of the method in recent years. This leads to an overview of the application areas where GP is most frequently used to present. Among these areas is robotics and the control of behavior, both of real and virtual agents. The article will conclude with a section on methodological issues and future directions. The use of Genetic Programming for simulation in the social sciences is briefly sketched.", } @Article{banzhaf:2001:intro, author = "W. Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "1", pages = "5", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/A:1010076931170", size = "1 page", notes = "Article ID: 319810", } @Article{banzhaf:2001:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "315--315", month = dec, ISSN = "1389-2576", DOI = "doi:10.1023/A:1017497620393", notes = "Article ID: 386360", } @Article{banzhaf:2002:intro, author = "W. Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "1", pages = "5--6", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/A:1017427619473", size = "2 pages", notes = "Article ID: 395987", } @Article{banzhaf:2002:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2002", volume = "3", number = "4", pages = "327", month = dec, ISSN = "1389-2576", DOI = "doi:10.1023/A:1020989508176", notes = "Article ID: 5103871", } @Article{banzhaf:2003:intro, author = "Wolfgang Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "1", pages = "5--6", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/A:1021808625350", notes = "Article ID: 5113069", } @InCollection{banzhaf:2003:GPTP, author = "Wolfgang Banzhaf", title = "Artificial Regulatory Networks and Genetic Programming", booktitle = "Genetic Programming Theory and Practice", publisher = "Kluwer", year = "2003", editor = "Rick L. Riolo and Bill Worzel", chapter = "4", pages = "43--61", keywords = "genetic algorithms, genetic programming, Regulatory Networks, Artificial Evolution, Evolutionary Algorithms, Development, Heterochrony", ISBN = "1-4020-7581-2", URL = "http://www.cs.mun.ca/~banzhaf/papers/toy_world3.pdf", URL = "http://www.springer.com/computer/ai/book/978-1-4020-7581-0", DOI = "doi:10.1007/978-1-4419-8983-3_4", abstract = "An artificial regulatory network able to reproduce a number of phenomena found in natural genetic regulatory networks (such as heterochrony, evolution, stability and variety of network behaviour) is proposed. The connection to a new genetic representation for Genetic Programming is outlined.", notes = "Part of \cite{RioloWorzel:2003}", size = "20 pages", } @InCollection{banzhaf:2003:ACI, author = "Wolfgang Banzhaf and Markus Brameier and Marc Stautner and Klaus Weinert", title = "Genetic Programming and Its Application in Machining Technology", booktitle = "Advances in Computational Intelligence: Theory and Practice", publisher = "Springer", year = "2003", editor = "Hans-Paul Schwefel and Ingo Wegener and Klaus Weinert", series = "Natural Computing Series", chapter = "7", pages = "194--241", keywords = "genetic algorithms, genetic programming, Linear Genetic Programming", ISBN = "3-540-43269-8", URL = "http://www.cs.mun.ca/~banzhaf/papers/CI-book-chapter.pdf", URL = "http://www.springer.com/computer/ai/book/978-3-540-43269-2", abstract = "Genetic Programming (GP) denotes a variants of evolutionary algorithms that breeds solutions to problems in the form of computer programs. In recent years, GP has become increasingly important for real-world applications, including engineering tasks in particular. This contribution integrates both further development of the GP paradigm and its applications to challenging problems in machining technology. Different variants of program representations are investigated.", notes = "Removal of Non-effective Code. Graph Interpretation. Linear Genetic Operators. Control of Variation Step Size. Control of Structural Diversity. Genetic Programming in Machining Technology. Tree-Based GP for Symbolic Regression. Graphical Representation. Parallelisation", } @Article{banzhaf:2004:intro, author = "Wolfgang Banzhaf", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "5", month = mar, ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017050.75941.55", notes = "Article ID: 5264730", } @Article{Banzhaf:2004:SSP, author = "Wolfgang Banzhaf", title = "Artificial chemistries - Toward Constructive Dynamical Systems", journal = "Solid State Phenomena", year = "2004", volume = "97/98", pages = "43--50", month = apr, keywords = "genetic algorithms, genetic programming, artificial chemistries, Self-Organization, Self-Assembly, Constructive Dynamical Systems", ISSN = "1662-9779", DOI = "doi:10.4028/www.scientific.net/SSP.97-98.43", abstract = "we consider constructive dynamical systems, taking one particular Artificial Chemistry as an example. We argue that constructive dynamical systems are in fact widespread in combinatorial spaces of Artificial Chemistries.", } @Article{Banzhaf:2004:JBPC, author = "Wolfgang Banzhaf and P. Dwight Kuo", title = "Network motifs in natural and artificial transcriptional regulatory networks", journal = "Journal of Biological Physics and Chemistry", year = "2004", volume = "4", number = "2", pages = "85--92", month = jun, keywords = "genetic algorithms, genetic programming", ISSN = "1512-0856", URL = "https://www.cs.mun.ca/~banzhaf/papers/JBPC.pdf", URL = "http://www.amsi.ge/jbpc/20404/2040405.html", URL = "http://www.amsi.ge/jbpc/20404/jbpc20404.html", size = "8 pages", abstract = "We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication/divergence process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These results are then compared with a network motif analysis of the gene expression networks of Escherichia coli and Saccharomyces cerevisiae. In addition, it is shown that certain individual network motifs may arise directly from the duplication/divergence mechanism.", } @Article{banzhaf:2004:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "7", month = dec, ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017051.93386.43", notes = "Article ID: 5264731", } @Article{banzhaf:2004:biogec, author = "Wolfgang Banzhaf and James Foster", title = "Editorial Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "2", pages = "119--120", month = jun, keywords = "genetic algorithms, genetic programming, bioinformatics", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000023710.47388.8b", notes = "BioGEC Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster", } @InCollection{banzhaf:2004:GPTP, author = "Wolfgang Banzhaf and Christian W. G. Lasarczyk", title = "Genetic Programming of an Algorithmic Chemistry", booktitle = "Genetic Programming Theory and Practice {II}", year = "2004", editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel", chapter = "11", pages = "175--190", address = "Ann Arbor", month = "13-15 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, artificial chemistry", ISBN = "0-387-23253-2", URL = "http://www.cs.mun.ca/~banzhaf/papers/algochem.pdf", DOI = "doi:10.1007/0-387-23254-0_11", size = "16 pages", abstract = "We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial chemistry. It lends itself well to distributed and parallel computing schemes in which synchronisation and coordination are not an issue.", notes = "part of \cite{oreilly:2004:GPTP2} Sin, UCI thyroid", } @Article{banzhaf:2004:BPC, author = "Wolfgang Banzhaf and P. Dwight Kuo", title = "Network motifs in natural and artificial transcriptional regulatory networks", journal = "Journal of Biological Physics and Chemistry", year = "2004", volume = "4", number = "2", pages = "50--63", keywords = "artificial life", ISBN = "0-387-23253-2", URL = "http://www.cs.mun.ca/~kuo/Motifs_Numerical_journal.pdf", URL = "http://www.amsi.ge/jbpc/20404/2040405.html", size = "11 pages", abstract = "We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication / divergence process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These results are then compared with a network motif analysis of the gene expression networks of Escherichia Coli and Saccharomyces cerevisiae. In addition, it is shown that certain individual network motifs may arise directly from the duplication / divergence mechanism.", } @InCollection{banzhaf:2004:cc, title = "The Challenge of Complexity", author = "Wolfgang Banzhaf and Julian Miller", booktitle = "Frontiers of Evolutionary Computation", editor = "Anil Menon", series = "Genetic Algorithms And Evolutionary Computation Series", volume = "11", chapter = "11", publisher = "Kluwer Academic Publishers", address = "Boston, MA, USA", year = "2004", pages = "243--260", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithm, Complexity, Scaling Problem, Development, Heterochrony", ISBN = "1-4020-7524-3", URL = "http://www.cs.mun.ca/~banzhaf/papers/challenge_rev.pdf", DOI = "doi:10.1007/1-4020-7782-3_11", abstract = "the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this ``challenge of complexity'' in Genetic Programming", } @InProceedings{banzhaf:2005:cPC, author = "Wolfgang Banzhaf", title = "Challenging the Program Counter", booktitle = "The Grand Challenge in Non-Classical Computation: International Workshop", year = "2005", editor = "Susan Stepney and Stephen Emmott", address = "York, UK", month = "18-19 " # apr, organisation = "University of York and Microsoft Research", keywords = "genetic algorithms, genetic programming, artificial chemistry", URL = "http://www.cs.york.ac.uk/nature/workshop/papers/Banzhaf.pdf", size = "3 pages", notes = "http://www.cs.york.ac.uk/nature/workshop/", } @Article{banzhaf:2005:intro, author = "Wolfgang Banzhaf", title = "Editorial", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "5", number = "2", pages = "135--136", month = jun, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-6162-z", size = "1.1 pages", } @Article{banzhaf:2005:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "5", number = "2", pages = "137--138", month = jun, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-6163-y", size = "1.2 pages", } @InCollection{banzhaf:2005:GPTP, author = "Wolfgang Banzhaf and Andre Leier", title = "Evolution on Neutral Networks in Genetic Programming", booktitle = "Genetic Programming Theory and Practice {III}", year = "2005", editor = "Tina Yu and Rick L. Riolo and Bill Worzel", volume = "9", series = "Genetic Programming", chapter = "14", pages = "207--221", address = "Ann Arbor", month = "12-14 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Neutrality, Linear GP, Networks, Population Dynamics", ISBN = "0-387-28110-X", URL = "http://www.cs.mun.ca/~banzhaf/papers/GPTP3.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.7947", oai = "oai:CiteSeerXPSU:10.1.1.137.7947", DOI = "doi:10.1007/0-387-28111-8_14", size = "15 pages", abstract = "We examine the behaviour of an evolutionary search on neutral networks in a simple linear GP system of a Boolean function space problem. To this end we draw parallels between notions in RNA-folding problems and in Genetic Programming, observe parameters of neutral networks and discuss the population dynamics via the occupation probability of network nodes in runs on their way to the optimal solution.", notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop", } @Article{banzhaf:2006:intro, author = "Wolfgang Banzhaf", title = "Introduction", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "6", number = "1", pages = "5--6", month = mar, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-7015-0", size = "1.1 pages", } @Article{banzhaf:2006:ack, author = "W. Banzhaf", title = "Acknowledgement", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "6", number = "1", pages = "7", month = mar, ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-7016-z", size = "1 page", notes = "list of reviewers", } @Article{Banzhaf:2006:NRG, author = "Wolfgang Banzhaf and Guillaume Beslon and Steffen Christensen and James Foster and Francois Kepes and Virginie Lefort and Julian Miller and Miroslav Radman and Jeremy J. Ramsden", title = "From Artificial Evolution to Computational Evolution: A Research Agenda", journal = "Nature Reviews Genetics", year = "2006", volume = "7", number = "9", pages = "729--735", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1471-0056", DOI = "doi:10.1038/nrg1921", size = "7 pages", abstract = "Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimisation and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.", notes = "We thank the people at Genopole Recherche, Evry, France, for generously sponsoring the meeting that initiated this paper.", } @Article{banzhaf:2007:intro, author = "Wolfgang Banzhaf", title = "Editorial introduction", journal = "Genetic Programming and Evolvable Machines", year = "2007", volume = "8", number = "1", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-007-9022-1", size = "2 pages", abstract = "As we have moved into the corporate sphere of Springer there were a number of changes, some subtle, some not so subtle. One change that is somewhat behind the scenes and eludes the eye of a reader is how Springer uses its distribution channels to spread the journal. Genetic Programming and Evolvable Machines is now accessible in over 5000 libraries across the globe. I think that speaks to the ability of this publisher, and its will to get the word out about our community. In the absence of an officially calculated impact factor I have taken the initiative myself to address this issue. Using the citation base of Google Scholar, we have evaluated the impact of GPEM by looking at all the papers published since its inception in 2000, up to May 2006. It turns out that authors did very well if publishing in GPEM. Their GPEM papers regularly featured prominently among their papers in terms of citations. 50% of our authors can count their GPEM paper among the first, second or third most cited paper of theirs. While this is certainly only true for half of the authors, it is indeed an achievement. So if you publish in GPEM, be prepared that your work is read, and also cited.", } @Article{Banzhaf:2007:Complexity, author = "Wolfgang Banzhaf and Nelishia Pillay", title = "Why Complex Systems Engineering needs Biological Development", journal = "Complexity", year = "2007", volume = "13", number = "2", pages = "12--21", month = nov # "/" # dec, keywords = "genetic algorithms, genetic programming", URL = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/cplx.20199.pdf", DOI = "doi:10.1002/cplx.20199", size = "10 pages", abstract = "Here we shall discuss the need of Complex Systems Engineering to adopt principles from natural development of complex biological organisms, besides principles of natural evolution, to accomplish the type of performance that biology achieves regularly. We shall situate Complex Systems Engineering and discuss an example of how it could be employed.", notes = "Essay and Commentary", } @InCollection{Banzhaf:2008:GPTP, author = "Wolfgang Banzhaf and Simon Harding and William B. Langdon and Garnett Wilson", title = "Accelerating Genetic Programming through Graphics Processing Units", booktitle = "Genetic Programming Theory and Practice {VI}", year = "2008", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "15", pages = "229--249", address = "Ann Arbor", month = "15-17 " # may, publisher = "Springer", size = "20 pages", keywords = "genetic algorithms, genetic programming, graphics processing units, parallel processing, GPU", DOI = "doi:10.1007/978-0-387-87623-8_15", size = "19 pages", abstract = "Graphics Processing Units (GPUs) are in the process of becoming a major source of computational power for numerical applications. Originally designed for application of time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. The true degree of parallelism of GPUs is often hidden from the user, making programming even more flexible and convenient. In this chapter we survey Genetic Programming methods currently ported to GPUs.", isbn13 = "978-0-387-87622-1", notes = "part of \cite{Riolo:2008:GPTP} published in 2009", } @InCollection{Banzhaf:2013:EB, author = "Wolfgang Banzhaf", title = "Evolutionary Computation and Genetic Programming", editor = "Akhlesh Lakhtakia and Raul Jose Martin-Palma", booktitle = "Engineered Biomimicry", publisher = "Elsevier", address = "Boston", year = "2013", pages = "429--447", chapter = "17", isbn13 = "978-0-12-415995-2", DOI = "doi:10.1016/B978-0-12-415995-2.00017-9", URL = "http://www.sciencedirect.com/science/article/pii/B9780124159952000179", keywords = "genetic algorithms, genetic programming, Algorithms, Artificial intelligence, Automatic programming, Bioinspired computing, Breeding, Crossover, Differential evolution, Evolutionary computation, Evolutionary programming, Evolution strategies, Generation, Human-competitive, Machine learning, Mutation, Natural selection, Population, Reproduction, Search space", abstract = "This chapter focuses on evolutionary computation, in particular genetic programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context and discuss the history of evolutionary computation and its variants before looking more closely at genetic programming. After a discussion of methods and the state of the art, we review application areas of genetic programming and its strength in providing human-competitive solutions.", } @Article{Banzhaf:2014:GPEM, author = "Wolfgang Banzhaf", title = "Genetic Programming and Emergence", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "63--73", month = mar, keywords = "genetic algorithms, genetic programming, Emergence, Emergent phenomena, Top-down causation, Repetitive patterns, Modularity", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9196-7", size = "11 pages", abstract = "Emergence and its accompanying phenomena are a widespread process in nature. Despite its prominence, there is no agreement in the sciences about the concept and how to define or measure emergence. One of the most contentious issues discussed is that of top-down (or downward) causation as a defining characteristic of systems with emergence. In this contribution we shall argue that emergence happens in Genetic Programming, for all the world to see", notes = "Commentaries to this article can be found at doi: 10.1007/s10710-013-9198-5, 10.1007/s10710-013-9199-4, 10.1007/s10710-013-9200-2, 10.1007/s10710-013-9201-1, 10.1007/s10710-013-9202-0, 10.1007/s10710-013-9203-z, 10.1007/s10710-013-9204-y. See \cite{Altenberg:2014:GPEM} \cite{Ekart:2014:GPEM} \cite{Leier:2014:GPEM} \cite{Krawiec:2014:GPEM} \cite{Montana:2014:GPEM} \cite{Sipper:2014:GPEM} \cite{Whigham:2014:GPEM} \cite{Banzhaf_reply:2014:GPEM}", } @Article{Banzhaf_reply:2014:GPEM, author = "Wolfgang Banzhaf", title = "Response to comments on ''Genetic Programming and Emergence''", journal = "Genetic Programming and Evolvable Machines", year = "2014", volume = "15", number = "1", pages = "103--108", month = mar, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9207-8", size = "6 pages", notes = "This response refers to the articles available at the following DOIs: 10.1007/s10710-013-9196-7; 10.1007/s10710-013-9198-5; 10.1007/s10710-013-9199-4; 10.1007/s10710-013-9200-2; 10.1007/s10710-013-9201-1; 10.1007/s10710-013-9202-0; 10.1007/s10710-013-9203-z; 10.1007/s10710-013-9204-y. See \cite{Banzhaf:2014:GPEM}", } @Article{Banzhaf:2016:TBS, author = "Wolfgang Banzhaf and Bert Baumgaertner and Guillaume Beslon and Rene Doursat and James Foster and Barry McMullin and Vinicius Veloso {de Melo} and Thomas Miconi and Lee Spector and Susan Stepney and Roger White", title = "Defining and Simulating Open-Ended Novelty: Requirements, Guidelines, and Challenges", journal = "Theory in Biosciences", year = "2016", volume = "135", number = "3", pages = "131--161", month = sep, note = "Special Issue in Commemoration of Olaf Breidbach - Part I", keywords = "genetic algorithms, genetic programming, open-ended evolution, Modeling and simulation, Open-ended evolution, Novelty, Innovation, Major transitions, Emergence", DOI = "doi:10.1007/s12064-016-0229-7", abstract = "The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system's model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community.", } @Proceedings{Banzhaf:2017:GPTP, title = "Genetic Programming Theory and Practice {XV}", year = "2017", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", series = "Genetic and Evolutionary Computation", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "http://www.springer.com/gb/book/9783319905112", DOI = "doi:10.1007/978-3-319-90512-9", size = "200 pages", abstract = "Provides papers describing cutting-edge work on the theory and applications of genetic programming (GP) Offers large-scale, real-world applications (big data) of GP to a variety of problem domains, including commercial and scientific applications as well as bioinformatics problems Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: exploiting subprograms in genetic programming, schema frequencies in GP, Accessible AI, GP for Big Data, lexicase selection, symbolic regression techniques, co-evolution of GP and LCS, and applying ecological principles to GP. It also covers several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.", notes = "Workshop 19-21 May 2016, published 2018. Also known as \cite{conf/gptp/2017}", } @InCollection{Banzhaf:2017:miller, author = "Wolfgang Banzhaf", title = "Some Remarks on Code Evolution with Genetic Programming", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "6", pages = "145--156", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, machine learning, ILP", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_6", abstract = "In this chapter we take a fresh look at the current status of evolving computer code using Genetic Programming methods. The emphasis is not so much on what has been achieved in detail in the past few years, but on the general research direction of code evolution and its ramifications for GP. We begin with a quick glance at the area of Search-based Software Engineering (SBSE), discuss the history of GP as applied to code evolution, consider various application scenarios, and speculate on techniques that might lead to a scaling-up of present-day approaches.", notes = "p148 'The transition between manual and automatic programming becomes continuous. Epochs. p153 evo-devo evolution and developmental approaches. p154 'big code' part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Proceedings{Banzhaf:2018:GPTP, title = "Genetic Programming Theory and Practice XVI", year = "2018", editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman", address = "Ann Arbor, USA", month = "17-20 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-04734-4", DOI = "doi:10.1007/978-3-030-04735-1", size = "xi + 234", abstract = "Contents: \cite{dolson:2018:GPTP} \cite{hintze:2018:GPTP} \cite{kelly:2018:GPTP} \cite{korns:2018:GPTP} \cite{kronberger:2018:GPTP} \cite{lalejini:2018:GPTP} \cite{metevier:2018:GPTP} \cite{miller:2018:GPTP} \cite{oneill:2018:GPTP} \cite{trujillo:2018:GPTP} \cite{yang:2018:GPTP}", notes = "Also known as \cite{DBLP:conf/gptp/2018}", } @Proceedings{Banzhaf:2019:GPTP, title = "Genetic Programming Theory and Practice XVII", year = "2019", editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel", address = "East Lansing, MI, USA", month = "16-19 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-39957-3", URL = "https://link.springer.com/book/10.1007/978-3-030-39958-0", URL = "https://doi.org/10.1007/978-3-030-39958-0", DOI = "doi:10.1007/978-3-030-39958-0", notes = "See \cite{Ofria:2019:GPTP}, \cite{Fernandez:2019:GPTP}, \cite{Freeland:2019:GPTP}, \cite{Goncalves:2019:GPTP}, \cite{Hu:2019:GPTP}, \cite{Kammerer:2019:GPTP}, \cite{Kelly:2019:GPTP}, \cite{Hintze:2019:GPTP}, \cite{Kordon:2019:GPTP}, \cite{Lehman:2019:GPTP}, \cite{Nicolau:2019:GPTP}, \cite{Olague:2019:GPTP}, \cite{Spector:2019:GPTP}, \cite{Rajapakse:2019:GPTP}, \cite{Saini:2019:GPTP}, \cite{Schmidt:2019:GPTP}, \cite{Shahrzad:2019:GPTP}, \cite{Sipper:2019:GPTP}, \cite{Sloss:2019:GPTP}, \cite{Heywood:2019:GPTP}, \cite{White:2019:GPTP}, \cite{Yuan:2019:GPTP}, Also known as \cite{DBLP:conf/gptp/2019}", } @Proceedings{Banzhaf:2021:GPTP, title = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8112-7", URL = "https://link.springer.com/book/9789811681127", DOI = "doi:10.1007/978-981-16-8113-4", size = "XVIII + 212", abstract = "Note GPTP 2020 not held due to corvid pandemic Contents \cite{Bayer:2021:GPTP}, \cite{Dolson:2021:GPTP}, \cite{Fleck:2021:GPTP}, \cite{Fonseca:2021:GPTP}, \cite{Guadalupe-Hernandez:2021:GPTP}, \cite{Korns:2021:GPTP}, \cite{Kotanchek:2021:GPTP}, \cite{Langdon:2021:GPTP}, \cite{Miller:2021:GPTP}, \cite{Saini:2021:GPTP}, \cite{Sloss:2021:GPTP}, Index A Action program, 2 multi-action program, 6 Activity dependence, 166 Ascension, 203 Automated program repair, 46 B Bees algorithm, 117 Benchmarking, 8, 84 C Cache, 52, 161 Cambrian explosion, 199 Causality, 71 Classification, 166 Co-evolution, 202 Competition, 89, 114, 205 Context-free grammar, 48 Convergence phenotypic, 150, 199 Crossover asymmetry of GP subtree crossover, 151 fatherless crossover, 158 unbiased subtree crossover, 150 D Data balancing, 133, 141 Deep learning, 2, 109, 165 with genetic programming, 109 Diagnostics exploration diagnostics, 104 selection scheme diagnostics, 104 Discriminant analysis, 113 Diversity, 2, 53, 63, 88, 139, 199 phenotypic, 64 phenotypic diversity, 89 phylogenetic, 64 phylogenetic diversity, 84 E Eco-EA, 66 Efficiency, 84, 114, 129, 155, 203 Ensembles, 133, 138, 139 Exploration diagnostic, 67 Exponential growth, 206 F Feedback loop, 71 Fitness predicting evaluation time of, 160 Fitness sharing, 66 G General artificial intelligence, 165 Genetic learning, 182 Genetic programming BalancedGP, 133, 137 grammar-based vectorial GP, 22 networked runs genetic programming, 109 OrdinalGP, 134, 137 PushGP, 52, 102, 190 template-constrained genetic program- ming, 45, 109 vectorial GP, 22 Grammar-guided, 22 Graph, 28, 111, 183 Growing neural networks, 111, 168 H High performance, 143, 195 Homeostatis, 172 Horizontal gene transfer, 203 I Inefficient threads avoiding, 143 causes, 143 measurement, 143 prediction, 143 Information loss, 33, 40 Inplace crossover, 143 shuffle, 143 speedup, 143 Intellectual property, 202 L Lexicase selection, 65, 66, 83, 191 cohort lexicase selection, 83 down-sampled lexicase selection, 83 epsilon lexicase selection, 83 novelty lexicase selection, 83 Linear genetic programming, 3, 7, 18, 69, 184 Liquid types, 50, 51 M Memory bandwidth, 143 Memory use minimising, 143 Metrics, 70 Mitochondria, 203 Modular, 167, 194 Modularity, 2, 7, 69, 181, 194 Moore Law, 197, 206 N Novelty, 90, 199 P Panmictic, 146, 150 Parent selection, 65, 83, 191 Pareto tournament, 131 Partially observable, 1 Population diversity, 2, 66, 97, 199 Population initialization, 2, 5, 12, 55, 90, 174 Predicting success based on diversity, 63 Program dendrite program, 168 evolving modular program, 182 neuron program, 176 program graph, 2, 183 program representation, 46 program synthesis, 47, 52, 84 program synthesis benchmark suite, 190 programming languages, 48 Program synthesis, 47, 84, 190 program synthesis benchmark suite, 190 R Rampant mutation, 2 Reinforcement learning, 2, 17, 176 Resilience, 203, 207 S Selection offspring selection, 34 selection pressure, 34, 157, 161 Semantic constraints, 48 Semiconductor industry, 197 SMT solvers, 48, 58 Social evolution, 203, 205 Strongly-typed, 25 Sustainability, 202 Symbolic regression, 24, 30, 87, 88, 115, 116 T Tags, 183 Tangled program graphs, 2, 183 Team, 3, 183 Tournament selection, 65, 85, 90, 91, 103, 130, 143, 145, 150, 161 Tree-based GP, 26, 28 Tree depth, 57 Type-aware, 50", notes = "Published after the workshop in 2022", } @Proceedings{Banzhaf:2022:GPTP, title = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", address = "East Lansing, USA", month = "2-4 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", ISSN = "1932-0167", URL = "https://link.springer.com/book/9789811984594", DOI = "doi:10.1007/978-981-19-8460-0", size = "XVI + 280 pages", abstract = "published after the workshop in 2023. http://gptp-workshop.com/contributions.html Contents \cite{Affenzeller:2022:GPTP} \cite{Banzhaf:2022:GPTP.2} \cite{gold:2022:GPTP} \cite{Hu:2022:GPTP} \cite{Kotanchek:2022:GPTP} \cite{LaCava:2022:GPTP} x, \cite{Machado:2022:GPTP} \cite{Moore:2022:GPTP} x, \cite{Olague:2022:GPTP} \cite{O'Reilly:2022:GPTP} x, \cite{Spector:2022:GPTP} x, \cite{Urbanowicz:2022:GPTP} \cite{Wright:2022:GPTP} April 2023 added: \cite{Stepney:2022:GPTP} \cite{Worzel:2022:GPTP}", } @InProceedings{Banzhaf:2022:GPTP.2, author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch", title = "Correlation versus {RMSE} Loss Functions in Symbolic Regression Tasks", booktitle = "Genetic Programming Theory and Practice XIX", year = "2022", editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf", series = "Genetic and Evolutionary Computation", pages = "31--55", address = "Ann Arbor, USA", month = jun # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-19-8459-4", DOI = "doi:10.1007/978-981-19-8460-0_2", abstract = "The use of correlation as a fitness function is explored in symbolic regression tasks and its performance is compared against a more typical RMSE fitness function. Using correlation with an alignment step to conclude the evolution led to significant performance gains over RMSE as a fitness function. Employing correlation as a fitness function led to solutions being found in fewer generations compared to RMSE. We also found that fewer data points were needed in a training set to discover correct equations. The Feynman Symbolic Regression Benchmark as well as several other old and recent GP benchmark problems were used to evaluate performance.", notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023", } @InProceedings{Banzhaf:2023:GPTP, author = "Wolfgang Banzhaf and Ting Hu and Gabriela Ochoa", title = "How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions", old_title = "Neutrality", booktitle = "Genetic Programming Theory and Practice XX", year = "2023", editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu", series = "Genetic and Evolutionary Computation", pages = "65--86", address = "Michigan State University, USA", month = jun # " 1-3", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-99-8412-1", DOI = "doi:10.1007/978-981-99-8413-8_4", abstract = "For various evolutionary systems it was found that the abundance of phenotypes in a search space, defined as the size of their respective neutral networks, is key to understanding the trajectory an evolutionary process takes from an initial to a target solution. we use a Linear Genetic Programming system to demonstrate that the abundance of phenotypes is determined by the combinatorics offered in its neutral components. This translates into the size of the neutral space available to a phenotype and also can explain the beautiful and rather curious observation that the abundance of phenotypes is dependent on their complexity in a negative exponential fashion.", notes = " Part of \cite{Hu:2023:GPTP} published after the workshop in 2024", } @Misc{DBLP:journals/corr/abs-2402-08011, author = "Wolfgang Banzhaf and Illya Bakurov", title = "On The Nature Of The Phenotype In Tree Genetic Programming", howpublished = "arXiv", volume = "abs/2402.08011", year = "2024", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.48550/arXiv.2402.08011", DOI = "doi:10.48550/ARXIV.2402.08011", eprinttype = "arXiv", eprint = "2402.08011", timestamp = "Mon, 19 Feb 2024 00:00:00 +0100", biburl = "https://dblp.org/rec/journals/corr/abs-2402-08011.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Banzhaf:2024:GPEM, author = "Wolfgang Banzhaf", title = "``The physics of evolution'' by Michael W. Roth, {CRC} press, 2023", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", number = "2", pages = "Article no 16", month = dec, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dK8KO", DOI = "doi:10.1007/s10710-024-09489-z", size = "3 pages", notes = "Koza Chair in Genetic Programming, Michigan State University, East Lansing, Michigan, USA", } @InProceedings{banzhaf:2024:GECCO, author = "Wolfgang Banzhaf and Illya Bakurov", title = "On the Nature of the Phenotype in Tree Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "868--877", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genotype-phenotype map, simplication, neutrality, explainability, symbolic regression", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654129", size = "10 pages", abstract = "In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five real-world datasets. We show that TGP exhibits the same behavior that we can observe in other GP representations: At the genotypic level trees show frequently unchecked growth with seemingly ineffective code, but on the phenotypic level, much smaller trees can be observed. To generate phenotypes, we provide a unique technique for removing semantically ineffective code from GP trees. The approach extracts considerably simpler phenotypes while not being limited to local operations in the genotype. We generalize this transformation based on a problem-independent parameter that enables a further simplification of the exact phenotype by coarse-graining to produce approximate phenotypes. The concept of these phenotypes (exact and approximate) allows us to clarify what evolved solutions truly predict, making GP models considered at the phenotypic level much better interpretable.", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{banzhaf:2024:GECCOcomp, author = "Wolfgang Banzhaf and Ting Hu", title = "Linear Genetic Programming", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Mengjie Zhang and Emma Hart", pages = "759--771", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Tutorial", keywords = "genetic algorithms, genetic programming", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3648422", size = "13 pages", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{bao:2023:AILA, author = "Hailu Bao and Changsheng Zhang and Chen Zhang and Bin Zhang", title = "An Improved Genetic Programming Based Factor Construction for Stock Price Prediction", booktitle = "Third International Conference on Artificial Intelligence Logic and Applications, AILA 2023", year = "2023", editor = "Songmao Zhang and Yonggang Zhang", volume = "1917", series = "CCIS", pages = "227--240", address = "Changchun, China", month = aug # " 5-6", organisation = "Chinese Association for Artificial Intelligence", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-99-7869-4_18", DOI = "doi:10.1007/978-981-99-7869-4_18", abstract = "In the process of stock price forecasting, there are the following problems: how to find the more effective factors for stock price forecasting, and how to calculate the weight of the constructed stock correlation factor sets. To solve the above problems, this paper proposes a method of factor construction in the field of stock price prediction based on genetic programming. The method can automatically construct the factor by reading the original data set of the stock, and calculate the weight of each factor. In addition, this paper also proposes a new crossover operator, which can dynamically adjust the selection of crossover nodes by using the information in the execution process of genetic programming algorithm, so as to improve the quality of the constructed factor set. A lot of experiments have been carried out with this method. The results show that the factors constructed by this method can improve the accuracy of the stock price prediction algorithm in most cases.", notes = "Proceedings published as Artificial Intelligence Logic and Applications Software College, Northeastern University, Shenyang, 110169, China", } @InProceedings{Bao:2009:ICNC, author = "Yun Bao and Erbo Zhao and Xiaocong Gan and Dan Luo and Zhangang Han", title = "A Review on Cutting-Edge Techniques in Evolutionary Algorithms", booktitle = "Fifth International Conference on Natural Computation, 2009. ICNC '09", year = "2009", month = aug, volume = "5", pages = "347--351", keywords = "genetic algorithms, genetic programming, EA performance improvements, convergence speed, cutting-edge techniques, evolutionary algorithms, nuclear power plant, evolutionary computation", DOI = "doi:10.1109/ICNC.2009.459", abstract = "There are vast amount researches in Evolutionary Algorithms (EA). We need an overview of the current state of EA research every few years. This paper reviews some of the interesting researches at the current state in both theory and application of EA. Works in EA performance improvements are introduced in the sense of balancing between convergence speed and diversity in the population. The combination of EA with other methods is highlighted as a prospective area that may give fertility results. Some smart applications are reviewed in this paper, for example, application in nuclear power plant. The authors point out some research highlights and drawbacks in the conclusion. Future research suggestions are also given.", notes = "Also known as \cite{5364257}", } @PhdThesis{ceg_baptist_20050418, author = "Martin Josephus Baptist", title = "Modelling floodplain biogeomorphology", school = "Technische Universiteit Delft", year = "2005", address = "Holland", month = "18 " # apr, publisher = "Delft University Press", ISBN = "90-407-2582-9", keywords = "genetic algorithms, genetic programming, biogeomorphology, flood management, hydraulic modelling, nature management, hydraulic roughness, river morphology, bed shear stress, floodplain vegetation", URL = "https://repository.tudelft.nl/islandora/object/uuid%3Ab2739720-e2f6-40e2-b55f-1560f434cbee", URL = "http://repository.tudelft.nl/assets/uuid...e2f6.../ceg_baptist_20050418.pdf", size = "213 pages", abstract = "There is an increasing awareness that rivers need more room in order to safeguard flood safety under climate change conditions. Contemporary river management is creating room in the floodplains and allowing, within certain bounds, natural processes of sedimentation and erosion. One of the aims is to restore dynamic conditions, so as to get a sustainable and more diverse river ecosystem that can cope with floods. This new approach requires understanding of the interaction between the biotic and abiotic components of river systems. More specifically, it requires a better understanding of the interaction between flora and fauna and geomorphological factors. This is the object of investigation of the interdiscipline of biogeomorphology. Modelling biogeomorphological processes in river floodplains is the topic of this thesis. To reduce flood risks in the Netherlands, measures to increase the flood conveyance capacity of the Rhine River will be implemented. However, it is expected that floodplain sedimentation and softwood forest development in rehabilitated floodplains will gradually reduce the conveyance capacity and the biodiversity. Moreover, in regulated rivers, such as the Rhine River, erosion and sedimentation processes caused by channel migration, which periodically interrupt vegetation succession, cannot be allowed. Therefore, a floodplain management strategy was proposed that would meet both flood protection and nature rehabilitation objectives. This strategy, 'Cyclic Floodplain Rejuvenation (CFR)', aims at mimicking the effects of channel migration by removal of softwood forests, by lowering floodplains or by (re)constructing secondary channels. In chapter 2, the effects of CFR measures on reducing flood levels and enhancing biodiversity along the Waal River were assessed. A one-dimensional hydraulic modelling system, SOBEK, was applied together with rule-based models for floodplain vegetation succession and floodplain sedimentation. The model simulations demonstrated that the flood management strategy of Cyclic Floodplain Rejuvenation is able to sustain safe flood levels in the Waal River. Rejuvenation is then needed every 25 to 35 years on average, each time in an area of about 15percent of the total floodplain area. The rejuvenation strategy led to a diverse floodplain vegetation distribution that largely complies to the historical reference for the Waal River. Cyclic Floodplain Rejuvenation may be the appropriate answer to find symbiosis between flood protection and nature rehabilitation in highly regulated rivers. ...", notes = "In English. Supervisor H.J. de Vriend", } @Article{Baptist:2007:JHR, author = "M. J. Baptist and Vladan Babovic and J. {Rodriguez Uthurburu} and M. Keijzer and R. E. Uittenbogaard and A. Mynett and A. Verwey", title = "On inducing equations for vegetation resistance", journal = "Journal of Hydraulic Research", year = "2007", volume = "45", number = "4", pages = "435--450", keywords = "genetic algorithms, genetic programming, vegetation, roughness, resistance, knowledge discovery", ISSN = "0022-1686", DOI = "doi:10.1080/00221686.2007.9521778", size = "16 pages", abstract = "The paper describes the process of induction of equations for the description of vegetation-induced roughness from several angles. Firstly, it describes two approaches for obtaining theoretically well-founded analytical expressions for vegetation resistance. The first of the two is based on simplified assumptions for the vertical flow profile through and over vegetation, whereas the second is based on an analytical solution to the momentum balance for flow through and over vegetation. In addition to analytical expressions the paper also outlines a numerical 1-DV k-e turbulence model which includes several important features related to the influence plants exhibit on the flow. Last but not least, the paper presents a novel way of applying genetic programming to the results of the 1-DV model, in order to obtain an expression for roughness based on synthetic data. The resulting expressions are evaluated and compared with an independent data set of flume experiments", } @Article{BarabasiEtAl01, author = "Albert-Laszlo Barabasi and Vincent W. Freeh and Hawoong Jeong and Jay B. Brockman", title = "Parasitic Computing", journal = "Nature", volume = "412", year = "2001", pages = "894--897", month = "30 " # aug, keywords = "16-SAT", URL = "http://www.nd.edu/~alb/Publication06/082%20Parasitic%20computing/Parasitic%20computing.pdf", DOI = "doi:10.1038/35091039", size = "3 pages", abstract = "Reliable communication on the Internet is guaranteed by a standard set of protocols, used by all computers. Here we show that these protocols can be exploited to compute with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node. In this model, which we call 'parasitic computing', one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard communication. Consequently, the target computers are unaware that they have performed computation for the benefit of a commanding node. As experimental evidence of the principle of parasitic computing, we harness the power of several web servers across the globe, which unknown to them work together to solve an NP complete problem", notes = "fig1 p895 Segement (TCP packet) dropped due to invalid checksum One property of the TCP checksum function is that it forms a sufficient logical basis for implementing any Boolean logic function, and by extension, any arithmetic operation. 65536 32 bit messages sent to http servers on three continents Cited by \cite{arXiv:cs/0701115v1}", } @InProceedings{baradavka03, author = "Igor Baradavka and Tatiana Kalganova", title = "Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional Incremental Evolution", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "276--285", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolvable hardware: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_25", abstract = "Bidirectional incremental evolution (BIE) has been proposed as a technique to overcome the ``stalling'' effect in evolvable hardware applications. However preliminary results show perceptible dependence of performance of BIE and quality of evaluated circuit on assembling strategy applied during reverse stage of incremental evolution. The purpose of this paper is to develop assembling strategy that will assist BIE to produce relatively optimal solution with minimal computational effort (e.g. the minimal number of generations).", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @Article{Barakova:2015:ieeeMMS, author = "Emilia I. Barakova and Roman Gorbunov and Matthias Rauterberg", journal = "IEEE Transactions on Human-Machine Systems", title = "Automatic Interpretation of Affective Facial Expressions in the Context of Interpersonal Interaction", year = "2015", volume = "45", number = "4", pages = "409--418", abstract = "This paper proposes a method for interpretation of the emotions detected in facial expressions in the context of the events that cause them. The method was developed to analyse the video recordings of facial expressions depicted during a collaborative game played as a part of the Mars-500 experiment. In this experiment, six astronauts were isolated for 520 days in a space station to simulate a flight to Mars. Seven time-dependent components of facial expressions were extracted from the video recordings of the experiment. To interpret these dynamic components, we proposed a mathematical model of emotional events. Genetic programming was used to find the locations, types, and intensities of the emotional events as well as the way the recorded facial expressions represented reactions to them. By classification of different statistical properties of the data, we found that there are significant relations between the facial expressions of different crew members and a memory effect between the collective emotional states of the crew members. The model of emotional events was validated on previously unseen video recordings of the astronauts. We demonstrated that both genetic search and optimisation of the parameters improve the accuracy of the proposed model. This method is a step toward automating the analysis of affective expressions in terms of the cognitive appraisal theory of emotion, which relies on the dependence of the expressed emotion on the causing event.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/THMS.2015.2419259", ISSN = "2168-2291", month = aug, notes = "Also known as \cite{7105880}", } @Article{Baral:2017:SSI, author = "Ashok Kumar Baral and Yoed Tsur", title = "Impedance spectroscopy of Gd-doped ceria analyzed by genetic programming (ISGP) method", journal = "Solid State Ionics", volume = "304", pages = "145--149", year = "2017", ISSN = "0167-2738", DOI = "doi:10.1016/j.ssi.2017.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0167273816309419", abstract = "This work presents the distribution function of relaxation time (DFRT) analysis of Gd doped ceria (GDC) and cobalt co-doped GDC prepared by precipitation method. Ionic transport properties and grain-boundary phenomena are discussed thoroughly based on the DFRT. The impedance results, especially the bulk and grain-boundary conductivities ( sigma b and sigma gb) and activation energies (Eb and Egb) obtained from the ISGP, are compared with the values obtained from the Equivalent Circuit Model. Grain boundary space charge (SC) effects discussed so far in the literature, generally do not consider the defect interaction between the oxygen vacancies and acceptor dopants in ceria and other oxide ion conductors. However, ISGP study clearly evidence the co-existence of SC effect and defect association in grain boundary regions, and both contribute to the grain boundary resistance (Rgb) at lower temperatures. The effect of sintering aid (Co) on the grain boundary activity is discussed considering both phenomena. Lower sintering temperature of the samples results in a relatively smaller grain boundary potential (Phi(0)) i.e., 0.15, 0.17 and 0.19 V at 300 degreeC in 0, 1 and 3 molpercent Co co-doped GDC, respectively.", keywords = "genetic algorithms, genetic programming, Impedance spectroscopy, DFRT, Grain boundary properties, Doped ceria", } @Article{baraldi:2021:Metals, author = "Daniele Baraldi and Stefan Holmstrom and Karl-Fredrik Nilsson and Matthias Bruchhausen and Igor Simonovski", title = "{316L(N)} Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods", journal = "Metals", year = "2021", volume = "11", number = "5", keywords = "genetic algorithms, genetic programming", ISSN = "2075-4701", URL = "https://www.mdpi.com/2075-4701/11/5/698", DOI = "doi:10.3390/met11050698", abstract = "A model that describes creep behaviour is essential in the design or life assessment of components and systems that operate at high temperatures. Using the RCC-MRx data and the LCSP (logistic creep strain prediction) model, processed design data were generated over the whole creep regime of 316L(N) steel--i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature.", notes = "also known as \cite{met11050698}", } @InProceedings{barash:1998:mGAofsalf, author = "Danny Barash and Ann Orel and V. Rao Vemuri", title = "Micro Genetic Algorithms in Finding the Optimal Frequency for Stabilizing Atoms by High-intensity Laser Fields", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "7--13", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.1378", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.1378.pdf", size = "7 pages", abstract = "The goal of this paper is to explore the power of genetic algorithms, in particular the so called micro genetic algorithms, to solve a challenging problem in experimental physics. The problem is to find an optimum frequency to stabilise atoms by high-intensity laser fields. The standard approach to search for optimal laser parameters has been by trial and error. This is the first known application of a genetic algorithm technique to model atomic stabilisation. The micro genetic algorithm worked exceptionally well for this problem as a way to automate the search in a time efficient manner. A parallel platform is used to perform the genetic search efficiently. Locating the best frequency to achieve a suppression of ionization, which is predicted to occur at high intensities, can help design a laboratory experiment and tune to that frequency in order to identify a stabilization effect. The micro genetic algorithm did successfully identify this optimum frequency. It is indeed possible to ex...", notes = "GP-98LB", } @InProceedings{DBLP:conf/ae/BarateM07, author = "Renaud Barate and Antoine Manzanera", title = "Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming", year = "2007", volume = "4926", bibdate = "2008-05-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ae/ae2007.html", booktitle = "Artificial Evolution", editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", isbn13 = "978-3-540-79304-5", pages = "25--36", series = "Lecture Notes in Computer Science", address = "Tours, France", month = oct # " 29-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-540-79305-2_3", abstract = "The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.", notes = "EA'07", } @InProceedings{Barate:2008:gecco, author = "Renaud Barate and Antoine Manzanera", title = "Generalization performance of vision based controllers for mobile robots evolved with genetic programming", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1331--1332", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1331.pdf", DOI = "doi:10.1145/1389095.1389349", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, generalisation, Obstacle avoidance, robotic simulation, vision, Poster", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389349}", } @InProceedings{DBLP:conf/sab/BarateM08, author = "Renaud Barate and Antoine Manzanera", title = "Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation", booktitle = "From Animals to Animats 10, Proceedings of the 10th International Conference on Simulation of Adaptive Behavior, SAB 2008", year = "2008", editor = "Minoru Asada and John C. T. Hallam and Jean-Arcady Meyer and Jun Tani", series = "Lecture Notes in Computer Science", volume = "5040", pages = "73--82", address = "Osaka, Japan", month = jul # " 7-12", publisher = "Springer", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-69133-4", DOI = "doi:10.1007/978-3-540-69134-1_8", abstract = "We present a system that automatically selects and parameterises a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques.", notes = "part of \cite{DBLP:conf/sab/2008}", notes = "From Animals to Animats 10", } @InProceedings{Barate:2008:ECSIS-LAB-RS, author = "Renaud Barate and Antoine Manzanera", title = "Learning Vision Algorithms for Real Mobile Robots with Genetic Programming", booktitle = "ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS '08", year = "2008", month = aug, pages = "47--52", keywords = "genetic algorithms, genetic programming, learning vision algorithms, mobile robots, obstacle avoidance algorithms, supervised learning system, control engineering computing, learning (artificial intelligence), mobile robots, robot vision", DOI = "doi:10.1109/LAB-RS.2008.20", abstract = "We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from the walls. This set of recorded video images and commands is our learning base. Genetic programming is used as a supervised learning system to generate algorithms that exhibit this corridor centering behavior. We show that the generated algorithms are efficient in the corridor that was used to build the learning base, and that they generalize to some extent when the robot is placed in a visually different corridor. More, the evolution process has produced algorithms that go past a limitation of our system, that is the lack of adequate edge extraction primitives. This is a good indication of the ability of this method to find efficient solutions for different kinds of environments.", notes = "Also known as \cite{4599426}", } @PhdThesis{Barate:thesis, author = "Renaud Barate", title = "Learning Visual Functions for a Mobile Robot with Genetic Programming", title_fr = "Apprentissage de fonctions visuelles pour un robot mobile par programmation genetique", school = "ENSTA", year = "2008", address = "32 Bd Victor, Paris 75015, France", month = nov, note = "In French", email = "Contact : Antoine.Manzanera@ensta.fr", keywords = "genetic algorithms, genetic programming, Vision, mobile robotics, obstacle avoidance", hal_id = "pastel-00004864", URL = "http://www.ensta.fr/~manzaner/Publis/these-barate.pdf", URL = "https://hal.inria.fr/tel-00811614v1", size = "149 pages", abstract = "Existing techniques used to learn artificial vision for mobile robots generally represent an image with a set of visual features that are computed with a hard-coded method. This impairs the system's adaptability to a changing visual environment. We propose a method to describe and learn vision algorithms globally, from the perceived image to the final decision. The target application is the obstacle avoidance function, which is necessary for any mobile robot. We formally describe the structure of vision-based obstacle avoidance algorithms with a grammar. Our system uses this grammar and genetic programming techniques to learn controllers adapted to a given visual context automatically. We use a simulation environment to test this approach and evaluate the performance of the evolved algorithms. We propose several techniques to speed up the evolution and improve the performance and generalization abilities of evolved controllers. In particular, we compare several methods that can be used to guide the evolution and we introduce a new one based on the imitation of a recorded behavior. Next we validate these methods on a mobile robot moving in an indoor environment. Finally, we indicate how this system can be adapted for other vision based applications and we give some hints for the online adaptation of the robot's behavior.", resume = "En robotique mobile, les techniques d'apprentissage qui utilisent la vision artificielle representent le plus souvent l'image par un ensemble de descripteurs visuels. Ces descripteurs sont extraits en utilisant une methode fixee a l'avance ce qui compromet les capacites d'adaptation du systeme a un environnement visuel changeant. Nous proposons une methode permettant de decrire et d'apprendre des algorithmes de vision de maniere globale, depuis l'image percue jusqu'a la decision finale. L'application visee est la fonction d'evitement d'obstacles, indispensable a tout robot mobile. Nous decrivons de maniere formelle la structure des algorithmes d'evitement d'obstacles bases sur la vision en utilisant une grammaire. Notre systeme utilise ensuite cette grammaire et des techniques de programmation genetique pour apprendre automatiquement des controleurs adaptes a un contexte visuel donne. Nous utilisons un environnement de simulation pour tester notre approche et mesurer les performances des algorithmes evolues. Nous proposons plusieurs techniques permettant d'accelerer l'evolution et d'ameliorer les performances et les capacites de generalisation des controleurs evolues. Nous comparons notamment plusieurs methodes d'evolution guidee et nous en presentons une nouvelle basee sur l'imitation d'un comportement enregistre. Par la suite nous validons ces methodes sur un robot reel se deplacant dans un environnement interieur. Nous indiquons finalement comment ce systeme peut etre adapte a d'autres applications utilisant la vision et nous proposons des pistes pour l'adaptation d'un comportement en temps reel sur le robot.", notes = "Francais. Universite Pierre et Marie Curie - Paris VI", } @Article{Barati:2014:PT, author = "Reza Barati and Seyed Ali Akbar {Salehi Neyshabouri} and Goodarz Ahmadi", title = "Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach", journal = "Powder Technology", volume = "257", pages = "11--19", year = "2014", ISSN = "0032-5910", DOI = "doi:10.1016/j.powtec.2014.02.045", URL = "http://www.sciencedirect.com/science/article/pii/S003259101400182X", keywords = "genetic algorithms, genetic programming, Particle motion, Sphere drag, Reynolds number", abstract = "An accurate correlation for the smooth sphere drag coefficient with wide range of applicability is a useful tool in the field of particle technology. The present study focuses on the development of high accurate drag coefficient correlations from low to very high Reynolds numbers (up to 1000000) using a multi-gene Genetic Programming (GP) procedure. A clear superiority of GP over other methods is that GP is able to determine the structure and parameters of the model, simultaneously, while the structure of the model is imposed by the user in traditional regression analysis, and only the parameters of the model are assigned. In other words, in addition to the parameters of the model, the structure of it can be optimised using GP approach. Among two new and high accurate models of the present study, one of them is acceptable for the region before drag dip, and the other is applicable for the whole range of Reynolds numbers up to 1 million including the transient region from laminar to turbulent. The performances of the developed models are examined and compared with other reported models. The results indicate that these models respectively give 16.2percent and 69.4percent better results than the best existing correlations in terms of the sum of squared of logarithmic deviations (SSLD). On the other hand, the proposed models are validated with experimental data. The validation results show that all of the estimated drag coefficients are within the bounds of 7percent of experimental values.", } @Article{Barbosa:2011:CTR, author = "Helio J. C. Barbosa and Heder S. Bernardino", title = "Genetic Programming in Civil, Structural and Environmental Engineering", journal = "Computational Technology Reviews", year = "2011", volume = "4", pages = "115--145", keywords = "genetic algorithms, genetic programming", publisher = "Civil-Comp", ISSN = "2044-8430", URL = "http://www.ctresources.info/ctr/paper.html?id=32", DOI = "doi:10.4203/ctr.4.5", abstract = "Soft computing techniques have been receiving considerable attention in recent years due to their wide applicability and low ratio of implementation effort to succeed in producing good results. In civil and environmental engineering one is often faced with the problem of inferring a mathematical model from a set of observed data. Also, in structural engineering, nature-inspired techniques, especially evolutionary algorithms, have been extensively applied, mainly to parametric design optimization problems. This paper provides an overview of the applications of one of the most versatile soft computing tools available - genetic programming - to relevant design, optimization, and identification of problems arising in civil, structural, and environmental engineering. Genetic programming (GP) is a domain-independent sub-area of the evolutionary computation field. The candidate solutions are referred to as programs, a high-level structure able to represent a large class of computational artefacts. A program can be a standard computer program, a numerical function or a classifier in symbolic form, a candidate design (such as the structure of a building), among many other possibilities. In the following sections tree-based, linear, and graph-based GPs are discussed. Moreover, grammatical evolution (GE) is presented in some detail, a relatively recent GP technique in which candidate solution's genotypes are binary encoded and space transformations create the programs employing a user-defined grammar. The most common classes of problems in civil, structural, and environmental engineering in which GP has been applied are loosely grouped here into two large classes, namely model inference and design. Both types of problems correspond to activities traditionally assigned only to humans, as they require intelligence and creativity not (yet) available elsewhere. Some representative papers from the literature were reviewed and are summarized in nine tables. The tables indicate the reference number, the GP technique adopted, the class of problem considered, a short description of the application, and the main results and conclusions of the paper. Our survey indicated a much larger number of papers dealing with model inference than with design applications in the civil, structural, and environmental engineering literature. Also, as expected, the standard tree-based genetic programming (TGP) is by far the most often adopted technique. Contrary to our expectations, gene expression programming (GEP) seems to be more popular than GE, which is probably due to the fact that GE, although more elegant and flexible, requires the specification of a problem dependent grammar by the user. Genetic programming has been proving its versatility in many different fields. Due to its great expressiveness, GP is able to evolve complex artifacts, either when inducing understandable and communicable models or generating novel designs.", notes = "Laboratorio Nacional de Computacao Cientifica, Petropolis, RJ, Brazil", } @InProceedings{barbosa:2024:GECCO, author = "Pedro Barbosa and Rosina Savisaar and Alcides Fonseca", title = "Semantically Rich Local Dataset Generation for Explainable {AI} in Genomics", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "267--276", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, XAI, evolutionary computation, instance generation, combinatorial optimization, local explainability, RNA splicing, Evolutionary Machine Learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3653990", size = "10 pages", abstract = "Black box deep learning models trained on genomic sequences excel at predicting the outcomes of different gene regulatory mechanisms. Therefore, interpreting these models may provide novel insights into the underlying biology, supporting downstream biomedical applications. Due to their complexity, interpretable surrogate models can only be built for local explanations (e.g., a single instance). However, accomplishing this requires generating a dataset in the neighborhood of the input, which must maintain syntactic similarity to the original data while introducing semantic variability in the model's predictions. This task is challenging due to the complex sequence-to-function relationship of DNA.We propose using Genetic Programming to generate datasets by evolving perturbations in sequences that contribute to their semantic diversity. Our custom, domain-guided individual representation effectively constrains syntactic similarity, and we provide two alternative fitness functions that promote diversity with no computational effort. Applied to the RNA splicing domain, our approach quickly achieves good diversity and significantly outperforms a random baseline in exploring the search space, as shown by our proof-of-concept, short RNA sequence. Furthermore, we assess its generalizability and demonstrate scalability to larger sequences, resulting in a approx30\% improvement over the baseline.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Barbosa-Diniz:2018:eniac, author = "Jessica {Barbosa Diniz} and Filipe R. Cordeiro and Pericles B. C. Miranda and Laura A. {Tomaz da Silva}", title = "A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures", booktitle = "Anais do XV Encontro Nacional de Inteligencia Artificial e Computacional", year = "2018", editor = "Denis D. Maua and Murilo Naldi", pages = "82--93", address = "Sao Paulo, Brazil", publisher_address = "Porto Alegre, RS, Brasil", month = "22-25 " # oct, publisher = "Sociedade Brasileira de Computacao", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, PonyGE2, ANN, CNN, Keras, image processing", URL = "https://sol.sbc.org.br/index.php/eniac/article/view/4406", URL = "https://sol.sbc.org.br/index.php/eniac/article/view/4406/4330.pdf", DOI = "doi:10.5753/eniac.2018.4406", size = "12 pages", abstract = "Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.", notes = "Federal Rural University of Pernambuco (UFRPE), Brazil", } @InProceedings{Barbudo:2021:SoCPaR, author = "Rafael Barbudo and Sebastian Ventura and Jose Raul Romero", title = "Grammar-Based Evolutionary Approach for Automatic Workflow Composition with Open Preprocessing Sequence", booktitle = "Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)", year = "2021", editor = "Ajith Abraham and Andries Engelbrecht and Fabio Scotti and Niketa Gandhi and Pooja Manghirmalani Mishra and Giancarlo Fortino and Virgilijus Sakalauskas and Sabri Pllana", volume = "417", series = "LNNS", pages = "647--656", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms, Grammar-Based Genetic Programming, TPOT", isbn13 = "978-3-030-96302-6", DOI = "doi:10.1007/978-3-030-96302-6_61", abstract = "Knowledge discovery is a complex process involving several phases. Some of them are repetitive and time-consuming, so they are susceptible of being automated. As an example, the large number of machine learning algorithms, together with their hyper-parameters, constitutes a vast search space to explore. In this vein, the term AutoML was coined to encompass those approaches automating such phases. The automatic workflow composition is an AutoML task that involves both the selection and the hyper-parameter optimisation of the algorithms addressing different phases, thus giving a more comprehensive assistance during the knowledge discovery process. Unlike other proposals that predetermine the structure of the preprocessing sequence, and in some cases the size of the workflow, our proposal generates workflows made up of an arbitrary number of preprocessing algorithms of any type and a classifier. This allows returning more accurate results since its avoids the oversimplification of the solution space. The optimisation is conducted by a grammar-guided genetic programming algorithm. The proposal has been validated and compared against TPOT and RECIPE generating workflows with greater predictive performance.", } @Article{BARBUDO:2021:JSS, author = "Rafael Barbudo and Aurora Ramirez and Francisco Servant and Jose Raul Romero", title = "{GEML:} A grammar-based evolutionary machine learning approach for design-pattern detection", journal = "Journal of Systems and Software", year = "2021", volume = "175", pages = "110919", month = may, keywords = "genetic algorithms, genetic programming, Design pattern detection, Reverse engineering, Machine learning, Associative classification, Grammar-guided genetic programming", ISSN = "0164-1212", URL = "https://www.sciencedirect.com/science/article/pii/S0164121221000169", DOI = "doi:10.1016/j.jss.2021.110919", abstract = "Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided", } @Article{BARBUDO:2024:asoc, author = "Rafael Barbudo and Aurora Ramirez and Jose Raul Romero", title = "Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity", journal = "Applied Soft Computing", year = "2024", volume = "153", pages = "111292", keywords = "genetic algorithms, genetic programming, AutoML, Automated workflow composition, Algorithm selection, Hyper-parameter optimisation, Grammar-guided genetic programming, Ensemble learning, Classification", ISSN = "1568-4946", URL = "https://www.sciencedirect.com/science/article/pii/S1568494624000668", DOI = "doi:10.1016/j.asoc.2024.111292", size = "14 pages", abstract = "The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow's unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow's specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workflow structures that other approaches in the literature have not considered", } @InProceedings{Barbulescu:2009:WSEAS, author = "Alina Barbulescu and Elena Bautu", title = "Meteorological time series modeling using an adaptive gene expression programming", booktitle = "Proceedings of the 10th WSEAS International Conference on Evolutionary Computation", year = "2009", editor = "Nikos E. Mastorakis and Anca Croitoru and Valentina Emilia Balas and Eduard Son and Valeri Mladenov", pages = "17--22", address = "Prague, Czech", publisher_address = "Stevens Point, Wisconsin, USA", month = "23-25 " # mar, publisher = "World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-960-474-067-3", ISSN = "1790-5109", URL = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC02.pdf", size = "6 pages", acmid = "1561917", abstract = "The precipitations are characterised by important spatial and temporal variation. Model determination for such series is of high importance for hydrological purposes (e.g. weather forecasting, agriculture, flood areas, administrative planning), even if discovering patterns in such series is a very difficult problem. The objective of the current study is to describe the use of an adaptive evolutionary technique that give promising results for the development of non-linear time series models.", notes = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC00.pdf broken http://www.wseas.org/conferences/2009/prague/ec/index.html", } @InProceedings{Barbulescu:2009:WSEASb, author = "Alina Barbulescu and Elena Bautu", title = "ARIMA Models versus Gene Expression Programming in Precipitation Modeling", booktitle = "Proceedings of the 10th WSEAS International Conference on Evolutionary Computation", year = "2009", editor = "Nikos E. Mastorakis and Anca Croitoru and Valentina Emilia Balas and Eduard Son and Valeri Mladenov", pages = "112--117", address = "Prague, Czech", publisher_address = "Stevens Point, Wisconsin, USA", month = "23-25 " # mar, publisher = "World Scientific and Engineering Academy and Society (WSEAS)", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, rain, time series modelling, statistical analysis", isbn13 = "978-960-474-067-3", ISSN = "1790-5109", URL = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC16.pdf", size = "6 pages", acmid = "1561931", abstract = "In this paper we present a case study: the application of some conceptually different approaches to the problem of identifying a model for a hydrological time series. The problem is particularly challenging, due to the size of the time series and more importantly, to the many complex phenomena that influence such time series and that reflect in the characteristics of the data. We use well established statistical methods to detect change points in the time series, and we model the subseries obtained by ARIMA, GEP and the adaptive variant and a combination of the two. The models obtained state the efficiency of combining pure statistical tests and methods with heuristic approaches.", notes = "http://www.wseas.us/e-library/conferences/2009/prague/EVOLUTIONARY/EC00.pdf broken http://www.wseas.org/conferences/2009/prague/ec/index.html", } @Article{Barbulescu20091, author = "Alina Barbulescu and Elena Bautu", title = "Alternative Models in Precipitation Analysis", journal = "Analele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica", year = "2009", volume = "XVII", number = "3", pages = "45--68", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISSN = "1844-0835", URL = "http://www.anstuocmath.ro/mathematics/pdf19/Barbulescu_Bautu.pdf", size = "24 pages", abstract = "Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon past weather related phenomena and can help to explain future events. The objective of this study is to investigate the application of some conceptually different methods to construct models for large hydrological time series. We perform a thorough statistical analysis of the time series, which covers the identification of the change points in the time series. Then, the subseries delimited by the change points are modelled with classical Box-Jenkins methods to construct ARIMA models and with a computational intelligence technique, gene expression programming, which produces non-linear symbolic models of the series. The combination of statistical techniques with computational intelligence methods, such as gene expression programming, for modelling time series, offers increased accuracy of the models obtained. This affirmation is illustrated with examples.", notes = "http://www.anstuocmath.ro/", } @Article{Barbulescu20092, author = "Alina Barbulescu and Elena Bautu", title = "Time Series Modeling Using an Adaptive Gene Expression Programming Algorithm", year = "2009", journal = "International Journal of Mathematical Models and Methods in Applied Sciences", volume = "3", pages = "85--93", number = "2", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", ISSN = "1998-0140", URL = "http://www.naun.org/journals/m3as/mmmas-134.pdf", size = "9 pages", abstract = "Meteorological time series are characterised by important spatial and temporal variation. Model determination and the prediction of evolution of such series is of high importance for different practical purposes, even if discovering evolution patterns in such series is a very difficult problem. In this article we describe an adaptive evolutionary technique and we apply it for modelling the precipitation and temperatures collected in a region of Romania. The results are promising for the analysis of such time series.", notes = "http://www.naun.org/journals/m3as/", } @Article{Barbulescu201003, author = "Alina Barbulescu and Elena Bautu", title = "Mathematical models of climate evolution in {Dobrudja}", journal = "Theoretical and Applied Climatology", year = "2010", pages = "29--44", volume = "100", issue = "1", month = mar, publisher = "Springer Wien", keywords = "genetic algorithms, genetic programming, gene expression programming, ARIMA, Earth and Environmental Science", ISSN = "0177-798X", DOI = "doi:10.1007/s00704-009-0160-7", size = "16 pages", abstract = "The understanding of processes that occur in climate change evolution and their spatial and temporal variations are of major importance in environmental sciences. Modelling these processes is the first step in the prediction of weather change. In this context, this paper presents the results of statistical investigations of monthly and annual meteorological data collected between 1961 and 2007 in Dobrudja (South-East of Romania between the Black Sea and the lower Danube River) and the models obtained using time series analysis and gene expression programming. Using two fundamentally different approaches, we provide a comprehensive analysis of temperature variability in Dobrudja, which may be significant in understanding the processes that govern climate changes in the region.", affiliation = "Ovidius University of Constanta Faculty of Mathematics and Informatics Constanta Romania", } @Article{Barclay:2015:Procedia, author = "Jack Barclay and Vimal Dhokia and Aydin Nassehi", title = "Generating Milling Tool Paths for Prismatic Parts Using Genetic Programming", journal = "Procedia CIRP", volume = "33", pages = "490--495", year = "2015", note = "9th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 14", ISSN = "2212-8271", DOI = "doi:10.1016/j.procir.2015.06.060", URL = "http://www.sciencedirect.com/science/article/pii/S2212827115007039", abstract = "The automatic generation of milling tool paths traditionally relies on applying complex tool path generation algorithms to a geometric model of the desired part. For parts with unusual geometries or intricate intersections between sculpted surfaces, manual intervention is often required when normal tool path generation methods fail to produce efficient tool paths. In this paper, a simplified model of the machining process is used to create a domain-specific language that enables tool paths to be generated and optimised through an evolutionary process - formulated, in this case, as a genetic programming system. The driving force behind the optimisation is a fitness function that promotes tool paths whose result matches the desired part geometry and favours those that reach their goal in fewer steps. Consequently, the system is not reliant on tool path generation algorithms, but instead requires a description of the desired characteristics of a good solution, which can then be used to measure and evaluate the relative performance of the candidate solutions that are generated. The performance of the system is less sensitive to different geometries of the desired part and doesn't require any additional rules to deal with changes to the initial stock (e.g. when rest roughing). The method is initially demonstrated on a number of simple test components and the genetic programming process is shown to positively influence the outcome. Further tests and extensions to the work are presented.", keywords = "genetic algorithms, genetic programming, Computer numerical control (CNC), Milling", notes = "Edited by Roberto Teti", } @Article{BARDHAN:2021:EG, author = "Abidhan Bardhan and Candan Gokceoglu and Avijit Burman and Pijush Samui and Panagiotis G. Asteris", title = "Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions", journal = "Engineering Geology", volume = "291", pages = "106239", year = "2021", ISSN = "0013-7952", DOI = "doi:10.1016/j.enggeo.2021.106239", URL = "https://www.sciencedirect.com/science/article/pii/S0013795221002507", keywords = "genetic algorithms, genetic programming, Soaked CBR, Machine learning, MARS, GPR, Sub-grade design", abstract = "California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of four efficient soft computing techniques, namely multivariate adaptive regression splines with piecewise linear models (MARS-L), multivariate adaptive regression splines with piecewise cubic models (MARS-C), Gaussian process regression, and genetic programming. For this purpose, a wide range of experimental results of soaked CBR was collected from an ongoing railway project of Indian Railways. Three explicit expressions are proposed to estimate the CBR of soils in soaked conditions. Separate laboratory experiments were performed to evaluate the generalization capabilities of the developed models. Furthermore, simulated datasets were used to validate the feasibility of the best-performing model. Experimental results reveal that the proposed MARS-L model attained the most accurate prediction (R2 = 0.9686 and RMSE = 0.0359 against separate laboratory experiments) in predicting the soaked CBR at all stages. Based on the accuracies attained, the proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects", } @Article{BARDHAN:2021:ASC, author = "Abidhan Bardhan and Pijush Samui and Kuntal Ghosh and Amir H. Gandomi and Siddhartha Bhattacharyya", title = "{ELM-based} adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions", journal = "Applied Soft Computing", volume = "110", pages = "107595", year = "2021", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2021.107595", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621005160", keywords = "genetic algorithms, genetic programming, Swarm intelligence, Soft computing, CBR, DFC, Indian Railways, Particle swarm optimization", abstract = "This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering", } @Article{BARDHAN:2022:TG, author = "Abidhan Bardhan and Anasua GuhaRay and Shubham Gupta and Biswajeet Pradhan and Candan Gokceoglu", title = "A novel integrated approach of {ELM} and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor", journal = "Transportation Geotechnics", year = "2022", volume = "32", pages = "100678", month = jan, keywords = "genetic algorithms, genetic programming, Subgrade layer design, Railway embankment, Soft computing, Consolidation parameter, Meta-heuristic optimization, Indian Railways, Swarm intelligence", ISSN = "2214-3912", URL = "https://www.sciencedirect.com/science/article/pii/S2214391221001689", DOI = "doi:10.1016/j.trgeo.2021.100678", size = "21 pages", abstract = "This study proposes a high-performance machine learning model to sidestep the time of conducting actual laboratory tests of soil compression index (Cc), one of the important criteria for determining the settlement of subgrade layers of roadways, railways, and airport runways. The suggested method combines the modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) in a novel way. In this study, Gaussian mutation with an exploratory search mechanism was incorporated to construct the MEO and used to enhance the performance of conventional ELM by optimizing its learning parameters. PCA (Principal component analysis)-based results exhibit that the developed ELM-MEO attained the most precise prediction with R2 = 0.9746, MAE = 0.0184, and RMSE = 0.0284 in training, and R2 = 0.9599, MAE = 0.0232, and RMSE = 0.0357 in the testing phase. The results showed that the proposed ELM-MEO model outperformed the other developed models, confirming the ELM-MEO model's superiority over the other models, such as random forest, gradient boosting machine, genetic programming, including the ELM and artificial neural network (ANN)-based models optimized with equilibrium optimizer, particle swarm optimization, Harris hawks optimization, slime mould algorithm, and marine predators algorithm. Based on the experimental results, the proposed ELM-MEO can be used as a promising alternative to predict soil Cc in civil engineering projects, including rail and road projects", notes = "'Fig. 1. A typical cross-section of ballasted railway embankment showing different layers.' 'Table 1 Details of previous studies on soil Cc prediction.' Gujarat, India.", } @Article{BARDHAN:2024:apm, author = "Abidhan Bardhan", title = "Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming", journal = "Applied Mathematical Modelling", volume = "125", pages = "687--720", year = "2024", ISSN = "0307-904X", DOI = "doi:10.1016/j.apm.2023.08.009", URL = "https://www.sciencedirect.com/science/article/pii/S0307904X2300358X", keywords = "genetic algorithms, genetic programming, Railway embankment, Reliability analysis, Bearing capacity, Heavy-haul freight corridor, Slope/W modelling, Artificial intelligence", abstract = "This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently", } @Article{Bardool:2016:JML, author = "Roghayeh Bardool and Jafar Javanmardi and Aliakbar Roosta and Amir H. Mohammadi", title = "Phase stability conditions of clathrate hydrates for methane + aqueous solution of water soluble organic promoter system: Modeling using a thermodynamic framework", journal = "Journal of Molecular Liquids", volume = "224, Part B", pages = "1117--1123", year = "2016", ISSN = "0167-7322", DOI = "doi:10.1016/j.molliq.2016.09.084", URL = "http://www.sciencedirect.com/science/article/pii/S016773221630335X", abstract = "A thermodynamic model is presented for predicting the phase stability conditions of clathrate hydrates for methane + water-soluble organic promoter aqueous solution. A new equation is then proposed to estimate the enthalpy of hydrate dissociation for methane + aqueous solution of water-soluble organic promoter using Genetic Programming (GP) and Teaching-Learning-Based Optimization (TLBO) evolutionary algorithm. The model reliably predicts the hydrate dissociation conditions for methane + aqueous solutions of tetrahydrofuran, 1,3-dioxolane, 1,4-dioxane and acetone. The van Laar model is used to calculate the activity coefficient of water in aqueous solution of water-soluble organic promoter. About 30percent of the reported experimental data were used for finding the empirical relationships to estimate the enthalpy of hydrate dissociation and the remaining 70percent was used to test the accuracy and the predictive capability of the correlation. The average absolute error for methane hydrate dissociation temperatures was found to be 0.33 K, which indicates the accuracy of the model.", keywords = "genetic algorithms, genetic programming, Gas hydrate, Clathrate hydrate, Methane, Water-soluble organic promoter, Thermodynamic model, Correlation", } @Article{Bardsiri:2015:IJBRA, title = "Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm", author = "Mahshid Khatibi Bardsiri and Mahdi Eftekhari and Reza Mousavi", journal = "Int. J. of Bioinformatics Research and Applications", publisher = "Inderscience Publishers", year = "2015", month = mar # "~17", volume = "11", number = "2", pages = "171--186", keywords = "genetic algorithms, genetic programming, multi-gene genetic programming, protein fold recognition, bioinformatics, weighted voting, classifiers, classification accuracy", ISSN = "1744-5493", bibsource = "OAI-PMH server at www.inderscience.com", URL = "http://www.inderscience.com/link.php?id=68092", DOI = "doi:10.1504/IJBRA.2015.068092", abstract = "In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.", notes = "PMID: 25786796 [PubMed - in process]", } @Article{Segota-Sandi:2023:Industry4.0, author = "{Baressi Segota} Sandi and Mrzljak Vedran and Prpic-Orsic Jasna and Zlatan Car", title = "Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method", journal = "Industry 4.0", year = "2023", volume = "8", number = "1", pages = "21--24", note = "Dominant technologies in", keywords = "genetic algorithms, genetic programming, friction torque prediction, industrial robotic manipulator, friction, machine learning, symbolic regression", ISSN = "2534-8582", URL = "https://stumejournals.com/journals/i4/2023/1/21.full.pdf", size = "4 pages", abstract = "The goal of the paper is estimating the normalized friction torque of a joint in an industrial robotic manipulator. For this purpose a source data, given as a figure, is digitized using a tool WebPlotDigitizer in order to obtain numeric data. The numeric data is the used within the machine learning algorithm genetic programming (GP), which performs the symbolic regression in order to obtain the equation that regresses the dataset in question. The obtained model shows a coefficient of determination equal to 0.87, which indicates that the model in question may be used for the wide approximation of the normalized friction torque using the torque load, operating temperature and joint velocity as inputs.", notes = "Published by Scientific Technical Union of Mechanical Engineering 'industry 4.0'. 108, RakovskiStr., 1000 Sofia, Bulgaria. office@stumejournals.com Faculty of Engineering, University of Rijeka, Croatia", } @Article{barge:2016:Water, author = "Jonathan T. Barge and Hatim O. Sharif", title = "An Ensemble Empirical Mode Decomposition, {Self-Organizing} Map, and Linear Genetic Programming Approach for Forecasting River Streamflow", journal = "Water", year = "2016", volume = "8", number = "6", keywords = "genetic algorithms, genetic programming", ISSN = "2073-4441", URL = "https://www.mdpi.com/2073-4441/8/6/247", DOI = "doi:10.3390/w8060247", abstract = "This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organising Map (SOM) in modelling the rainfall-runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was used to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Using EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalised mean biased error (NMBE) of -2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Using the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of using EEMD and an SOM in conjunction with LGP for streamflow forecasting.", notes = "also known as \cite{w8060247}", } @InProceedings{DBLP:conf/seal/BarileCT08, author = "Perry Barile and Victor Ciesielski and Karen Trist", title = "Non-photorealistic Rendering Using Genetic Programming", booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)", year = "2008", editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi", volume = "5361", series = "Lecture Notes in Computer Science", pages = "299--308", address = "Melbourne, Australia", month = dec # " 7-10", publisher = "Springer", keywords = "genetic algorithms, genetic programming, non-photorealistic rendering, evolutionary computation", isbn13 = "978-3-540-89693-7", DOI = "doi:10.1007/978-3-540-89694-4_31", abstract = "We take a novel approach to Non-Photorealistic Rendering by adapting genetic programming in combination with computer graphics drawing techniques. As a GP tree is evaluated, upon encountering certain nodes referred to as Draw nodes, information contained within such nodes are sent to one of three virtual canvasses and a mark is deposited on the canvas. For two of the canvasses the user is able to define custom brushes to be applied to the canvas. Drawing functions are supplied with little localised information regarding the target image. Based on this local data, the drawing functions are enabled to apply contextualized information to the canvas. The obtained results include a Shroud of Turin effect, a Decal effect and a Starburst effect.", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{DBLP:conf/gecco/BarileCBT09, author = "Perry Barile and Victor Ciesielski and Marsha Berry and Karen Trist", title = "Animated drawings rendered by genetic programming", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "939--946", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570030", abstract = "We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2 and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of creative opportunities for artists. Artists have control over choice of target and the various stroke parameters.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @InProceedings{Barlow:2013:CEC, article_id = "1360", author = "Brendan Barlow and Andy Song", title = "Towards Scene Text Recognition with Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1310--1317", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557716", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @MastersThesis{barlow2004-thesis, author = "Gregory J. Barlow", title = "Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming", school = "North Carolina State University", year = "2004", address = "Raleigh, NC, USA", month = mar, keywords = "genetic algorithms, genetic programming, mobile robotics, evolutionary robotics, multi-objective optimization, incremental evolution, unmanned aerial vehicles", URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/thesis/barlow2004-thesis/barlow2004-thesis.pdf", size = "181 pages", abstract = "Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution. In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications. Four behavioral fitness functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy source, to navigate the UAV to that source efficiently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies. All computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types. Both the direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the success rate of incremental evolution over direct evolution. The final incremental evolution experiment on the most complex radar investigated in this research evolved controllers that were able to handle all of the radar types. Evolved UAV controllers were successfully transferred to a wheeled mobile robot. An acoustic array on-board the mobile robot replaced the radar sensor, and a speaker emitting a tone was used as the target. Using the evolved navigation controllers, the mobile robot moved to the speaker and circled around it. Future research will include testing the best evolved controllers by using them to fly real UAVs.", notes = "ADA460111", } @InProceedings{barlow:2004:lbp, author = "Gregory J. Barlow and Choong K. Oh and Edward Grant", title = "Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming", booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference", year = "2004", editor = "Maarten Keijzer", address = "Seattle, Washington, USA", month = "26 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/other/barlow2004-geccolbp/barlow2004-geccolbp.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP011.pdf", abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently emitting, mobile radars. The use of incremental evolution drastically increased evolution's chances of evolving a successful controller compared to direct evolution. This technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be tested by using them to fly real UAVs.", notes = "Part of \cite{keijzer:2004:GECCO:lbp}", } @InProceedings{barlow:2004:geccogsw, author = "Gregory J. Barlow", title = "Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming", booktitle = "Proceedings of the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004)", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", year = "2004", address = "Seattle, Washington, USA", month = "24-26 " # jun, keywords = "genetic algorithms, genetic programming, evolutionary robotics, multi-objective optimisation, unmanned aerial vehicles", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WGSW001.pdf", URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/conference/barlow2004-geccogsw/barlow2004-geccogsw.pdf", abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multi-objective genetic programming (GP). Four fitness functions derived from flight simulations were designed and multi-objective GP was used to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. In this study, realistic flight parameters and sensor inputs were selected to aid in the transference of evolved controllers to physical UAVs.", notes = "Winner of Best Paper at the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004). http://www-illigal.ge.uiuc.edu:8080/GECCO-2004/awards-winners.html GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{barlow2004-cis, author = "Gregory J. Barlow and Choong K. Oh and Edward Grant", title = "Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming", booktitle = "Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS)", year = "2004", pages = "688--693", address = "Singapore", month = "1-3 " # dec, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, incremental evolution, multi-objective optimisation", URL = "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2004-cis/barlow2004-cis.pdf", abstract = "Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently emitting, mobile radars. The use of incremental evolution drastically increased evolution's chances of evolving a successful controller compared to direct evolution. This technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be tested by using them to fly real UAVs.", notes = "IEEE CIS RAM 2004 http://cis-ram.nus.edu.sg/", } @InProceedings{barlow2005-icra, author = "Gregory J. Barlow and Leonardo S. Mattos and Edward Grant and Choong K. Oh", title = "Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot", booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation", year = "2005", editor = "Ruediger Dillmann", address = "Barcelona, Spain", month = "18-22 " # apr, organisation = "IEEE Robotics and Automation Society", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2005-icra/barlow2005-icra.pdf", abstract = "Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multi-objective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.", notes = "http://www.icra2005.org/ ", } @InProceedings{1144023, author = "Gregory J. Barlow and Choong K. Oh", title = "Robustness analysis of genetic programming controllers for unmanned aerial vehicles", booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation", year = "2006", editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens", volume = "1", ISBN = "1-59593-186-4", pages = "135--142", address = "Seattle, Washington, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p135.pdf", DOI = "doi:10.1145/1143997.1144023", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "8-12 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Artificial Life Evolutionary Robotics, Adaptive Behavior, autonomous vehicles, program synthesis, reliability, robustness, synthesis, transference, unmanned aerial vehicles", notes = "GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM Order Number 910060", } @InProceedings{Barlow:2008:gecco, author = "Gregory J. Barlow and Choong K. Oh and Stephen F. Smith", title = "Evolving cooperative control on sparsely distributed tasks for {UAV} teams without global communication", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "177--184", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p177.pdf", DOI = "doi:10.1145/1389095.1389125", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, evolutionary robotics, multiagent systems, multiobjective optimisation, unmanned aerial vehicles, Artificial life, adaptive behaviour, evolvable hardware", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389125} Phd thesis on classifiers with memory: http://www.ri.cmu.edu/pub_files/2011/7/barlow-dissertation.pdf Improving memory for optimization and learning in dynamic environments. Gregory Barlow doctoral dissertation, tech. report CMU-RI-TR-11-17", } @Article{Barmpalexis2011122, author = "Panagiotis Barmpalexis and Kyriakos Kachrimanis and Emanouil Georgarakis", title = "Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming", journal = "European Journal of Pharmaceutics and Biopharmaceutics", volume = "77", number = "1", pages = "122--131", year = "2011", ISSN = "0939-6411", DOI = "doi:10.1016/j.ejpb.2010.09.017", URL = "http://www.sciencedirect.com/science/article/B6T6C-51696TP-1/2/61fc7d46e9a66d451646234b5e96dedb", keywords = "genetic algorithms, genetic programming, Solid dispersions, Nimodipine, Controlled release, Effervescent floating tablets, Artificial neural networks", abstract = "The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterised by differential scanning calorimetry, powder X-ray diffraction, FT-IR spectroscopy and polarised light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC), effervescent agents (EFF) and nimodipine were optimised in relation to drug release (percent release at 60 min, and time at which the 90percent of the drug was dissolved) and floating properties (tablet's floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period. The tablets showed good floating properties and controlled release profiles, with drug release proceeding via the concomitant operation of swelling and erosion of the polymer matrix. ANNs and GP both proved to be efficient tools in the optimization of the tablet formulation, and the global optimum formulation suggested by the GP equations consisted of PEG = 9percent, PVP = 30percent, HPMC = 36percent, EFF = 11percent, nimodipine = 14percent.", } @Article{Barmpalexis201175, author = "P. Barmpalexis and K. Kachrimanis and A. Tsakonas and E. Georgarakis", title = "Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation", journal = "Chemometrics and Intelligent Laboratory Systems", volume = "107", number = "1", pages = "75--82", year = "2011", ISSN = "0169-7439", DOI = "doi:10.1016/j.chemolab.2011.01.012", broken = "http://www.sciencedirect.com/science/article/B6TFP-523CDG2-4/2/67c4e87b7f04a0e4f5f6fe07a1127ef8", keywords = "genetic algorithms, genetic programming, Artificial neural networks, Controlled release, Experimental design, Optimisation", abstract = "Symbolic regression via genetic programming (GP) was used in the optimisation of a pharmaceutical zero-order release matrix tablet, and its predictive performance was compared to that of artificial neural network (ANN) models. Two types of GP algorithms were employed: 1) standard GP, where a single population is used with a restricted or an extended function set, and 2) multi-population (island model) GP, where a finite number of populations is adopted. The amounts of four polymers, namely PEG4000, PVP K30, HPMC K100 and HPMC E50LV were selected as independent variables, while the percentage of nimodipine released in 2 and 8 h (Y2h, and Y8h), respectively, and the time at which 90% of the drug was dissolved (t90%), were selected as responses. Optimal models were selected by minimisation of the Euclidian distance between predicted and optimum release parameters. It was found that the prediction ability of GP on an external validation set was higher compared to that of the ANNs, with the multi population and standard GP combined with an extended function set, showing slightly better predictive performance. Similarity factor (f2) values confirmed GP's increased prediction performance for multi-population GP (f2 = 85.52) and standard GP using an extended function set (f2 = 84.47).", } @Article{BARMPALEXIS:2018:IJP, author = "Panagiotis Barmpalexis and Anna Karagianni and Grigorios Karasavvaides and Kyriakos Kachrimanis", title = "Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets", journal = "International Journal of Pharmaceutics", volume = "551", number = "1", pages = "166--176", year = "2018", keywords = "genetic algorithms, genetic programming, Mini-tablets, Quality by design (QbD), Particle swarm optimization ANNs, Flow properties, DoE optimization", ISSN = "0378-5173", DOI = "doi:10.1016/j.ijpharm.2018.09.026", URL = "http://www.sciencedirect.com/science/article/pii/S037851731830677X", abstract = "In the present study, the preparation of pharmaceutical mini-tablets was attempted in the framework of Quality by Design (QbD) context, by comparing traditionally used multi-linear regression (MLR), with artificially-intelligence based regression techniques (such as standard artificial neural networks (ANNs), particle swarm optimization (PSO) ANNs and genetic programming (GP)) during Design of Experiment (DoE) implementation. Specifically, the effect of diluent type and particle size fraction for three commonly used direct compression diluents (lactose, pregelatinized starch and dibasic calcium phosphate dihydrate, DCPD) blended with either hydrophilic or hydrophobic flowing aids was evaluated in terms of: a) powder blend properties (such as bulk (Y1) and tapped (Y2) density, Carr's compressibility index (Y3, CCI), Kawakita's compaction fitting parameters a (Y4) and 1/b (Y5)), and b) mini-tablet's properties (such as relative density (Y6), average weight (Y7) and weight variation (Y8)). Results showed better flowing properties for pregelatinized starch and improved packing properties for lactose and DPCD. MLR analysis showed high goodness of fit for the Y1, Y2, Y4, Y6 and Y8 with RMSE values of Y1a =a 0.028, Y2a =a 0.032, Y4a =a 0.019, Y6a =a 0.015 and Y8a =a 0.130; while for rest responses, high correlation was observed from both standard ANNs and GP. PSO-ANNs fitting was the only regression technique that was able to adequately fit all responses simultaneously (RMSE values of Y1a =a 0.026, Y2a =a 0.022, Y3a =a 0.025, Y4a =a 0.010, Y5a =a 0.063, Y6a =a 0.013, Y7a =a 0.064 and Y8a =a 0.104)", } @Article{barmpalexis:2018:AAPSPST, author = "Panagiotis Barmpalexis and Agni Grypioti and Georgios K. Eleftheriadis and Dimitris G. Fatouros", title = "Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming", journal = "AAPS PharmSciTech", year = "2018", volume = "19", number = "2", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1208/s12249-017-0893-z", DOI = "doi:10.1208/s12249-017-0893-z", } @Misc{Barnes:2021:GPTP, author = "Elizabeth Barnes", title = "Viewing Anthropogenic Change Through an {AI} Lens", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", address = "East Lansing, USA", month = "19-21 " # may, note = "keynote", keywords = "genetic algorithms, genetic programming", video_url = "https://mediaspace.msu.edu/media/Barnes_Keynote_GPTP_2021/1_obavvcra", notes = "Department of Atmospheric Science, Colorado State University Not part of published proceedings", } @InProceedings{Barnes:2019:GECCOcomp, author = "Kenton M. Barnes and Michael B. Gale", title = "Meta-genetic programming for static quantum circuits", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "2016--2019", address = "Prague, Czech Republic", URL = "http://wrap.warwick.ac.uk/119812/1/WRAP-meta-genetic-programming-static-quantum-circuits-Gale-2019.pdf", DOI = "doi:10.1145/3319619.3326907", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, quantum computing", size = "5 pages", abstract = "Quantum programs are difficult for humans to develop due to their complex semantics that are rooted in quantum physics. It is there-fore preferable to write specifications and then use techniques such as genetic programming (GP) to generate quantum programs in-stead. We present a new genetic programming system for quantumcircuits which can evolve solutions to the full-adder and quantumFourier transform problems in fewer generations than previouswork, despite using a general set of gates. This means that it is nolonger required to have any previous knowledge of the solutionand choose a specialised gate set based on it.", notes = "Also known as \cite{3326907} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{oai:arXiv.org:quant-ph/9907056, title = "A quantum circuit for {OR}", author = "Howard Barnum and Herbert J. Bernstein and Lee Spector", year = "1999", month = oct # "~08", abstract = "We give the first quantum circuit for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) \oplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup. The XOR algorithm is of fundamental importance in quantum computation; our OR algorithm (found with the aid of genetic programming), may represent a new quantum computational effect, also useful as a ``subroutine''.", oai = "oai:arXiv.org:quant-ph/9907056", URL = "http://arXiv.org/abs/quant-ph/9907056", URL = "http://arxiv.org/PS_cache/quant-ph/pdf/9907/9907056.pdf", howpublished = "arXiv.or", keywords = "genetic algorithms, genetic programming", size = "6 pages", notes = "Comment: 4 pages + 2 postscript figures. Version 3 restores the figures to Version 2, which changed the title, abstract, introduction and concluding paragraph, order of material, and emphasis from Version 1. No change in technical content", } @TechReport{2000-barnum-2, author = "Howard Barnum and Herbert J Bernstein and Lee Spector", title = "Quantum circuits for {OR} and {AND} of {OR}'s", year = "2000", institution = "University of Bristol", address = "UK", month = aug, keywords = "genetic algorithms, genetic programming", abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000497", URL = "http://www.cs.bris.ac.uk/Publications/Papers/1000497.pdf", pubtype = "117", abstract = "We give the first quantum circuit, derived with the aid of genetic programming, for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) \oplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup.", notes = "See also \cite{barnum:2000:qc}", size = "15 pages", } @Article{barnum:2000:qc, author = "Howard Barnum and Herbert J Bernstein and Lee Spector", title = "Quantum circuits for {OR} and {AND} of {ORs}", journal = "Journal of Physics A: Mathematical and General", year = "2000", volume = "33", number = "45", pages = "8047--8057", month = "17 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://hampshire.edu/lspector/pubs/jpa.pdf", URL = "http://hampshire.edu/lspector/pubs/jpa.ps", abstract = "We give the first quantum circuit for computing f(0) or f(1) more reliably than is classically possible with a single evaluation function. Or therefor joins XOR (ie parity) to give the full set of logical connectives (up to relabelling of inputs and outputs) for which there is a quantum speedup", notes = "reports new quantum algorithms discovered by GP, with some details on the GP processes", } @InProceedings{baron:1999:S, author = "Christophe Baron and Guy Gouarderes", title = "Systemions to model alternative issues in problem solving", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "31--37", address = "Orlando, Florida, USA", month = "13 " # jul, notes = "GECCO-99LB", } @Article{Barone:2023:MLST, author = "Francesco Pio Barone and Daniele Dell'Aquila and Marco Russo", title = "A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals", journal = "Machine Learning: Science and Technology", year = "2023", volume = "4", number = "4", pages = "045054", month = dec, keywords = "genetic algorithms, genetic programming, BP, gravitational-wave science, analysis of noisy timeseries, fuzzy-classification of signals, speech-processing, artificial neural networks, ANN", publisher = "IOP Publishing", ISSN = "2632-2153", URL = "https://iopscience.iop.org/article/10.1088/2632-2153/ad1200", DOI = "doi:10.1088/2632-2153/ad1200", size = "18 pages", abstract = "Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective GW detection algorithms is crucial. We propose a novel layered framework for real-time detection of GWs inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g. it identifies of low signal-to-noise-ration GW signals, against of the state-of-the-art, at a false alarm probability of 10−2), but has a much lower computational complexity (it exploits only 4 numerical features in the present implementation) and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of GW signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.", } @InProceedings{baronti:2002:gecco:lbp, title = "Enhancing Tournament Selection to Prevent Code Bloat in Genetic Programming", author = "Flavio Baronti and Antonina Starita", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "17--22", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", notes = "Late Breaking Papers, {GECCO-2002}. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @InProceedings{Barr:2014:FSE, author = "Earl T. Barr and Yuriy Brun and Premkumar Devanbu and Mark Harman and Federica Sarro", title = "The Plastic Surgery Hypothesis", booktitle = "22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014)", year = "2014", editor = "Alessandro Orso and Margaret-Anne Storey and Shing-Chi Cheung", address = "Hong Kong", month = "16-12 " # nov, publisher = "ACM", keywords = "genetic improvement, SBSE, APR, Distribution, Maintenance, and Enhancement, Reusable Software, Experimentation, Languages, Measurement, Software graftability, code reuse, empirical software engineering, mining software repositories, automated program repair", URL = "http://earlbarr.com/publications/psh.pdf", size = "12 pages", abstract = "Recent work on genetic-programming-based approaches to automatic program patching have relied on the insight that the content of new code can often be assembled out of fragments of code that already exist in the code base. This insight has been dubbed the plastic surgery hypothesis; successful, well-known automatic repair tools such as GenProg rest on this hypothesis, but it has never been validated. We formalise and validate the plastic surgery hypothesis and empirically measure the extent to which raw material for changes actually already exists in projects. In this paper, we mount a large-scale study of several large Java projects, and examine a history of 15,723 commits to determine the extent to which these commits are graftable, i.e., can be reconstituted from existing code, and find an encouraging degree of graftability, surprisingly independent of commit size and type of commit. For example, we find that changes are 43percent graftable from the exact version of the software being changed. With a view to investigating the difficulty of finding these grafts, we study the abundance of such grafts in three possible sources: the immediately previous version, prior history, and other projects. We also examine the contiguity or chunking of these grafts, and the degree to which grafts can be found in the same file. Our results are quite promising and suggest an optimistic future for automatic program patching methods that search for raw material in already extant code in the project being patched.", notes = "http://fse22.gatech.edu/", } @InProceedings{Barr:2015:ISSTA, author = "Earl T. Barr and Mark Harman and Yue Jia and Alexandru Marginean and Justyna Petke", title = "Automated Software Transplantation", booktitle = "International Symposium on Software Testing and Analysis, ISSTA 2015", year = "2015", editor = "Tao Xie and Michal Young", pages = "257--269", address = "Baltimore, Maryland, USA", month = "14-17 " # jul, organisation = "ACM Special Interest Group on Software Engineering", publisher = "ACM", note = "{ACM SIGSOFT} Distinguished Paper Award", keywords = "genetic algorithms, genetic programming, genetic improvement, Automated software transplantation, autotransplantation, TXL", isbn13 = "978-1-4503-3620-8", URL = "http://crest.cs.ucl.ac.uk/autotransplantation/", URL = "http://crest.cs.ucl.ac.uk/autotransplantation/downloads/autotransplantation.pdf", URL = "http://www.human-competitive.org/sites/default/files/barr-harman-jia-marginean-petke-text.txt", DOI = "doi:10.1145/2771783.2771796", acmid = "2771796", size = "13 pages", abstract = "Automated transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system. This paper introduces a theory, an algorithm, and a tool that achieve this. Leveraging lightweight annotation, program analysis identifies an organ (interesting behaviour to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. While we do not claim automated transplantation is now a solved problem, our results are encouraging: we report that in 12 of 15 experiments, involving 5 donors and 3 hosts (all popular real-world systems), we successfully autotransplanted new functionality and passed all regression tests. Autotransplantation is also already useful: in 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player; compare this to upgrading x264 within VLC, a task that we estimate, from VLC's version history, took human programmers an average of 20 days of elapsed, as opposed to dedicated, time", notes = "Winner 2016 HUMIES mutrans http://crest.cs.ucl.ac.uk/autotransplantation/MuScalpel.html#scripts muScalpel http://crest.cs.ucl.ac.uk/autotransplantation/downloads/muScalpel.tar.gz Pidgin Cflow SOX VLC Idct: Mytar Webserver TuxCrypt Cflow x264 cited by \cite{Lu:2018:ICSE} http://issta2015.cs.uoregon.edu/papers.php also known as \cite{Barr:2015:AST:2771783.2771796}", } @InProceedings{Barrero:2010:gecco, author = "David F. Barrero and David Camacho and Maria D. R-Moreno", title = "Confidence intervals of success rates in evolutionary computation", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "975--976", keywords = "genetic algorithms, genetic programming: Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830657", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examined in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research.One of those tools, confidence intervals (CIs), is studied.", notes = "Santa Fe trail artificial ant Also known as \cite{1830657} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{barrero:2011:EuroGP, author = "David F. Barrero and Bonifacio Casta\~no and Maria D. R-Moreno and David Camacho", title = "Statistical Distribution of Generation-to-Success in GP: Application to Model Accumulated Success Probability", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "154--165", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_14", abstract = "Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Barrero:2011:AESotAoCEiGP, title = "An Empirical Study on the Accuracy of Computational Effort in Genetic Programming", author = "David F. Barrero and Maria R-Moreno and Bonifacio Castano and David Camacho", pages = "1169--1176", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, computational effort, estimation, variability sources, computational complexity, estimation theory", DOI = "doi:10.1109/CEC.2011.5949748", abstract = "Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we try to study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @InProceedings{Barrero:2013:CEC, article_id = "1254", author = "David F. Barrero and Maria D. R-Moreno and Bonifacio Castano and David Camacho", title = "Effects of the Lack of Selective Pressure on the Expected Run-Time Distribution in Genetic Programming", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1748--1755", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557772", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @Article{Barrero:2015:GPEM, author = "David F. Barrero and Bonifacio Castano and Maria D. R-Moreno and David Camacho", title = "A study on Koza's performance measures", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "327--349", month = sep, keywords = "genetic algorithms, genetic programming, Computational effort, Performance measures, Experimental methods, Measurement error", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9238-9", size = "23 pages", abstract = "John R. Koza defined several metrics to measure the performance of an Evolutionary Algorithm that have been widely used by the Genetic Programming community. Despite the importance of these metrics, and the doubts that they have generated in many authors, their reliability has attracted little research attention, and is still not well understood. The lack of knowledge about these metrics has likely contributed to the decline in their usage in the last years. This paper is an attempt to increase the knowledge about these measures, exploring in which circumstances they are more reliable, providing some clues to improve how they are used, and eventually making their use more justifiable. Specifically, we investigate the amount of uncertainty associated with the measures, taking an analytical and empirical approach and reaching theoretical boundaries to the error. Additionally, a new method to calculate Koza's performance measures is presented. It is shown that these metrics, under common experimental configurations, have an unacceptable error, which can be arbitrary large in certain conditions.", } @InProceedings{Barresi:2014:GECCOcomp, author = "Kevin M. Barresi", title = "Evolved nonlinear predictor functions for lossless image compression", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming: Poster", pages = "129--130", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598503", DOI = "doi:10.1145/2598394.2598503", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Due to the increased quantity of digital data, especially in the form of digital images, the need for effective image compression techniques is greater than ever. The JPEG lossless mode relies on predictive coding, in which accurate predictive models are critical. This study presents an efficient method of generating predictor models for input images via genetic programming. It is shown to always produce error images with entropy equal to or lower than those produced by the JPEG lossless mode. This method is demonstrated to have practical use as a real-time asymmetric image compression algorithm due to its ability to quickly and reliably derive prediction models.", notes = "Also known as \cite{2598503} Distributed at GECCO-2014.", } @Article{Barrett:2005:TP, author = "John Barrett and Aneta Kostadinova and Juan Antonio Raga", title = "Mining parasite data using genetic programming", journal = "Trends in Parasitology", year = "2005", volume = "21", number = "5", pages = "207--209", month = may, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.pt.2005.03.007", abstract = "Genetic programming is a technique that can be used to tackle the hugely demanding data-processing problems encountered in the natural sciences. Application of genetic programming to a problem using parasites as biological tags demonstrates its potential for developing explanatory models using data that are both complex and noisy.", notes = "PMID: 15837607", } @InProceedings{barrett:2003:dmtmb, author = "S. J. Barrett", title = "Recurring Analytical Problems within Drug Discovery and Development", booktitle = "Data Mining and Text Mining for Bioinformatics: Proceedings of the European Workshop", year = "2003", editor = "Tobias Scheffer and Ulf Leser", pages = "6--7", address = "Dubrovnik, Croatia", month = "22 " # sep, organisation = "KDnet", note = "Invited talk", keywords = "genetic algorithms, genetic programming, SVM, SNP", URL = "http://www2.informatik.hu-berlin.de/~scheffer/publications/ProceedingsWS2003.pdf", abstract = "The overall processes driving pharmaceuticals discovery and development research involve many disparate kinds of problems and problem-solving at multiple levels of generality and specificity. The discovery/pre-clinical processes are also highly technology-driven and specific aspects may be more dynamic over time compared to developmental research which is conducted in a more conservatively controlled manner, conducive to regulatory requirements.", notes = "Held in Conjunction with ECML / PKDD- 2003 https://www.cs.uni-potsdam.de/ml/publications/ws03proc/", } @InProceedings{barrett:2005:WSC, author = "S. J. Barrett and W. B. Langdon", title = "Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development", booktitle = "Applications of Soft Computing: Recent Trends", year = "2006", editor = "Ashutosh Tiwari and Joshua Knowles and Erel Avineri and Keshav Dahal and Rajkumar Roy", series = "Advances in Soft Computing", volume = "36", pages = "99--110", address = "On the World Wide Web", month = "19 " # sep # " - 7 " # oct # " 2005", organisation = "World Federation of Soft Computing (WFSC), European Neural Network Society (ENNS), North American Fuzzy Information Processing Society (NAFIPS), European Society for Fuzzy Logic and Technology (EUSFLAT), and International Fuzzy Systems Association (IFSA)", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Pharmaceutical applications, Drug design, Particle swarm optimisation, Support vector machines", ISBN = "3-540-29123-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/barrett_2005_WSC.pdf", URL = "http://isxp1010c.sims.cranfield.ac.uk/Papers/paper196.pdf", URL = "https://link.springer.com/chapter/10.1007/978-3-540-36266-1_10", size = "21 pages", old_abstract = "Pharmaceutical discovery and development is a cascade of extremely complex and costly research encompassing many facets from: therapeutic target identification and bioinformatics study, candidate drug discovery and optimisation to pre-clinical organism-level evaluations and beyond to extensive clinical trials assessing effectiveness and safety of new medicines. Machine learning, in particular support vector machines SVM, particle swarm optimisation PSO and genetic programming GP, is increasingly used.", abstract = "Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. They are inherently suitable for use with noisy, high dimensional (many variables) data, as is commonly used in cheminformatic (i.e. In silico screening), bioinformatic (i.e. bio-marker studies, using DNA chip data) and other types of drug research studies. These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities.", notes = "http://www.cranfield.ac.uk/wsc10/ broken Original conference title= WSC10: 10th Online World Conference on Soft Computing in Industrial Applications http://isxp1010c.sims.cranfield.ac.uk/Presentations/presentation196.pdf broken slides (1Mbyte) Revised following conference. Published 2006. See link.springer.com for published version", } @Article{Barrett:2006:GPEM, author = "Steven J. Barrett", title = "Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems John Wiley \& Sons Ltd., Chichester, UK, Keedwell, Edward and Narayanan, Ajit, 2005, 280 p., Hardcover, ISBN 0-470-02175-6", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "3", pages = "283--284", month = oct, note = "Book review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8e5", DOI = "doi:10.1007/s10710-006-7003-4", } @InProceedings{Barriere:2008:PPSN, author = "Olivier Barriere and Evelyne Lutton and Cedric Baudrit and Mariette Sicard and Bruno Pinaud and Nathalie Perrot", title = "Modeling human expertise on a cheese ripening industrial process using GP", booktitle = "Parallel Problem Solving from Nature - PPSN X", year = "2008", editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume", volume = "5199", series = "LNCS", pages = "859--868", address = "Dortmund", month = "13-17 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-87699-5", url_fake = "http://metronum.futurs.inria.fr/html/Papers/files/pdf/Barriere_18-06-2008_INCALIN-PPSN2008-Final.pdf", DOI = "doi:10.1007/978-3-540-87700-4_85", size = "10 pages", abstract = "Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (colour, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of French Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modelling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.", notes = "GPLAB, Matlab, multi linear regression, INCALIN, Terminals: time derivatives of pH acidity, lactose and two bacteria concentrations. Gaussian random constants. Function set: arithmetics, log, exp?, Boolean ops. Fitness: parsimony, Euclidean sharing distance. tree GP. 30-40 nodes. Mutation Chi squared. 16 experiments each lasting 40 days. Missing data estimated by fitting splines. Log? distribution on floats. See also \cite{inria-00381681} PPSN X", } @TechReport{inria-00381681, title = "Modeling an agrifood industrial process using cooperative coevolution Algorithms", author = "Olivier Barriere and Evelyne Lutton and Pierre-Henri Wuillemin and Cedric Baudrit and Mariette Sicard and Bruno Pinaud and Nathalie Perrot", institution = "INRIA", year = "2009", number = "inria-00381681, version 1", address = "Parc Orsay, France", month = "6 " # may, keywords = "genetic algorithms, genetic programming, Parisian, Computer Science, Artificial Intelligence, Life Sciences/Food and Nutrition, Agrifood, Cheese ripening, Cooperative coevolution, Parisian approach, Bayesian Network", URL = "http://hal.inria.fr/inria-00381681/en/", URL = "http://hal.inria.fr/docs/00/38/16/81/PDF/RR2008.pdf", bibsource = "OAI-PMH server at oai.archives-ouvertes.fr", identifier = "HAL:inria-00381681, version 1", language = "EN", oai = "oai:hal.archives-ouvertes.fr:inria-00381681_v1", abstract = "This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focused on a specific problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: -- a deterministic modeling problem, the phase prediction problem, for which a search for a closed form tree expression has been performed using genetic programming (GP), -- a Bayesian network structure estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution techniques (also called ``Parisian'' approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important.", size = "51 pages", } @InProceedings{barro:2022:MSMASF, author = "Diana Barro and Francesca Parpinel and Claudio Pizzi", title = "Pricing Rainfall Derivatives by Genetic Programming: A Case Study", booktitle = "Mathematical and Statistical Methods for Actuarial Sciences and Finance", year = "2022", editor = "Marco Corazza and Cira Perna and Claudio Pizzi and Marilena Sibillo", pages = "64--69", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-99638-3", URL = "http://link.springer.com/chapter/10.1007/978-3-030-99638-3_11", DOI = "doi:10.1007/978-3-030-99638-3_11", abstract = "we consider a genetic programming approach to price rainfall derivatives and we test it on a case study based on data collected from a meteorological station in a city in the northeast region of Friuli Venezia Giulia (Italy), characterized by a fairly abundant rainfall.", } @InProceedings{Barros:2011:GECCOcomp, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Andre C. P. L. F. {de Carvalho} and Alex A. Freitas", title = "Towards the automatic design of decision tree induction algorithms", booktitle = "GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms", year = "2011", editor = "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "567--574", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002050", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes two different approaches for automatically generating generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the end-user.", notes = "Also known as \cite{2002050} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InProceedings{Barros:2013:GECCO, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Ricardo Cerri and Tiago S. {da Silva} and Andre C. P. L. F. {de Carvalho}", title = "A grammatical evolution approach for software effort estimation", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "1413--1420", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463546", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.", notes = "Also known as \cite{2463546} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @Article{Barros2011954, author = "Rodrigo C. Barros and Duncan D. Ruiz and Marcio P. Basgalupp", title = "Evolutionary model trees for handling continuous classes in machine learning", journal = "Information Sciences", year = "2011", volume = "181", number = "5", pages = "954--971", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Model trees, Continuous classes, Machine learning", ISSN = "0020-0255", URL = "http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe", DOI = "doi:10.1016/j.ins.2010.11.010", size = "18 pages", abstract = "Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications.", } @Article{Barros:2014:ieeeTEC, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Alex A. Freitas and Andre C. P. L. F. {de Carvalho}", title = "Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", month = dec, volume = "18", number = "6", pages = "873--892", keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2291813", size = "20 pages", abstract = "Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.", notes = "also known as \cite{6670778}", } @Article{Barros:2015:GPEM, author = "Rodrigo C. Barros and Marcio P. Basgalupp and Andre C. P. L. F. {de Carvalho}", title = "Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification", journal = "Genetic Programming and Evolvable Machines", year = "2015", volume = "16", number = "3", pages = "241--281", month = sep, keywords = "genetic algorithms, genetic programming, Hyper-heuristics, Decision trees, Fitness function, Imbalanced data", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-014-9235-z", size = "41 pages", abstract = "In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.", notes = "Author Affiliations 1. Faculdade de Informatica (FACIN), Pontificia Universidade Catolica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil 2. Instituto de Ciencia e Tecnologia (ICT), Universidade Federal de Sao Paulo (UNIFESP), Sao Josedos Campos, Brazil 3. Instituto de Ciencias Matematicas e de Computacao (ICMC), Universidade de Sao Paulo (USP), Sao Carlos, Brazil ", } @InProceedings{barry:1999:AXCSPE, author = "Alwyn Barry", title = "Aliasing in XCS and the Consecutive State Problem: 1 - Effects", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "19--26", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-317.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-317.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{barry:1999:AXCSPS, author = "Alwyn Barry", title = "Aliasing in XCS and the Consecutive State Problem: 2 - Solutions", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "27--34", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-336.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-336.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Proceedings{barry:2002:gecco:workshop, title = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution, multi-objective optimization, planning, scheduling, industrial applications, machine learning, niching, linkage learning", size = "330 pages", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @Proceedings{barry:2003:gecco:workshop, title = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution, multi-objective optimization, planning, scheduling, machine learning, representations", URL = "http://gpbib.cs.ucl.ac.uk/gecco2003wks.bib", size = "330 pages", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", } @InProceedings{Bartashevich:2018:GECCOcomp, author = "Palina Bartashevich and Illya Bakurov and Sanaz Mostaghim and Leonardo Vanneschi", title = "Evolving {PSO} algorithm design in vector fields using geometric semantic {GP}", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "262--263", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205760", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "This paper investigates the possibility of evolving new particle swarm equations representing a collective search mechanism, acting in environments with unknown external dynamics, using Geometric Semantic Genetic Programming (GSGP). The proposed method uses a novel initialization technique - the Evolutionary Demes Despeciation Algorithm (EDDA)- which allows to generate solutions of smaller size than using the traditional ramped half-and-half algorithm. We show that EDDA, using a mixture of both GP and GSGP mutation operators, allows us to evolve new search mechanisms with good generalization ability.", notes = "Also known as \cite{3205760} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Bartashevich:2018:PPSN, author = "Palina Bartashevich and Illya Bakurov and Sanaz Mostaghim and Leonardo Vanneschi", title = "{PSO}-based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming", booktitle = "15th International Conference on Parallel Problem Solving from Nature", year = "2018", editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley", volume = "11101", series = "LNCS", pages = "41--53", address = "Coimbra, Portugal", month = "8-12 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Particle swarm optimization, Vector fields, Semantics, EDDA", isbn13 = "978-3-319-99252-5", URL = "https://www.springer.com/gp/book/9783319992587", DOI = "doi:10.1007/978-3-319-99253-2_4", abstract = "In this paper, we study Particle Swarm Optimization (PSO) as a collective search mechanism for individuals (such as aerial micro-robots) which are supposed to search in environments with unknown external dynamics. In order to deal with the unknown disturbance, we present new PSO equations which are evolved using Genetic Programming (GP) with a semantically diverse starting population, seeded by the Evolutionary Demes Despeciation Algorithm (EDDA), that generalizes better than standard GP in the presence of unknown dynamics. The analysis of the evolved equations shows that with only small modifications in the velocity equation, PSO can achieve collective search behaviour while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source.", notes = "PPSN2018 http://ppsn2018.dei.uc.pt This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018", } @InProceedings{conf/isat/Bartczuk15, title = "Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects", author = "Lukasz Bartczuk", booktitle = "Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology - ISAT 2015 - Part I", year = "2015", volume = "429", editor = "Leszek Borzemski and Adam Grzech and Jerzy Swiatek and Zofia Wilimowska", series = "Advances in Intelligent Systems and Computing", pages = "125--134", publisher = "Springer", bibdate = "2017-05-21", keywords = "genetic algorithms, genetic programming, gene expression programming, nonlinear modelling, dynamic objects", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/isat/isat2015-1.html#Bartczuk15", isbn13 = "978-3-319-28553-5", DOI = "doi:10.1007/978-3-319-28555-9_11", abstract = "In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linearisation technique that allows to incorporate in modelling process the known linear model of the same or similar object or phenomenon. The usefulness of the proposed method will be shown on a practical example of the continuous stirred tank reactor modelling.", } @InProceedings{conf/icaisc/BartczukPK15, author = "Lukasz Bartczuk and Andrzej Przybyl and Petia D. Koprinkova-Hristova", title = "New Method for Non-linear Correction Modelling of Dynamic Objects with Genetic Programming", bibdate = "2015-06-08", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2015-2.html#BartczukPK15", booktitle = "Artificial Intelligence and Soft Computing - 14th International Conference, {ICAISC} 2015, Zakopane, Poland, June 14-28, 2015, Proceedings, Part {II}", publisher = "Springer", year = "2015", volume = "9120", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-19368-7", pages = "318--329", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-19369-4", DOI = "doi:10.1007/978-3-319-19369-4_29", } @InProceedings{conf/icaisc/BartczukG16, author = "Lukasz Bartczuk and Alexander I. Galushkin", title = "A New Method for Generating Nonlinear Correction Models of Dynamic Objects Based on Semantic Genetic Programming", bibdate = "2016-05-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2016-2.html#BartczukG16", booktitle = "Artificial Intelligence and Soft Computing - 15th International Conference, {ICAISC} 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part {II}", publisher = "Springer", year = "2016", volume = "9693", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-39383-4", pages = "249--261", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-39384-1", } @InProceedings{conf/icaisc/BartczukLK16, author = "Lukasz Bartczuk and Krystian Lapa and Petia D. Koprinkova-Hristova", title = "A New Method for Generating of Fuzzy Rules for the Nonlinear Modelling Based on Semantic Genetic Programming", bibdate = "2016-05-31", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2016-2.html#BartczukLK16", booktitle = "Artificial Intelligence and Soft Computing - 15th International Conference, {ICAISC} 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part {II}", publisher = "Springer", year = "2016", volume = "9693", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", isbn13 = "978-3-319-39383-4", pages = "262--278", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-39384-1", } @InProceedings{conf/icaisc/BartczukDR17, author = "Lukasz Bartczuk and Piotr Dziwinski and Vladimir G. Redko", title = "The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming", booktitle = "Artificial Intelligence and Soft Computing - 16th International Conference, {ICAISC} 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part {II}", editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada", year = "2017", volume = "10246", pages = "209--220", series = "Lecture Notes in Computer Science", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2017-06-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2017-2.html#BartczukDR17", isbn13 = "978-3-319-59059-2; 978-3-319-59060-8", DOI = "doi:10.1007/978-3-319-59060-8_20", } @Article{Bartlett:TEVC, author = "Deaglan J. Bartlett and Harry Desmond and Pedro G. Ferreira", journal = "IEEE Transactions on Evolutionary Computation", title = "Exhaustive Symbolic Regression", year = "2024", volume = "28", number = "4", pages = "950--964", month = aug, keywords = "genetic algorithms, genetic programming, Mathematical models, Complexity theory, Optimisation, Numerical models, Biological system modelling, Standards, Search problems, Symbolic regression, data analysis, minimum description length, MDL, model selection, cosmology", DOI = "doi:10.1109/TEVC.2023.3280250", ISSN = "1941-0026", size = "15 pages", abstract = "Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations-made with a given basis set of operators and up to a specified maximum complexity- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding 40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available.", notes = "Also known as \cite{10136815} Institute of Cosmology and Gravitation, University of Portsmouth, PO1 3FX Portsmouth, UK", } @InProceedings{bartlett:2023:cogsci, author = "Laura K. Bartlett and Angelo Pirrone and Noman Javed and Peter C. R. Lane and Fernand Gobet", title = "Genetic programming for developing simple cognitive models", booktitle = "Proceedings of the 45th Annual Meeting of the Cognitive Science Society", year = "2023", editor = "M. Goldwater and F. K. Anggoro and B. K. Hayes and D. C. Ong", pages = "2833--2839", address = "Sydney, Australia", month = jul # " 26-29", keywords = "genetic algorithms, genetic programming, delayed-match-to-sample,memory, psychology", URL = "http://hdl.handle.net/2299/27181", URL = "https://escholarship.org/uc/item/08x8m02w", URL = "https://researchprofiles.herts.ac.uk/files/48593960/qt08x8m02w.pdf", size = "7 pages", abstract = "Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models.", notes = "http://bit.ly/44AJvz3 VP-Z-624-1983", } @InProceedings{Bartoli:2011:EuroGP, author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De Lorenzo} and Eric Medvet", title = "GP-based Electricity Price Forecasting", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "37--48", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_4", abstract = "The electric power market is increasingly relying on competitive mechanisms taking the form of day-ahead auctions, in which buyers and sellers submit their bids in terms of prices and quantities for each hour of the next day. Methods for electricity price forecasting suitable for these contexts are crucial to the success of any bidding strategy. Such methods have thus become very important in practice, due to the economic relevance of electric power auctions. In this work we propose a novel forecasting method based on Genetic Programming. Key feature of our proposal is the handling of outliers, i.e., regions of the input space rarely seen during the learning. Since a predictor generated with Genetic Programming can hardly provide acceptable performance in these regions, we use a classifier that attempts to determine whether the system is shifting toward a difficult-to-learn region. In those cases, we replace the prediction made by Genetic Programming by a constant value determined during learning and tailored to the specific subregion expected. We evaluate the performance of our proposal against a challenging baseline representative of the state-of-the-art. The baseline analyses a real-world dataset by means of a number of different methods, each calibrated separately for each hour of the day and recalibrated every day on a progressively growing learning set. Our proposal exhibits smaller prediction error, even though we construct one single model, valid for each hour of the day and used unmodified across the entire testing set. We believe that our results are highly promising and may open a broad range of novel solutions.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Bartoli:2012:GECCOcomp, author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De Lorenzo} and Marco Mauri and Eric Medvet and Enrico Sorio", title = "Automatic generation of regular expressions from examples with genetic programming", booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, Genetic programming: Poster", pages = "1477--1478", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2331000", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We explore the practical feasibility of a system based on genetic programming (GP) for the automatic generation of regular expressions. The user describes the desired task by providing a set of labeled examples, in the form of text lines. The system uses these examples for driving the evolutionary search towards a regular expression suitable for the specified task. Usage of the system should require neither familiarity with GP nor with regular expressions syntax. In our GP implementation each individual represents a syntactically correct regular expression. We performed an experimental evaluation on two different extraction tasks applied to real-world datasets and obtained promising results in terms of precision and recall, even in comparison to an earlier state-of-the-art proposal.", notes = "Also known as \cite{2331000} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Bartoli:2014:GECCO, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Playing Regex Golf with Genetic Programming", booktitle = "GECCO '14: Proceeding of the sixteenth annual conference on genetic and evolutionary computation conference", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", pages = "1063--1070", organisation = "SIGEVO", address = "Vancouver, BC, Canada", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, isbn13 = "978-1-4503-2662-9", URL = "http://machinelearning.inginf.units.it/publications/international-conference-publications/playingregexgolfwithgeneticprogramming", URL = "http://doi.acm.org/10.1145/2576768.2598333", DOI = "doi:10.1145/2576768.2598333", abstract = "Regex golf has recently emerged as a specific kind of code golf, i.e., unstructured and informal programming competitions aimed at writing the shortest code solving a particular problem. A problem in regex golf consists in writing the shortest regular expression which matches all the strings in a given list and does not match any of the strings in another given list. The regular expression is expected to follow the syntax of a specified programming language, e.g., Javascript or PHP. In this paper, we propose a regex golf player internally based on Genetic Programming. We generate a population of candidate regular expressions represented as trees and evolve such population based on a multi-objective fitness which minimises the errors and the length of the regular expression. We assess experimentally our player on a popular regex golf challenge consisting of 16 problems and compare our results against those of a recently proposed algorithm---the only one we are aware of. Our player obtains scores which improve over the baseline and are highly competitive also with respect to human players. The time for generating a solution is usually in the order of tens minutes, which is arguably comparable to the time required by human players.", notes = "Also known as \cite{2598333} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Bartoli:2014:PPSN, author = "Alberto Bartoli and Simone Cumar and Andrea {De Lorenzo} and Eric Medvet", title = "Compressing Regular Expression Sets for Deep Packet Inspection", booktitle = "13th International Conference on Parallel Problem Solving from Nature", year = "2014", editor = "Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith", publisher = "Springer", isbn13 = "978-3-319-10761-5", pages = "394--403", series = "Lecture Notes in Computer Science", address = "Ljubljana, Slovenia", month = "13-17 " # sep, volume = "8672", keywords = "genetic algorithms, genetic programming", URL = "http://machinelearning.inginf.units.it/publications/international-conference-publications/compressingregularexpressionsetsfordeeppacketinspection", DOI = "doi:10.1007/978-3-319-10762-2_39", abstract = "The ability to generate security-related alerts while analysing network traffic in real time has become a key mechanism in many networking devices. This functionality relies on the application of large sets of regular expressions describing attack signatures to each individual packet. Implementing an engine of this form capable of operating at line speed is considerably difficult and the corresponding performance problems have been attacked from several points of view. In this work we propose a novel approach complementing earlier proposals: we suggest transforming the starting set of regular expressions to another set of expressions which is much smaller yet classifies network traffic in the same categories as the starting set. Key component of the transformation is an evolutionary search based on Genetic Programming: a large population of expressions represented as abstract syntax trees evolves by means of mutation and crossover, evolution being driven by fitness indexes tailored to the desired classification needs and which minimise the length of each expression. We assessed our proposals on real datasets composed of up to 474 expressions and the outcome has been very good, resulting in compressions in the order of 74percent. Our results are highly encouraging and demonstrate the power of evolutionary techniques in an important application domain.", notes = "PPSN-XIII", } @Article{Bartoli:2014:Computer, author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De Lorenzo} and Eric Medvet and Enrico Sorio", journal = "IEEE Computer", title = "Automatic Synthesis of Regular Expressions from Examples", year = "2014", month = dec, volume = "47", number = "12", pages = "72--80", keywords = "genetic algorithms, genetic programming, text extraction, NLP", ISSN = "0018-9162", DOI = "doi:10.1109/MC.2014.344", size = "9 pages", abstract = "We propose a system for the automatic generation of regular expressions for text-extraction tasks. The user describes the desired task only by means of a set of labelled examples. The generated regexes may be used with common engines such as those that are part of Java, PHP, Perl and so on. Usage of the system does not require any familiarity with regular expressions syntax. We performed an extensive experimental evaluation on 12 different extraction tasks applied to real-world datasets. We obtained very good results in terms of precision and recall, even in comparison to earlier state-of-the-art proposals. Our results are highly promising toward the achievement of a practical surrogate for the specific skills required for generating regular expressions, and significant as a demonstration of what can be achieved with GP-based approaches on modern IT technology.", notes = "http://regex.inginf.units.it Levenshtein distance = edit distance. NSGA-II. 'We transform a tree into a regular expression by means of a depth-first post order visit' Also known as \cite{6994453}", } @InProceedings{Bartoli:2015:EuroGP, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Learning Text Patterns using Separate-and-Conquer Genetic Programming", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "16--27", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, Regular expressions, Multiple pattern, Programming by example, Text extraction", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_2", abstract = "The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Bartoli:2015:GECCO, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao and Marco Virgolin", title = "Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1183--1190", keywords = "genetic algorithms, genetic programming, Real World Applications", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754706", DOI = "doi:10.1145/2739480.2754706", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "There is an increasing interest in the development of techniques for automatic relation extraction from unstructured text. The biomedical domain, in particular, is a sector that may greatly benefit from those techniques due to the huge and ever increasing amount of scientific publications describing observed phenomena of potential clinical interest. In this paper, we consider the problem of automatically identifying sentences that contain interactions between genes and proteins, based solely on a dictionary of genes and proteins and a small set of sample sentences in natural language. We propose an evolutionary technique for learning a classifier that is capable of detecting the desired sentences within scientific publications with high accuracy. The key feature of our proposal, that is internally based on Genetic Programming, is the construction of a model of the relevant syntax patterns in terms of standard part-of-speech annotations. The model consists of a set of regular expressions that are learned automatically despite the large alphabet size involved. We assess our approach on two realistic datasets and obtain 74percent accuracy, a value sufficiently high to be of practical interest and that is in line with significant baseline methods.", notes = "Also known as \cite{2754706} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Bartoli:2016:ASC, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Predicting the effectiveness of pattern-based entity extractor inference", journal = "Applied Soft Computing", volume = "46", pages = "398--406", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.05.023", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616302241", abstract = "An essential component of any workflow leveraging digital data consists in the identification and extraction of relevant patterns from a data stream. We consider a scenario in which an extraction inference engine generates an entity extractor automatically from examples of the desired behaviour, which take the form of user-provided annotations of the entities to be extracted from a dataset. We propose a methodology for predicting the accuracy of the extractor that may be inferred from the available examples. We propose several prediction techniques and analyse experimentally our proposals in great depth, with reference to extractors consisting of regular expressions. The results suggest that reliable predictions for tasks of practical complexity may indeed be obtained quickly and without actually generating the entity extractor.", keywords = "genetic algorithms, genetic programming, String similarity metrics, Information extraction, Hardness estimation", } @Article{Bartoli:2016:ieeeTKDE, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Inference of Regular Expressions for Text Extraction from Examples", journal = "IEEE Transactions on Knowledge and Data Engineering", year = "2016", volume = "28", number = "5", pages = "1217--1230", month = may, keywords = "genetic algorithms, genetic programming", ISSN = "1041-4347", URL = "http://www.human-competitive.org/sites/default/files/bartoli-delorenzo-medvet-tarlao-text.txt", DOI = "doi:10.1109/TKDE.2016.2515587", abstract = "A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work, we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behaviour. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators. A prototype is available as a web application at regex.inginf.units.it", notes = "Entered 2016 HUMIES Department of Engineering and Architecture (DIA), University of Trieste, Italy. Also known as \cite{7374717}", } @Article{Bartoli:2016:ieeeIS, author = "Alberto Bartoli and Eric Medvet and Andrea {De Lorenzo} and Fabiano Tarlao", title = "Can A Machine Replace Humans In Building Regular Expressions? A Case Study", journal = "IEEE Intelligent Systems", year = "2016", volume = "31", number = "6", pages = "15--21", month = nov, keywords = "genetic algorithms, genetic programming, Buildings, Creativity, Intelligent systems, Object recognition, Pattern matching, Web pages", ISSN = "1541-1672", URL = "http://www.human-competitive.org/sites/default/files/bartoli-delorenzo-medvet-tarlao-text.txt", DOI = "doi:10.1109/MIS.2016.46", size = "10 pages", abstract = "Regular expressions are routinely used in a variety of different application domains. Building a regular expression involves a considerable amount of skill, expertise and creativity. In this work we investigate whether a machine may surrogate these qualities and construct automatically regular expressions for tasks of realistic complexity. We discuss a large scale experiment involving more than 1700 users on 10 challenging tasks. We compared the solutions constructed by these users to those constructed by a tool based on Genetic Programming that we have recently developed and made publicly available. The quality of automatically-constructed solutions turned out to be similar to the quality of those constructed by the most skilled user group; and, the time for automatic construction was similar to the time required by human users.", notes = "Entered 2016 HUMIES University of Trieste. cited by \cite{Bartoli:2016:GECCOcomp}. Also known as \cite{7478431}", } @InProceedings{conf/sac/BartoliLMT16, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Active learning approaches for learning regular expressions with genetic programming", bibdate = "2016-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2016.html#BartoliLMT16", booktitle = "Proceedings of the 31st Annual {ACM} Symposium on Applied Computing, Pisa, Italy, April 4-8, 2016", publisher = "ACM", year = "2016", editor = "Sascha Ossowski", isbn13 = "978-1-4503-3739-7", pages = "97--102", keywords = "genetic algorithms, genetic programming, entity extraction, information extraction, machine learning, programming by examples", DOI = "doi:10.1145/2851613.2851668", abstract = "We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behaviour. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required.", notes = "See \cite{Bartoli:2016:acmACR}", } @Article{Bartoli:2016:acmACR, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Regex-based Entity Extraction with Active Learning and Genetic Programming", journal = "ACM SIGAPP Applied Computing Review", year = "2016", volume = "16", number = "2", pages = "7--15", month = jun, keywords = "genetic algorithms, genetic programming, entity extraction, information extraction, machine learning, programming by examples", publisher = "ACM", address = "New York, NY, USA", ISSN = "1559-6915", URL = "https://sites.google.com/site/machinelearningts/publications/international-journal-publications/regex-basedentityextractionwithactivelearningandgeneticprogramming/2016-ACR-RegexEntityExtractionActiveLearningGP.pdf", URL = "http://doi.acm.org/10.1145/2993231.2993232", DOI = "doi:10.1145/2993231.2993232", acmid = "2993232", size = "9 pages", abstract = "We consider the long-standing problem of the automatic generation of regular expressions for text extraction, based solely on examples of the desired behaviour. We investigate several active learning approaches in which the user annotates only one desired extraction and then merely answers extraction queries generated by the system. The resulting framework is attractive because it is the system, not the user, which digs out the data in search of the samples most suitable to the specific learning task. We tailor our proposals to a state-of-the-art learner based on Genetic Programming and we assess them experimentally on a number of challenging tasks of realistic complexity. The results indicate that active learning is indeed a viable framework in this application domain and may thus significantly decrease the amount of costly annotation effort required.", notes = "See \cite{conf/sac/BartoliLMT16}. Also known as \cite{Bartoli:2016:REE:2993231.2993232}", } @InProceedings{Bartoli:2016:GECCOcomp, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "On the Automatic Construction of Regular Expressions from Examples (GP vs. Humans 1-0)", booktitle = "GECCO 2016 Hot of the Press", year = "2016", editor = "Benjamin Doerr and Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "155--156", keywords = "genetic algorithms, genetic programming", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", DOI = "doi:10.1145/2908961.2930946", abstract = "Regular expressions are systematically used in a number of different application domains. Writing a regular expression for solving a specific task is usually quite difficult, requiring significant technical skills and creativity. We have developed a tool based on Genetic Programming capable of constructing regular expressions for text extraction automatically, based on examples of the text to be extracted. We have recently demonstrated that our tool is human-competitive in terms of both accuracy of the regular expressions and time required for their construction. We base this claim on a large-scale experiment involving more than 1700 users on 10 text extraction tasks of realistic complexity. The F-measure of the expressions constructed by our tool was almost always higher than the average F-measure of the expressions constructed by each of the three categories of users involved in our experiment (Novice, Intermediate, Experienced). The time required by our tool was almost always smaller than the average time required by each of the three categories of users. The experiment is described in full detail in Can a machine replace humans? A case study. IEEE Intelligent Systems, 2016 \cite{Bartoli:2016:ieeeIS}", notes = "Distributed at GECCO-2016.", } @InProceedings{Bartoli:2016:PPSN, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Syntactical Similarity Learning by means of Grammatical Evolution", booktitle = "14th International Conference on Parallel Problem Solving from Nature", year = "2016", editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter", volume = "9921", series = "LNCS", pages = "260--269", address = "Edinburgh", month = "17-21 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-45823-6", DOI = "doi:10.1007/978-3-319-45823-6_24", abstract = "Several research efforts have shown that a similarity function synthesized from examples may capture an application-specific similarity criterion in a way that fits the application needs more effectively than a generic distance definition. In this work, we propose a similarity learning algorithm tailored to problems of syntax-based entity extraction from unstructured text streams. The algorithm takes in input pairs of strings along with an indication of whether they adhere or not adhere to the same syntactic pattern. Our approach is based on Grammatical Evolution and explores systematically a similarity definition space including all functions that may be expressed with a specialized, simple language that we have defined for this purpose. We assessed our proposal on patterns representative of practical applications. The results suggest that the proposed approach is indeed feasible and that the learned similarity function is more effective than the Levenshtein distance and the Jaccard similarity index.", notes = "PPSN2016", } @Article{Bartoli:2017:ieeeTC, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", journal = "IEEE Transactions on Cybernetics", title = "Active Learning of Regular Expressions for Entity Extraction", year = "2018", volume = "48", number = "3", pages = "1067--1080", month = mar, keywords = "genetic algorithms, genetic programming, automatic programming, evolutionary computation, inference mechanisms, man machine systems, semisupervised learning, text processing", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2017.2680466", size = "14 pages", abstract = "We consider the automatic synthesis of an entity extractor, in the form of a regular expression, from examples of the desired extractions in an unstructured text stream. This is a long-standing problem for which many different approaches have been proposed, which all require the preliminary construction of a large dataset fully annotated by the user. we propose an active learning approach aimed at minimizing the user annotation effort: the user annotates only one desired extraction and then merely answers extraction queries generated by the system. During the learning process, the system digs into the input text for selecting the most appropriate extraction query to be submitted to the user in order to improve the current extractor. We construct candidate solutions with genetic programming (GP) and select queries with a form of querying-by-committee, i.e., based on a measure of disagreement within the best candidate solutions. All the components of our system are carefully tailored to the peculiarities of active learning with GP and of entity extraction from unstructured text. We evaluate our proposal in depth, on a number of challenging datasets and based on a realistic estimate of the user effort involved in answering each single query. The results demonstrate high accuracy with significant savings in terms of computational effort, annotated characters, and execution time over a state-of-the-art baseline.", notes = "Also known as \cite{7886274}", } @Article{BARTOLI:2019:ASC, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Giovanni Squillero", title = "Multi-level diversity promotion strategies for Grammar-guided Genetic Programming", journal = "Applied Soft Computing", volume = "83", pages = "105599", year = "2019", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2019.105599", URL = "http://www.sciencedirect.com/science/article/pii/S1568494619303795", keywords = "genetic algorithms, genetic programming, Representation, Grammatical evolution, CFGGP, SGE, WHGE", abstract = "Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyzes in diversity promotion", } @Article{Bartoli:2019:ieeeTCyber, author = "Alberto Bartoli and Mauro Castelli and Eric Medvet", title = "Weighted Hierarchical Grammatical Evolution", journal = "IEEE Transactions on Cybernetics", year = "2020", volume = "50", number = "2", pages = "476--488", month = feb, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genotype-phenotype, mapping, representation", ISSN = "2168-2267", URL = "https://sites.google.com/site/machinelearningts/publications/international-journal-publications/weightedhierarchicalgrammaticalevolution/2018-TCyb-WHGE.pdf", DOI = "doi:10.1109/TCYB.2018.2876563", size = "13 pages", abstract = "Grammatical Evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings of a language defined by a user-provided context-free grammar (CFG). In this work, we propose a novel procedure for mapping genotypes to phenotypes that we call Weighted Hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results to the standard GE framework as well as to two of the most significant enhancements proposed in the literature: Position-independent GE and Structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.", notes = "also known as \cite{8525307}", } @Article{Bartoli:ieeeTcybernetics, author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao", title = "Automatic Search-and-Replace From Examples With Coevolutionary Genetic Programming", journal = "IEEE Transactions on Cybernetics", year = "2021", volume = "51", number = "5", pages = "2612--2624", month = may, keywords = "genetic algorithms, genetic programming, diversity promotion, find-and-replace, programming by examples, regular expressions", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2019.2918337", abstract = "We describe the design and implementation of a system for executing search-and-replace text processing tasks automatically, based only on examples of the desired behaviour. The examples consist of pairs describing the original string and the desired modified string. Their construction, thus, does not require any specific technical skill. The system constructs a solution to the specified task that can be used unchanged on popular existing software for text processing. The solution consists of a search pattern coupled with a replacement expression: the former is a regular expression which describes both the strings to be replaced and their portions to be reused in the latter, which describes how to build the modified strings. Our proposed system is internally based on Genetic Programming and implements a form of cooperative coevolution in which two separate populations are evolved independently, one for search patterns and the other for replacement expressions. We assess our proposal on six tasks of realistic complexity obtaining very good results, both in terms of absolute quality of the solutions and with respect to the challenging baselines considered.", notes = "also known as \cite{8734703}", } @Article{bartoli:2023:GPEM, author = "Alberto Bartoli and Luca Manzoni and Eric Medvet", title = "Commentary on {``Jaws 30'', by W. B. Langdon}", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 23", month = dec, note = "Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/drZe8", DOI = "doi:10.1007/s10710-023-09471-1", size = "4 pages", abstract = "While genetic programming has had a huge impact on the research community, it is fair to say that its impact on industry and practitioners has been much smaller. In this commentary we elaborate on this claim and suggest some broad research goals aimed at greatly increasing such impact.", notes = "Response to \cite{langdon:jaws30} Peer commentary editors: Leonardo Vanneschi and Leonardo Trujillo \cite{Vanneschi:2023:GPEM} See also \cite{jaws30_reply}", } @InProceedings{DBLP:conf/rsctc/BartonV08, author = "Alan J. Barton and Julio J. Valdes", title = "Computational Intelligence Techniques Applied to Magnetic Resonance Spectroscopy Data of Human Brain Cancers", booktitle = "Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2008", editor = "Chien-Chung Chan and Jerzy W. Grzymala-Busse and Wojciech Ziarko", series = "Lecture Notes in Computer Science", volume = "5306", year = "2008", pages = "485--494", address = "Akron, OH, USA", month = oct # " 23-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-88423-1", DOI = "doi:10.1007/978-3-540-88425-5_50", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data. In particular, two approaches, Rough Sets and a Genetic Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm. Good preliminary results were obtained with 100percent training and 100percent testing accuracy that differentiate normal versus malignant samples.", } @InProceedings{Barton:2009:IJCNN, author = "Alan J. Barton and Julio J. Valdes and Robert Orchard", title = "Learning the neuron functions within a neural network via Genetic Programming: Applications to geophysics and hydrogeology", booktitle = "International Joint Conference on Neural Networks, IJCNN 2009", year = "2009", pages = "264--271", address = "Atlanta, Georgia, USA", month = jun # " 14-19", keywords = "genetic algorithms, genetic programming, gene expression programming, geophysics, geophysics computing, hydrology, neural nets, geophysics, hydrogeology, neural network classifier, neural network neurons, neuron functions", DOI = "doi:10.1109/IJCNN.2009.5178731", size = "8 pages", abstract = "A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation function. This paper learns everything within the neuron using a variant of genetic programming called gene expression programming. That is, this paper does not explicitly use weights or activation functions within a neuron, nor bias nodes within a layer. Promising preliminary results are reported for a study of the detection of underground caves (a 1 class problem) and for a study of the interaction of water and minerals near a glacier in the Arctic (a 5 class problem).", notes = " one class membership. ANN variable with 0 mean 1 standard deviation. Also known as \cite{5178731} See \cite{Barton2009614}", } @Article{Barton2009614, author = "Alan J. Barton and Julio J. Valdes and Robert Orchard", title = "Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming", journal = "Neural Networks", volume = "22", number = "5-6", pages = "614--622", year = "2009", note = "Advances in Neural Networks Research: IJCNN2009, 2009 International Joint Conference on Neural Networks", editor = "S. Bressler and R. Kozma and L. Perlovsky and Venayagamoorthy", keywords = "genetic algorithms, genetic programming, General neuron model, Evolutionary Computation, Hybrid algorithm, Machine learning, Parameter space, Visualization", ISSN = "0893-6080", DOI = "doi:10.1016/j.neunet.2009.06.043", URL = "http://www.sciencedirect.com/science/article/B6T08-4WNRK15-3/2/d8803b07859caa7efcd99475af7005ae", abstract = "Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.", } @InProceedings{Barton:2010:ieeeCIBCB, author = "Alan J. Barton", title = "Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach", booktitle = "2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", year = "2010", month = may, abstract = "Proteomics involves collecting and analysing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem; including very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit. This paper extends previous work and explores finding an explicit global function for which two stages are involved: i) computation of a set of candidate functions (results) by the algorithm, and ii) searching within the set for patterns of interest. For the first stage, three classes of approximating global functions are considered: Class 1 functions that have a completely unknown structure, Class 2 functions that have a tiny amount of domain knowledge incorporated, and Class 3 functions that have a small amount of domain knowledge incorporated. For the second stage, some issues with current similarity measures for mathematical expressions are discussed and a new measure is proposed. Preliminary experimental results with an Evolutionary Computation algorithm called Gene Expression Programming (a variant of Genetic Programming) when used with a fuzzy membership within the fitness function are reported.", keywords = "genetic algorithms, genetic programming, gene expression programming, fuzzy-evolutionary computational proteomics approach, liquid chromatography coupled mass spectrometry, mathematical function, nanoLC-MS, nanoLC-MS data, nonlinear retention time shifts problem, biocomputing, evolutionary computation, fuzzy set theory, proteins, proteomics", DOI = "doi:10.1109/CIBCB.2010.5510688", notes = "Also known as \cite{5510688}", } @Article{Bartos:2013:SIGMOD, author = "Tomas Bartos and Tomas Skopal and Juraj Mosko", title = "Towards Efficient Indexing of Arbitrary Similarity", journal = "SIGMOD Record", year = "2013", volume = "42", number = "2", pages = "5--10", month = jul, note = "Vision Paper. ACM Special Interest Group on Management of Data", keywords = "genetic algorithms, genetic programming", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/sigmod.bib", acmid = "2503794", publisher = "ACM", address = "New York, NY, USA", ISSN = "0163-5808", URL = "http://doi.acm.org/10.1145/2503792.2503794", DOI = "doi:10.1145/2503792.2503794", size = "6 pages", abstract = "The popularity of similarity search expanded with the increased interest in multimedia databases, bioinformatics, or social networks, and with the growing number of users trying to find information in huge collections of unstructured data. During the exploration, the users handle database objects in different ways based on the used similarity models, ranging from simple to complex models. Efficient indexing techniques for similarity search are required especially for growing databases. In this paper, we study implementation possibilities of the recently announced theoretical framework SIMDEX, the task of which is to algorithmically explore a given similarity space and find possibilities for efficient indexing. Instead of a fixed set of indexing properties, such as metric space axioms, SIMDEX aims to seek for alternative properties that are valid in a particular similarity model (database) and, at the same time, provide efficient indexing. In particular, we propose to implement the fundamental parts of SIMDEX by means of the genetic programming (GP) which we expect will provide high-quality resulting set of expressions (axioms) useful for indexing.", acknowledgement = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|", notes = "Bartos:2013:TEI:2503792.2503794", } @InProceedings{Bartovs:2013:GECCO, author = "Tomas Bartos and Tomas Skopal and Juraj Mosko", title = "Efficient indexing of similarity models with inequality symbolic regression", booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", isbn13 = "978-1-4503-1963-8", pages = "901--908", keywords = "genetic algorithms, genetic programming", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2463372.2463487", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The increasing amount of available unstructured content introduced a new concept of searching for information, the content-based retrieval. The principle behind is that the objects are compared based on their content which is far more complex than simple text or metadata based searching. Many indexing techniques arose to provide an efficient and effective similarity searching. However, these methods are restricted to a specific domain such as the metric space model. If this prerequisite is not fulfilled, indexing cannot be used, while each similarity search query degrades to sequential scanning which is unacceptable for large datasets. Inspired by previous successful results, we decided to apply the principles of genetic programming to the area of database indexing. We developed the GP-SIMDEX which is a universal framework that is capable of finding precise and efficient indexing methods for similarity searching for any given similarity data. For this purpose, we introduce the inequality symbolic regression principle and show how it helps the GP-SIMDEX Framework to find appropriate results that in most cases outperform the best-known indexing methods.", notes = "Also known as \cite{2463487} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{conf/sisap/BartosS13, author = "Tomas Bartos and Tomas Skopal", title = "Designing Similarity Indexes with Parallel Genetic Programming", booktitle = "Proceedings of the 6th International Conference on Similarity Search and Applications (SISAP 2013)", year = "2013", editor = "Nieves R. Brisaboa and Oscar Pedreira and Pavel Zezula", volume = "8199", series = "Lecture Notes in Computer Science", pages = "294--299", address = "A Coruna, Spain", month = oct # " 2-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", bibdate = "2013-09-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sisap/sisap2013.html#BartosS13", isbn13 = "978-3-642-41061-1", URL = "http://dx.doi.org/10.1007/978-3-642-41062-8", URL = "http://dx.doi.org/10.1007/978-3-642-41062-8_29", DOI = "doi:10.1007/978-3-642-41062-8_29", size = "6 pages", abstract = "The increasing diversity of unstructured databases leads to the development of advanced indexing techniques as the metric indexing model does not fit to the general similarity models. Once the most critical postulate, namely the triangle inequality, does not hold, the metric model produces notable errors during the query evaluation. To overcome this situation and to obtain more qualitative results, we want to discover better indexing models for databases using arbitrary similarity measures. However, each database is unique in a specific way, so we outline the automatic way of exploring the best indexing method. We introduce the exploration approach using parallel genetic programming principles in a multi-threaded environment built upon recently introduced SIMDEX Framework. Furthermore, we introduce smart pivot table which is an intelligent indexing method capable of incorporating obtained results. We supplement the theoretical background with experiments showing the achieved improvements in comparison to the single-threaded evaluations.", } @InCollection{Bartram:2008:ECP, author = "Derek Bartram and Michael Burrow and Xin Yao", title = "A Computational Intelligence Approach to Railway Track Intervention Planning", booktitle = "Evolutionary Computation in Practice", publisher = "Springer", year = "2008", editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar Roy", volume = "88", series = "Studies in Computational Intelligence", chapter = "8", pages = "163--198", keywords = "genetic algorithms, genetic programming, k-means, RPCL, learning", isbn13 = "978-3-540-75770-2", DOI = "doi:10.1007/978-3-540-75771-9_8", abstract = "Railway track intervention planning is the process of specifying the location and time of required maintenance and renewal activities. To facilitate the process, decision support tools have been developed and typically use an expert system built with rules specified by track maintenance engineers. However, due to the complex interrelated nature of component deterioration, it is problematic for an engineer to consider all combinations of possible deterioration mechanisms using a rule based approach. To address this issue, this chapter describes an approach to the intervention planning using a variety of computational intelligence techniques. The proposed system learns rules for maintenance planning from historical data and incorporates future data to update the rules as they become available thus the performance of the system improves over time. To determine the failure type, historical deterioration patterns of sections of track are first analysed. A Rival Penalised Competitive Learning algorithm is then used to determine possible failure types. We have devised a generalised two stage evolutionary algorithm to produce curve functions for this purpose. The approach is illustrated using an example with real data which demonstrates that the proposed methodology is suitable and effective for the task in hand.", notes = "Part of \cite{TinaYu:2008:book} Scheduling, railroad, maintenance, planning. Missing data. Missing values. p181 function set + - * / sin cos tan power", } @Article{BartschJr:2016:TJU, author = "Georg {Bartsch Jr.} and Anirban P. Mitra and Sheetal A. Mitra and Arpit A. Almal and Kenneth E. Steven and Donald G. Skinner and David W. Fry and Peter F. Lenehan and William P. Worzel and Richard J. Cote", title = "Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder", journal = "The Journal of Urology", volume = "195", number = "2", pages = "493--498", year = "2016", ISSN = "0022-5347", DOI = "doi:10.1016/j.juro.2015.09.090", URL = "http://www.sciencedirect.com/science/article/pii/S0022534715049629", abstract = "Purpose Due to the high recurrence risk of non-muscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with non muscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumour. Materials and Methods Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. Results The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77percent sensitivity and 85percent specificity to predict recurrence in the training set, and 69percent and 62percent, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80percent sensitivity and 90percent specificity in the training set, and 71percent and 67percent, respectively, in the test set. Conclusions Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management.", keywords = "genetic algorithms, genetic programming, urinary bladder neoplasms, neoplasm recurrence, local, genome, algorithms, software", } @InProceedings{basanta03, author = "David Basanta and Mark A. Miodownik and Elizabeth A. Holm", title = "Evolving Cellular Automata to Grow Microstructures", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "1--10", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-00971-X", URL = "http://rcswww.urz.tu-dresden.de/~basanta/eurogp03.pdf", DOI = "doi:10.1007/3-540-36599-0_1", abstract = "The properties of engineering structures such as cars, cell phones or bridges rely on materials and on the properties of these materials. The study of these properties, which are determined by the internal architecture of the material or microstructure, has significant importance for material scientists. One of the things needed for this study is a tool that can create microstructural patterns. In this paper we explore the use of a genetic algorithm to evolve the rules of an effector automata to recreate these microstructural patterns.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @InProceedings{Basanta:2004:EH, author = "David Basanta and Mark A. Miodownik and Peter J. Bentley and Elizabeth A. Holm", title = "Evolving and Growing Microstructures of Materials using Biologically Inspired CA", booktitle = "2004 NASA/DoD Conference on Evolvable Hardware", year = "2004", publisher = "IEEE Computer Society", editor = "RS Zebulum", pages = "275--275", address = "Seattle, Washington, USA", month = jun # " 24-26", keywords = "genetic algorithms, genetic programming", ISBN = "0-7695-2145-2", DOI = "doi:10.1109/EH.2004.1310841", abstract = "The properties of engineering structures, such as robotic arms, aircrafts or bridges, rely on the properties of the materials used to build them. The internal architecture of the material or microstructure determines its properties and therefore, its study is of great interest for engineers and material scientists. Although there are tools that can provide 2D microstructural information, tools that can be used to obtain 3D characterisations of microstructures for routine analysis are not yet available to material scientists. In this paper we will describe Microconstructor. Microconstructor comprises a genetic algorithm that evolves populations of Cellular Automata inspired by developmental biology that self organise into 3D patterns that can be used for microstructural analysis.", } @Article{Baser:2017:Energy, author = "Furkan Baser and Haydar Demirhan", title = "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation", journal = "Energy", volume = "123", pages = "229--240", year = "2017", keywords = "genetic algorithms, genetic programming, Artificial intelligence, Fuzzy regression, METEONORM, Solar radiation model, Support vector machines", ISSN = "0360-5442", DOI = "doi:10.1016/j.energy.2017.02.008", URL = "http://www.sciencedirect.com/science/article/pii/S0360544217301822", abstract = "Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that uses fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics.", } @InProceedings{Basgalupp:2014:GECCO, author = "Marcio Porto Basgalupp and Rodrigo Coelho Barros and Tiago Barabasz", title = "A grammatical evolution based hyper-heuristic for the automatic design of split criteria", booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2662-9", pages = "1311--1318", keywords = "genetic algorithms, genetic programming, grammatical evolution", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2576768.2598327", DOI = "doi:10.1145/2576768.2598327", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Top-down induction of decision trees (TDIDT) is a powerful method for data classification. A major issue in TDIDT is the decision on which attribute should be selected for dividing the nodes in subsets, creating the tree. For performing such a task, decision trees make use of a split criterion, which is usually an information-theory based measure. Apparently, there is no free-lunch regarding decision-tree split criteria, as is the case of most things in machine learning. Each application may benefit from a distinct split criterion, and the problem we pose here is how to identify the suitable split criterion for each possible application that may emerge. We propose in this paper a grammatical evolution algorithm for automatically generating split criteria through a context-free grammar. We name our new approach ESC-GE (Evolutionary Split Criteria with Grammatical Evolution). It is empirically evaluated on public gene expression datasets, and we compare its performance with state-of-the-art split criteria, namely the information gain and gain ratio. Results show that ESC-GE outperforms the baseline criteria in the domain of gene expression data, indicating its effectiveness for automatically designing tailor-made split criteria.", notes = "Also known as \cite{2598327} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)", } @InProceedings{Basher:2022:CEC, author = "Sheikh Faishal Basher and Brian J. Ross", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", title = "Managing Diversity and Many Objectives in Evolutionary Design", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, isbn13 = "978-1-6654-6708-7", abstract = "A new approach to evolving a diversity of high-quality solutions for problems having many objectives is presented. Mouret and Clunes MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. We introduce into MAP-elites the many objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. We also enhance MAP-Elites by extending it with multiple solutions per cell (the original MAP-Elites saves only a single solution per cell). Different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 light weight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions, which can be competitive in quality to those from standard GP runs.", keywords = "genetic algorithms, genetic programming, Visualization, Image synthesis, Image color analysis, Sociology, Search problems, Entropy, Performance analysis, Diversity, Many-objective Optimization, Evolutionary Art, Procedural Texture", DOI = "doi:10.1109/CEC55065.2022.9870353", notes = "Also known as \cite{9870353}", } @Article{journals/apin/Bashir14, author = "Shariq Bashir", title = "Combining pre-retrieval query quality predictors using genetic programming", journal = "Appl. Intell", year = "2014", number = "3", volume = "40", pages = "525--535", keywords = "genetic algorithms, genetic programming", bibdate = "2014-03-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/apin/apin40.html#Bashir14", URL = "http://dx.doi.org/10.1007/s10489-013-0475-z", } @Article{Bashir:2016:ASC, author = "Shariq Bashir and Wasif Afzal and Abdul Rauf Baig", title = "Opinion-Based Entity Ranking using learning to rank", journal = "Applied Soft Computing", volume = "38", pages = "151--163", year = "2016", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2015.10.001", URL = "http://www.sciencedirect.com/science/article/pii/S156849461500616X", abstract = "As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevant entities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgements and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many ranking features, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach.", keywords = "genetic algorithms, genetic programming, Entity Ranking, Opinion analysis, Learning to rank", } @Misc{DBLP:journals/corr/BasiosLWKLB17, author = "Michail Basios and Lingbo Li and Fan Wu and Leslie Kanthan and Donald Lawrence and Earl T. Barr", title = "Darwinian Data Structure Selection", howpublished = "arXiv", year = "2017", month = "10 " # jun, keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based software engineering, SBSE, Software analysis and optimisation, Multi-objective optimisation, SBSE, Software Engineering", timestamp = "Tue, 29 Aug 2017 15:03:42 +0200", biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/BasiosLWKLB17", bibsource = "dblp computer science bibliography, http://dblp.org", URL = "http://arxiv.org/abs/1706.03232", size = "11 pages", abstract = "Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. We introduce ARTEMIS a multiobjective, cloud-based optimisation framework that automatically finds optimal, tuned data structures and rewrites applications to use them. ARTEMIS achieves substantial performance improvements for every project in a set of 29 Java programs uniformly sampled from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution for each project that improves all measures. The median improvement across all these best solutions is 8.38percent for execution time, 24.27percent for memory consumption and 11.61percent for CPU usage. In detail, ARTEMIS improved the memory consumption of JUnit4, a ubiquitous Java testing framework, by 45.42percent memory, while also improving its execution time 2.29percent at the cost a 1.25percent increase in CPU usage. LinkedIn relies on the Cleo project as their autocompletion engine for search. ARTEMIS improves its execution time by 12.17percent, its CPU usage by 4.32percent and its memory consumption by 23.91percent.", } @InProceedings{Basios:2017:SSBSE, author = "Michail Basios and Lingbo Li and Fan Wu and Leslie Kanthan and Earl T. Barr", title = "Optimising Darwinian Data Structures on {Google Guava}", booktitle = "Proceedings of the 9th International Symposium on Search Based Software Engineering, SSBSE 2017", year = "2017", editor = "Tim Menzies and Justyna Petke", volume = "10452", series = "LNCS", pages = "161--167", address = "Paderborn, Germany", month = sep # " 9-11", publisher = "Springer", note = "Best Challenge Paper Award", keywords = "genetic algorithms, genetic improvement, SBSE, Software analysis and optimisation, Multi-objective optimisation", isbn13 = "978-3-319-66299-2", DOI = "doi:10.1007/978-3-319-66299-2_14", size = "7 pages", abstract = "Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, 27percent of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13percent, execution time by up to 9percent, and 4percent CPU usage.", notes = "Is this GP? http://ssbse17.github.io/ Co-located with FSE/ESEC 2017", } @InProceedings{Basios:2018:FSE, author = "Michail Basios and Lingbo Li and Fan Wu and Leslie Kanthan and Earl T. Barr", title = "Darwinian Data Structure Selection", booktitle = "Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018", year = "2018", editor = "Gary T. Leavens and Alessandro Garcia and Corina S. Pasareanu", pages = "118--128", address = "Lake Buena Vista, FL, USA", month = "4-9 " # nov, publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Search-based Software Engineering, SBSE, Software Analysis and Optimisation", isbn13 = "978-1-4503-5573-5", URL = "http://human-competitive.org/sites/default/files/artemis.pdf", DOI = "doi:10.1145/3236024.3236043", acmid = "3236043", size = "11 pages", abstract = "Data structure selection and tuning is laborious but can vastly improve an applications performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86percent (37/43) of the projects. The median improvement across the best solutions is 4.8percent, 10.1percent, 5.1percent for runtime, memory and CPU usage. These aggregate results understate ARTEMIS potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache XML transformation tool. ARTEMIS improves gson by 16.5percent, 1percent and 2.2percent for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by 23.5percent. Every client of these projects will benefit from these performance improvements.", notes = "Bronze winner 2019 HUMIES. Slides: http://www.human-competitive.org/sites/default/files/basiosslides.pptx Also known as \cite{Basios:2018:DDS:3236024.3236043}", } @PhdThesis{Basios_10070648_thesis, author = "Michail Basios", title = "Darwinian Code Optimisation", school = "Department of Computer Science, University College London", year = "2019", address = "UK", month = "18 " # jan, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, Blockchain, Java, JVM, Immortal bugs, Proteus grammar, solidify, ETH, MultiSig, ICO, trampoline", URL = "https://discovery.ucl.ac.uk/id/eprint/10070648", URL = "https://discovery.ucl.ac.uk/id/eprint/10070648/1/Basios_10070648_thesis.pdf", size = "173 pages", abstract = "Programming is laborious. A long-standing goal is to reduce this cost through automation. Genetic Improvement (GI) is a new direction for achieving this goal. It applies search to the task of program improvement. The research conducted in this thesis applies GI to program optimisation and to enable program optimisation. In particular, it focuses on automatic code optimisation for complex managed runtimes,such as Java and Ethereum Virtual Machines. We introduce the term Darwinian Data Structures (DDS) for the data structures of a program that share a common interface and enjoy multiple implementations. We call them Darwinian since we can subject their implementations to the survival of the fittest. We introduce ARTEMIS, a novel cloud-based multi-objective multi-language optimisation framework that automatically finds optimal, tuned data structures and rewrites the source code of applications accordingly to use them. ARTEMIS achieves substantial performance improvements for 44 diverse programs. ARTEMIS achieves 4.8percent, 10.1percent, 5.1percent median improvement for runtime, memory and CPU usage. Even though GI has been applied successfully to improve properties of programs running in different runtimes, GI has not been applied in Blockchains, such as Ethereum. The code immutability of programs running on top of Ethereum limits the application of GI. The first step of applying GI in Ethereum is to overcome the code immutability limitations. Thus, to enable optimisation, we present PROTEUS, a state of the art framework that automatically extends the functionality of smart contracts written in Solidity and makes them upgradeable. Thus, allowing developers to introduce alternative optimised versions of code (e.g., code that consumes less gas), found by GI, in newer versions.", notes = "http://www.darwinianoptimiser.com/ Supervisor: Earl T. Barr", } @InProceedings{DBLP:conf/visapp/BasiratR19, author = "Mina Basirat and Peter M. Roth", editor = "Alain Tremeau and Giovanni Maria Farinella and Jose Braz", title = "Learning Task-specific Activation Functions using Genetic Programming", booktitle = "Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, {VISIGRAPP} 2019, Volume 5: VISAPP, Prague, Czech Republic, February 25-27, 2019", pages = "533--540", publisher = "SciTePress", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.5220/0007408205330540", DOI = "doi:10.5220/0007408205330540", timestamp = "Tue, 04 Jun 2019 15:54:43 +0200", biburl = "https://dblp.org/rec/conf/visapp/BasiratR19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Bassett:2012:GECCO, author = "Jeffrey Bassett and Uday Kamath and Kenneth {De Jong}", title = "A new methodology for the GP theory toolbox", booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference", year = "2012", editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb", isbn13 = "978-1-4503-1177-9", pages = "719--726", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330264", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Recently Quantitative Genetics has been successfully employed to understand and improve operators in some Evolutionary Algorithms (EAs) implementations. This theory offers a phenotypic view of an algorithm's behavior at a population level, and suggests new ways of quantifying and measuring concepts such as exploration and exploitation. In this paper, we extend the quantitative genetics approach for use with Genetic Programming (GP), adding it to the set of GP analysis techniques. We use it in combination with some existing diversity and bloat measurement tools to measure, analyze and predict the evolutionary behavior of several GP algorithms. GP specific benchmark problems, such as ant trail and symbolic regression, are used to provide new insight into how various evolutionary forces work in combination to affect the search process. Finally, using the tools, a multivariate phenotypic crossover operator is designed to both improve performance and control bloat on the difficult ant trail problem.", notes = "Also known as \cite{2330264} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)", } @PhdThesis{Bassett:thesis, author = "Jeffrey Kermes Bassett", title = "Methods for Improving the Design and Performance of Evolutionary Algorithms", school = "The Volgenau School of Engineering, George Mason University", year = "2012", address = "USA", month = "Fall", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/1920/8122", URL = "http://digilib.gmu.edu/dspace/bitstream/handle/1920/8122/Bassett_gmu_0883E_10215.pdf", size = "160 pages", abstract = "Evolutionary Algorithms (EAs) can be applied to almost any optimization or learning problem by making some simple customizations to the underlying representation and/or reproductive operators. This makes them an appealing choice when facing a new or unusual problem. Unfortunately, while making these changes is often easy, getting a customized EA to operate effectively (i.e. find a good solution quickly) can be much more difficult. Ideally one would hope that theory would provide some guidance here, but in these cases, evolutionary computation (EC) theories are not easily applied. They either require customization themselves, or they require information about the problem that essentially includes the solution. Consequently most practitioners rely on an ad-hoc approach, incrementally modifying and testing various customizations until they find something that works reasonably well. The drawback that most EC theories face is that they are closely associated with the underlying representation of an individual (i.e. the genetic code). There has been some success at addressing this limitation by applying a biology theory called quantitative genetics to EAs. This approach allows one to monitor the behaviour of an EA by observing distributions of an outwardly observable phenotypic trait (usually fitness), and thus avoid modelling the algorithm's internal details. Unfortunately, observing a single trait does not provide enough information to diagnose most problems within an EA. It is my hypothesis that using multiple traits will allow one to observe how the population is traversing the search space, thus making more detailed diagnosis possible. In this work, I adapt a newer multivariate form of quantitative genetics theory for use with evolutionary algorithms and derive a general equation of population variance dynamics. This provides a foundation for building a set of tools that can measure and visualize important characteristics of an algorithm, such as exploration, exploitation, and heritability, throughout an EA run. Finally I demonstrate that the tools can actually be used to identify and fix problems in two well known EA variants: Pittsburgh approach rule systems and genetic programming trees.", notes = "Supervisor Kenneth A. De Jong", } @Article{Bastian:2000:FSS, author = "Andreas Bastian", title = "Identifying fuzzy models utilizing genetic programming", journal = "Fuzzy Sets and Systems", volume = "113", pages = "333--350", year = "2000", number = "3", month = "1 " # aug, keywords = "genetic algorithms, genetic programming, System identification, Fuzzy modeling", URL = "http://www.sciencedirect.com/science/article/B6V05-4234BFC-1/1/261a04fa056f3f0dfe0fb79a773a971a", abstract = "Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages fuzzy models are employed in various fields of applications, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasised that the identification of a fuzzy model is a complex optimisation task with many local minima. Genetic programming provides a way to solve such complex optimization problems. In this work, the use of genetic programming to identify the input variables, the rule base and the involved membership functions of a fuzzy model is proposed. For this purpose, several new reproduction operators are introduced.", } @Article{BastoFernandes:2014:PT, author = "Vitor Basto-Fernandes and Iryna Yevseyeva and Rafael Z. Frantz and Carlos Grilo and Noemi {Perez Diaz} and Michael Emmerich", title = "An Automatic Generation of Textual Pattern Rules for Digital Content Filters Proposal, Using Grammatical Evolution Genetic Programming", journal = "Procedia Technology", volume = "16", pages = "806--812", year = "2014", note = "CENTERIS 2014 - Conference on ENTERprise Information Systems / ProjMAN 2014 - International Conference on Project MANagement / HCIST 2014 - International Conference on Health and Social Care Information Systems and Technologies", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Spam filtering, Digital Content Filters, Classification", ISSN = "2212-0173", DOI = "doi:10.1016/j.protcy.2014.10.030", URL = "http://www.sciencedirect.com/science/article/pii/S2212017314002576", abstract = "This work presents a conceptual proposal to address the problem of intensive human specialised resources that are nowadays required for the maintenance and optimised operation of digital contents filtering in general and anti-spam filtering in particular. The huge amount of spam, malware, virus, and other illegitimate digital contents distributed through network services, represents a considerable waste of physical and technical resources, experts and end users time, in continuous maintenance of anti-spam filters and deletion of spam messages, respectively. The problem of cumbersome and continuous maintenance required to keep anti-spam filtering systems updated and running in an efficient way, is addressed in this work by the means of genetic programming grammatical evolution techniques, for automatic rules generation, having SpamAssassin anti-spam system and SpamAssassin public corpus as the references for the automatic filtering customisation.", } @Article{Batenkov:2010:HIG:1836543.1836558, author = "Dmitry Batenkov", title = "Hands-on introduction to genetic programming", journal = "XRDS Crossroads", year = "2010", volume = "17", number = "1", pages = "46--51", month = sep # " 2010", note = "The ACM Magazine for Students", keywords = "genetic algorithms, genetic programming, Coding Tools and Techniques, Expressions and Their Representation, Object-oriented Programming, Problem Solving, Control Methods, Search", ISSN = "1528-4972", acmid = "1836558", publisher = "ACM", URL = "http://xrds.acm.org/article.cfm?aid=1836558", DOI = "doi:10.1145/1836543.1836558", code_url = "https://xrds.acm.org/helloworld/2010/08/genetic-programming.cfm", size = "2.5 pages", abstract = "The idea to mimic the principles of Darwinian evolution in computing has been around at least since the 1950s, so long, in fact, that it has grown into the field called evolutionary computing (EC). In this tutorial, we'll learn the basic principles of EC and its offspring, genetic programming (GP), on a {"}toy problem{"} of symbolic regression. We'll also learn how to use OpenBeagle, a generic C++ object-oriented EC framework.", notes = "http://xrds.acm.org/ Christian Gagne's Open Beagle", } @Article{Batenkov:2011:GPEM, author = "Dmitry Batenkov", title = "Open BEAGLE: a generic framework for evolutionary computations", journal = "Genetic Programming and Evolvable Machines", year = "2011", volume = "12", number = "3", pages = "329--331", month = sep, note = "Software review", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8fs", DOI = "doi:10.1007/s10710-011-9135-4", publisher = "Springer", size = "3 pages", affiliation = "Weizmann Institute of Science, Rehovot, Israel", } @InProceedings{Bates:2003:ICCIFE, author = "R. G. Bates and M. A. H. Dempster and Y. S. Romahi", title = "Evolutionary reinforcement learning in {FX} order book and order flow analysis", booktitle = "IEEE International Conference on Computational Intelligence for Financial Engineering", year = "2003", pages = "355--362", address = "Hong Kong", month = "20-23 " # mar, keywords = "genetic algorithms, genetic programming", URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2003/WP06.pdf", DOI = "doi:10.1109/CIFER.2003.1196282", abstract = "As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.", notes = "Final report to HSBC Investment Bank, November (2002). Location: Technical report WP06/2003 ", } @InProceedings{conf/agi/BatishchevaP15, author = "Vita Batishcheva and Alexey Potapov", title = "Genetic Programming on Program Traces as an Inference Engine for Probabilistic Languages", booktitle = "Proceedings of the 8th International Conference Artificial General Intelligence, AGI 2015", year = "2015", editor = "Jordi Bieger and Ben Goertzel and Alexey Potapov", volume = "9205", series = "Lecture Notes in Computer Science", pages = "14--24", address = "Berlin, Germany", month = jul # " 22-25", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Probabilistic programming, Program traces", isbn13 = "978-3-319-21364-4", bibdate = "2015-07-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/agi/agi2015.html#BatishchevaP15", URL = "http://dx.doi.org/10.1007/978-3-319-21365-1", DOI = "doi:10.1007/978-3-319-21365-1_2", abstract = "Methods of simulated annealing and genetic programming over probabilistic program traces are developed firstly. These methods combine expressiveness of Turing-complete probabilistic languages, in which arbitrary generative models can be defined, and search effectiveness of meta-heuristic methods. To use these methods, one should only specify a generative model of objects of interest and a fitness function over them without necessity to implement domain-specific genetic operators or mappings from objects to and from bit strings. On the other hand, implemented methods showed better quality than the traditional mh-query on several optimization tasks. Thus, these results can contribute to both fields of genetic programming and probabilistic programming.", } @MastersThesis{Batista:mastersthesis, author = "Joao Eduardo {Silva Pombinho Batista}", title = "Studying elements of genetic programming for multiclass classification", school = "Department of Informatics, Faculdade de Ciencias, Universidade de Lisboa", year = "2018", address = "Lisbon, Portugal", keywords = "genetic algorithms, genetic programming, M3GP, Java, Machine Learning, Classification, Multiclass, Multidimensional clustering, Programacao genetica, Aprendizagem automatica, Classificacao, Multi-classe, Aglomeracao multi-dimensional", URL = "http://hdl.handle.net/10451/35287", URL = "https://repositorio.ul.pt/handle/10451/35287", URL = "https://repositorio.ul.pt/bitstream/10451/35287/1/ulfc121857_tm_Jo%c3%a3o_Batista.pdf", size = "97 pages", abstract = "Although Genetic Programming (GP) has been very successful in both symbolic regression and binary classification by solving many difficult problems from various domains, it requires improvements in multiclass classification, which due to the high complexity of this kind of problems, requires specialised classifiers. In this project, we explored a multiclass classification GP-based algorithm, the M3GP [4]. The individuals in standard GP only have one node at their root. This means that their output space is in R. Unlike standard GP, M3GP allows each individual to have n nodes at its root. This variation changes the output space to Rn, allowing them to construct clusters of samples and use a cluster-based classification. Although M3GP is capable of creating interpretable models while having competitive results with state-of-the-art classifiers, such as Random Forests and Neural Networks, it has downsides. The focus of this project is to improve the algorithm by exploring two components, the fitness function, and the genetic operators' selection method. The original fitness function was accuracy-based. Since using this kind of functions does not allow a smooth evolution of the output space, we tried to improve the algorithm by exploring two distance-based fitness functions as an attempt to separate the clusters while bringing the samples closer to their respective centroids. Until now, the genetic operators in M3GP were selected with a fixed probability. Since some operators have a better effect on the fitness at different stages of the evolution, the fixed probabilities allow operators to be selected at the wrong stages of the evolution, slowing down the learning process. In this project, we try to evolve the probability the genetic operators have of being chosen over the generations. On a later stage, we proposed a new crossover genetic operator that uses three individuals for the M3GP algorithm. The results obtained show significantly better results in the training set in half the datasets, while improving the test accuracy in two datasets.", notes = "In English SEG WAV VOW YST HRT MCD3 MCD10 Adviser: Sara Guilherme Oliveira da Silva", } @InProceedings{Batista:2019:GECCOcomp, author = "Joao E. Batista and Nuno M. Rodrigues and Sara Silva", title = "To adapt or not to adapt, or the beauty of random settings", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "336--337", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321994", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3321994} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Batista:2020:CEC, author = "Joao E. Batista and Sara Silva", title = "Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by {M3GP}", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24404", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Classification, RemoteSensing, Feature Spaces, Hyper-features, Transfer Learning", isbn13 = "978-1-7281-6929-3", URL = "https://arxiv.org/abs/2002.00053", DOI = "doi:10.1109/CEC48606.2020.9185630", size = "8 pages", abstract = "One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better. These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method", notes = "https://wcci2020.org/ Faculdade de Ciencias, Universidade de Lisboa, Portugal. Also known as \cite{9185630}", } @Article{batista:2021:remotesensing, author = "Joao E. Batista and Ana I. R. Cabral and Maria J. P. Vasconcelos and Leonardo Vanneschi and Sara Silva", title = "Improving Land Cover Classification Using Genetic Programming for Feature Construction", journal = "Remote Sensing", year = "2021", volume = "13", number = "9", article-number = "1623", keywords = "genetic algorithms, genetic programming, evolutionary computation, machine learning, classification, multi-class classification, feature construction, hyperfeatures, spectral indices", ISSN = "2072-4292", URL = "https://www.mdpi.com/2072-4292/13/9/1623", URL = "https://www.mdpi.com/2072-4292/13/9/1623.pdf", DOI = "doi:10.3390/rs13091623", size = "25 pages", abstract = "Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.", notes = "Also remotesensing-13-01623-v2 LASIGE, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal", } @InProceedings{batista:2022:GECCOlba, author = "Joao Batista and Sara Silva", title = "Evolving a {Cloud-Robust} Water Index with Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Yew-Soon Ong and Abhishek Gupta", pages = "55--56", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, feature construction, water indices, remote sensing, satellite imagery", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3533946", abstract = "Over the years, remote sensing (RS) experts created many indices to help them study satellite imagery by highlighting characteristics like vegetation, water, or burnt areas, among others. In this work, we study water indices. Although there is a large number of water indices that work perfectly in unclouded imagery, clouds and shadows cast by clouds are often mistaken for water. This work is focused on the automatic feature construction using genetic programming (GP), in an attempt to make features that are more robust to these issues. To do this, we use a dataset containing pixels from areas where we could find these issues to evolve models that learn how to classify those pixels correctly. The results indicate improvements when comparing evolved features with indices, but further improvements are required to tackle other issues found.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Batista:2022:CEC, author = "Joao E. Batista and Sara Silva", title = "Comparative study of classifier performance using automatic feature construction by {M3GP}", booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)", year = "2022", editor = "Carlos A. Coello Coello and Sanaz Mostaghim", address = "Padua, Italy", month = "18-23 " # jul, keywords = "genetic algorithms, genetic programming, M3GP, Python, naive Bayes, decision trees, Support vector machines, random forests, xgboost, Machine learning algorithms, Computational modeling, Evolutionary computation, Classification algorithms, Complexity theory, Feature Construction, Multiclass Classification", isbn13 = "978-1-6654-6708-7", DOI = "doi:10.1109/CEC55065.2022.9870343", code_url = "http://github.com/jespb/Python-M3GP", size = "8 pages", abstract = "The M3GP algorithm, originally designed to perform multiclass classification with genetic programming, is also a powerful feature construction method. Here we explore its ability to evolve hyper-features that are tailored not only to the problem to be solved, but also to the learning algorithm that is used to solve it. We pair M3GP with six different machine learning algorithms and study its performance in eight classification problems from different scientific domains, with substantial variety in the number of classes, features and samples. The results show that automatic feature construction with M3GP, when compared to using the standalone classifiers without feature construction, achieves statistically significant improvements in the majority of the test cases, sometimes by a very large margin, while degrading the weighted f-measure in only one out of 48 cases. We observe the differences in the number and size of the hyper-features evolved for each case, hypothesising that the simpler the classifier, the larger the amount of problem complexity is being captured in the hyperfeatures. Our results also reveal that the M3GP algorithm can be improved, both in execution time and in model quality, by replacing its default classifier with support vector machines or random forest classifiers.", notes = "Also known as \cite{9870343}", } @Article{Batista:2022:IJRS, author = "Joao E. Batista and Nuno M. Rodrigues and Ana I. R. Cabral and Maria J. P. Vasconcelos and Adriano Venturieri and Luiz G. T. Silva and Sara Silva", title = "Optical time series for the separation of land cover types with similar spectral signatures: cocoa agroforest and forest", journal = "International Journal of Remote Sensing", year = "2022", volume = "43", number = "9", pages = "3298--3319", keywords = "genetic algorithms, genetic programming, Cocoa agroforest classification, land cover mapping, machine learning, time series, tropical areas", publisher = "Taylor \& Francis", DOI = "doi:10.1080/01431161.2022.2089540", data_url = "https://github.com/jespb/Cocoa_PublicDS", size = "22 pages", abstract = "One of the main applications of machine learning (ML) in remote sensing (RS) is the pixel-level classification of satellite images into land cover types. Although classes with different spectral signatures can be easily separated, e.g. aquatic and terrestrial land cover types, others have similar spectral signatures and are hard to separate using only the information within a single pixel. This work focused on the separation of two cover types with similar spectral signatures, cocoa agroforest and forest, over an area in Para, Brazil. For this, we study the training and application of several ML algorithms on datasets obtained from a single composite image, a time-series (TS) composite obtained from the same location and by preprocessing the TS composite using simple TS preprocessing techniques. As expected, when ML algorithms are applied to a dataset obtained from a composite image, the median producer accuracy (PA) and user accuracy (UA) in those two classes are significantly lower than the median overall accuracy (OA) for all classes. The second dataset allows the ML models to learn the evolution of the spectral signatures over 5 months. Compared to the first dataset, the results indicate that ML models generalise better using TS data, even if the series are short and without any preprocessing. This generalization is further improved in the last dataset. The ML models are subsequently applied to an area with different geographical bounds. These last results indicate that, out of seven classifiers, the popular random forest (RF) algorithm ranked fourth, while XGBoost (xGB) obtained the best results. The best OA, as well as the best PA/UA balance, were obtained by performing feature construction using the M3GP algorithm and then applying XGB to the new extended dataset.", notes = "a LASIGE, Faculty of Sciences, University of Lisbon, Portugal", } @InProceedings{batista:2024:CEC, author = "Joao Eduardo Batista and Nuno Miguel Rodrigues and Leonardo Vanneschi and Sara Silva", title = "{M6GP:} Multiobjective Feature Engineering", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Training, Machine learning algorithms, Power demand, Machine learning, Predictive models, Market research, Multiobjective Optimization, Feature Engineering, Explainable AI, Interpretability", isbn13 = "979-8-3503-0837-2", URL = "http://hdl.handle.net/10362/172920", DOI = "doi:10.1109/CEC60901.2024.10612107", abstract = "The current trend in machine learning is to use powerful algorithms to induce complex predictive models that often fall under the category of black-box models. Thanks to this, there is also a growing interest in studying model explainability and interpretability so that human experts can understand, validate, and correct those models. With the objective of promoting the creation of inherently interpretable models, we present M6GP. This wrapper-based multi-objective automatic feature engineering algorithm combines key components of the M3GP and NSGA-II algorithms. Wrapping M6GP around another machine learning algorithm evolves a set of features optimised for this algorithm while potentially increasing its robustness. We compare our results with M3GP and M4GP, two ancestors from the same algorithm family, and verify that, by using a multi-objective approach, M6GP obtains equal or better results. In addition, by using complexity metrics on the list of objectives, the M6GP models come down to one-fifth of the size of the M3GP models, making them easier to read by comparison.", notes = "also known as \cite{10612107} WCCI 2024", } @InProceedings{batista:2024:CEC2, author = "Joao Eduardo Batista and Adam Kotaro Pindur and Hitoshi Iba and Sara Silva", title = "Measuring Structural Complexity of {GP} Models for Feature Engineering over the Generations", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Analytical models, Computational modeling, Pipelines, Predictive models, Prediction algorithms, Complexity theory, Model Complexity, Feature Engineering, Model Interpretability, Classification", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611989", abstract = "Feature engineering is a necessary step in the machine learning pipeline. Together with other preprocessing methods, it allows the conversion of raw data into a dataset containing only the necessary features to solve the task at hand, reducing the computational complexity of inducing models and creating models that are potentially simpler, more robust, and more interpretable. We use M3GP, a wrapper-based feature engineering algorithm, to induce a set of features that are adapted in number and in shape to several classifiers with different levels of predictive power, from decision trees with depth 3 to random forests with 100 estimators and no depth limit. Intuition tells us that classifiers that are restricted in the number of features should compensate for this restriction by using features with a high degree of correlation with the target objective. By opposition, the principle behind the boosting algorithm tells us that we can create a strong classifier using a large set of weak features. This indicates that classifiers with no restrictions should prefer many but weaker features. Our results confirm this hypothesis while also revealing that M3GP induces unnecessarily complex features. We measure complexity using several structural complexity metrics found in the literature and show that, although our pipeline consistently obtains good results, the structural complexity of the induced models varies drastically across runs. Additionally, while the test performance peaks in the early stages of the evolution, the complexity of the feature engineering models continues to grow, with little to no return in test performance. This work promotes using several complexity metrics to measure model interpretability and identifies issues related to model complexity in M3GP, proposing solutions to improve the computational cost of inducing models and the complexity of the final models.", notes = "also known as \cite{10611989} WCCI 2024", } @InProceedings{conf/iwann/BatistaSSLR13, author = "Rayco Batista and Eduardo Segredo and Carlos Segura and Coromoto Leon and Casiano Rodriguez", title = "Solving the Unknown Complexity Formula Problem with Genetic Programming", bibdate = "2013-06-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iwann/iwann2013-1.html#BatistaSSLR13", booktitle = "Advances in Computational Intelligence - 12th International Work-Conference on Artificial Neural Networks, {IWANN} 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part {I}", publisher = "Springer", year = "2013", volume = "7902", editor = "Ignacio Rojas and Gonzalo Joya Caparr{\'o}s and Joan Cabestany", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-38678-7", pages = "232--240", series = "Lecture Notes in Computer Science", URL = "http://dx.doi.org/10.1007/978-3-642-38679-4", DOI = "doi:10.1007/978-3-642-38679-4_22", abstract = "The Unknown Complexity Formula Problem (ucfp) is a particular case of the symbolic regression problem in which an analytical complexity formula that fits with data obtained by multiple executions of certain algorithm must be given. In this work, a set of modifications has been added to the standard Genetic Programming (GP) algorithm to deal with the ucfp. This algorithm has been applied to a set of well-known benchmark functions of the symbolic regression problem. Moreover, a real case of the ucfp has been tackled. Experimental evaluation has demonstrated the good behaviour of the proposed approach in obtaining high quality solutions, even for a real instance of the ucfp. Finally, it is worth pointing out that the best published results for the majority of benchmark functions have been improved.", } @InProceedings{2018_Batot_Sahraoui_SSBSE18, author = "Edouard Batot and Houari Sahraoui", title = "Injecting Social Diversity in Multi-Objective Genetic Programming: the Case of Model Well-formedness Rule Learning", booktitle = "SSBSE 2018", year = "2018", editor = "Thelma Elita Colanzi and Phil McMinn", volume = "11036", series = "LNCS", pages = "166--181", address = "Montpellier, France", month = "8-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE, Model Driven Engineering (MDE), Metamodel, Modelling Space, WFR, OCL, NSGA-II, SSDM, TF-IDF", isbn13 = "978-3-319-99241-9", URL = "http://www-ens.iro.umontreal.ca/~batotedo/papers/2018_Batot_Sahraoui_SSBSE18.pdf", DOI = "doi:10.1007/978-3-319-99241-9_8", size = "15 pages", abstract = "Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of the Model Driven Engineering (MDE) paradigm. Nevertheless, in the recent years, much research work has been dedicated to learn MDE artefacts instead of writing them manually. In this context, mono- and multi-objective Genetic Programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of input/out examples representing the expected behaviour of an artefact. Generally, the conformance to the training example set is the main objective to lead the search for a solution. Yet, single fitness peak, or local optima deadlock, one of the major drawbacks of GP, happens when adapted to MDE and hinder the results of the learning. We aim at showing in this paper that an improvement in population social diversity carried out during the evolutionary", notes = "FamilyTree, Statemachine, and Project Manager Social semantic diversity instead of crowding distance", } @PhdThesis{Batot_Edouard_2018_These, author = "Edouard Batot", title = "From examples to knowledge in model-driven engineering : a holistic and pragmatic approach", school = "Departement d'informatique et de recherche operationnelle, Universite de Montreal", year = "2018", address = "Canada", month = nov, keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Model-Driven Engineering, Search Based Software Engineering, Learning from examples, Generalization and Representativeness, Machine Learning, Aritficial Intelligence, Genie logiciel, Apprentissage machine, Intelligence artificielle, Genie logiciel experimental, Apprentissage a partir d'exemples", URL = "https://papyrus.bib.umontreal.ca/xmlui/bitstream/1866/21737/2/Batot_Edouard_2018_These.pdf", URL = "http://hdl.handle.net/1866/21737", size = "169 pages", abstract = "Model-Driven Engineering (MDE) is a software development approach that proposes to raise the level of abstraction of languages in order to shift the design and understanding effort from a programmer point of view to the one of decision makers. However, the manipulation of these abstract representations, or models, has become so complex that traditional techniques are not enough to automate its inherent tasks. For its part, the Search-Based Software Engineering (SBSE) proposes to reformulate the automation of MDE tasks as optimization problems. Once reformulated, the problem will be solved by metaheuristic algorithms. With a plethora of studies on the subject, the power of automation of SBSE has been well established. Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions. However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions. We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities.", abstract = "Le Model-Driven Engineering (MDE) est une approche de developpement logiciel qui propose d'elever le niveau d'abstraction des langages afin de deplacer l'effort de conception et de comprehension depuis le point de vue des programmeurs vers celui des decideurs du logiciel. Cependant, la manipulation de ces representations abstraites, ou modeles, est devenue tellement complexe que les moyens traditionnels ne suffisent plus a automatiser les differentes taches. De son cote, le Search-Based Software Engineering (SBSE) propose de reformuler l'automatisation des taches du MDE comme des problemes d'optimisation. Une fois reformule, la resolution du probleme sera effectuee par des algorithmes metaheuristiques. Face a la plethore d'etudes sur le sujet, le pouvoir d'automatisation du SBSE n'est plus a demontrer. C'est en s'appuyant sur ce constat que la communaute du Example-Based MDE (EBMDE) a commence a utiliser des exemples d'application pour alimenter la reformulation SBSE du probleme d'apprentissage de tache MDE. Dans ce contexte, la concordance de la sortie des solutions avec les exemples devient un barometre efficace pour evaluer l'aptitude d'une solution a resoudre une tache. Cette mesure a prouve etre un objectif semantique de choix pour guider la recherche metaheuristique de solutions. Cependant, s'il est communement admis que la representativite des exemples a un impact sur la generalisabilite des solutions, l'etude de cet impact souffre d'un manque de consideration flagrant. Dans cette these, nous proposons une formulation globale du processus d'apprentissage dans un contexte MDE incluant une methodologie complete pour caracteriser et evaluer la relation qui existe entre la generalisabilite des solutions et deux proprietes importantes des exemples, leur taille et leur couverture. Nous effectuons l'analyse empirique de ces deux proprietes et nous proposons un plan detaille pour une analyse plus approfondie du concept de representativite, ou d'autres representativites.", notes = "Suvervisor: Houari Sahraoui", } @Article{Batot:2022:SSM, author = "Edouard R. Batot and Houari Sahraoui", title = "Promoting social diversity for the automated learning of complex {MDE} artifacts", journal = "Software and Systems Modeling", year = "2022", volume = "21", pages = "1159--1178", month = jun, keywords = "genetic algorithms, genetic programming, Model-driven engineering, Social diversity, MOGP, NSGA-II, fitness sharing, ROUGE", ISSN = "1619-1366", URL = "https://rdcu.be/c69Rx", DOI = "doi:10.1007/s10270-021-00969-9", size = "20 pages", abstract = "Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of model-driven engineering (MDE). Nevertheless, in the recent years, much research work has been dedicated to learn executable MDE artifacts instead of writing them manually. In this context, mono- and multi-objective genetic programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of examples representing the expected behaviour of an artifact. Generally, conformance to the training example set is the main objective to lead the learning process. Yet, single fitness peak, or local optima deadlock, a common challenge in GP, hinders the application of GP to MDE. we propose a strategy to promote populations social diversity during the GP learning process. We evaluate our approach with an empirical study featuring the case of learning well-formedness rules in MDE with a multi-objective genetic programming algorithm. Our evaluation shows that integration of social diversity leads to more efficient search, faster convergence, and more generalizable results. Moreover, when the social diversity is used as crowding distance, this convergence is uniform through a hundred of runs despite the probabilistic nature of GP. It also shows that genotypic diversity strategies cannot achieve comparable results", notes = "p1165 'SSD increases proportionally to the number of examples solved and is offset by the frequency of which an example is solved by the population's individuals, which helps to adjust for the fact that some examples are more frequently solved in general' See also https://semla.polymtl.ca/wp-content/uploads/2022/11/SEMLA22_HSahraoui.pdf 'GP-based techniques allow to learn actionable and explainable artifacts but require precise specifications' ", } @InProceedings{battle:1999:GPFKBFLC, author = "Daryl Battle and Abdollah Homaifar and Edward Tunstel and Gerry Dozier", title = "Genetic Programming of Full Knowledge Bases for Fuzzy Logic Controllers", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1463--1468", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-730.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-730.ps", size = "6 pages", abstract = "Genetic programming (GP) is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Baudry:2014:ISSTA, author = "Benoit Baudry and Simon Allier and Martin Monperrus", title = "Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants", booktitle = "Proceedings of the 2014 International Symposium on Software Testing and Analysis, ISSTA 2014", year = "2014", pages = "149--159", address = "San Jose, CA, USA", month = jul # " 21-25", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Program Transformation, Software Diversity", isbn13 = "978-1-4503-2645-2", acmid = "2610415", URL = "https://hal.archives-ouvertes.fr/hal-00938855/document", URL = "http://doi.acm.org/10.1145/2610384.2610415", DOI = "doi:10.1145/2610384.2610415", size = "11 pages", abstract = "The predictability of program execution provides attackers a rich source of knowledge who can exploit it to spy or remotely control the program. Moving target defence addresses this issue by constantly switching between many diverse variants of a program, which reduces the certainty that an attacker can have about the program execution. The effectiveness of this approach relies on the availability of a large number of software variants that exhibit different executions. However, current approaches rely on the natural diversity provided by of-the-shelf components, which is very limited. In this paper, we explore the automatic synthesis of large sets of program variants, called sosies. Sosies provide the same expected functionality as the original program, while exhibiting different executions. They are said to be computationally diverse. This work addresses two objectives: comparing different transformations for increasing the likelihood of sosie synthesis (densifying the search space for sosies); demonstrating computation diversity in synthesized sosies. We synthesized 30 184 sosies in total, for 9 large, real-world, open source applications. For all these programs we identified one type of program analysis that systematically increases the density of sosies; we measured computation diversity for sosies of 3 programs and found diversity in method calls or data in more than 40percent of sosies. This is a step towards controlled massive unpredictability of software.", notes = "Add/replace AST mutations of existing program's source code. Spoon, JUnit, Dagger, EasyMock. Grid5000 p152 'sosiefication is a search problem'. 'natural software diversity' p158 'In total, we were able to synthesize 30184 sosies' Definition 1. Sosie (noun). Given a program P , a test suite T S for P and a program transformation T , a variant P 0 =T (P ) is a sosie of P if the two following conditions hold 1) there is at least one test case in T S that executes the part of P that is modified by T 2) all test cases in T S pass on P 0 . http://diversify-project.eu/sosiefied-programs/ also known as \cite{Baudry:2014:TSC:2610384.2610415}", } @Article{baudry:2015:acmcs, author = "Benoit Baudry and Martin Monperrus", title = "The Multiple Facets of Software Diversity: Recent Developments in Year 2000 and Beyond", journal = "ACM Computer Surveys", year = "2015", volume = "48", number = "1", month = sep, pages = "16:1--16:26", keywords = "genetic algorithms, genetic programming, Software Engineering, Software diversity, design principles, program transformation", acmid = "2807593", publisher = "ACM", address = "New York, NY, USA", ISSN = "0360-0300", URL = "http://arxiv.org/abs/1409.7324", URL = "https://hal.inria.fr/hal-01182103/document", DOI = "doi:10.1145/2807593", size = "26 pages", abstract = "Early experiments with software diversity in the mid 1970s investigated N-version programming and recovery blocks to increase the reliability of embedded systems. Four decades later, the literature about software diversity has expanded in multiple directions: goals (fault tolerance, security, software engineering), means (managed or automated diversity), and analytical studies (quantification of diversity and its impact). Our article contributes to the field of software diversity as the first work that adopts an inclusive vision of the area, with an emphasis on the most recent advances in the field. This survey includes classical work about design and data diversity for fault tolerance, as well as the cybersecurity literature that investigates randomization at different system levels. It broadens this standard scope of diversity to include the study and exploitation of natural diversity and the management of diverse software products. Our survey includes the most recent works, with an emphasis from 2000 to the present. The targeted audience is researchers and practitioners in one of the surveyed fields who miss the big picture of software diversity. Assembling the multiple facets of this fascinating topic sheds a new light on the field.", notes = "Brief mention of GP Also known as \cite{Baudry:2015:MFS:2808687.2807593}", } @InProceedings{Baudry:2018:GI, author = "Benoit Baudry and Nicolas Harrand and Eric Schulte and Christopher Timperley and Shin Hwei Tan and Marija Selakovic and Emamurho Ugherughe", title = "A spoonful of {DevOps} helps the {GI} Go Down", booktitle = "GI-2018, ICSE workshops proceedings", year = "2018", editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer", pages = "35--37", address = "Gothenburg, Sweden", month = "2 " # jun, publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, genetic improvement, continuous integration, DevOps", isbn13 = "978-1-4503-5753-1", URL = "http://www.shinhwei.com/devop-gi.pdf", DOI = "doi:10.1145/3194810.3194818", size = "2 pages", abstract = "DevOps emphasizes a high degree of automation at all phases of the software development lifecyle. Meanwhile, Genetic Improvement (GI) focuses on the automatic improvement of software artefacts. In this paper, we discuss why we believe that DevOps offers an excellent technical context for easing the adoption of GI techniques by software developers. We also discuss A/B testing as a prominent and clear example of GI taking place in the wild today, albeit one with human-supervised fitness and mutation operators.", notes = "GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}", } @InCollection{bauer:1995:EEAGPACSS, author = "Eric T. Bauer", title = "Evolving Efficient Algorithms by Genetic Programming: A Case Study in Sorting", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Bauer:2008:TREC, author = "Johannes M. Bauer and Kurt DeMaagd", title = "Network Management Practices and Sector Performance - A Genetic Programming Approach", booktitle = "36th Research Conference on Communications, Information, and Internet Policy", year = "2008", editor = "Elizabeth Mateja", address = "Arlington, VA, USA", month = sep # " 27", keywords = "genetic algorithms, genetic programming", URL = "http://www.tprcweb.com/images/stories/2008/Bauer-DeMaagd-Network-Management-2008-TPRC-fin.pdf", size = "27 pages", abstract = "Introduction The migration to next-generation network architectures, in which platform and application/content layers are relatively distinct, has unleashed a very important and possibly far-reaching policy debate as to the rules that should govern the interaction of players operating on one or both of these layers. Started as a discussion on network neutrality, the debate recently shifted focus to delineating reasonable from unreasonable forms of network management. Legislation to strengthen regulatory powers (Markey Bill) or antitrust enforcement (Conyers Bill) is pending in Congress. The Federal Communications Commission has reasserted its willingness to enforce an open internet in its Comcast Decision in August 2008.", notes = "http://www.tprcweb.com/index.php?option=com_content&view=article&id=29&Itemid=18", } @TechReport{baum:1998:tceaeTR, author = "Eric B. Baum and Igor Durdanovic", title = "Toward Code Evolution By Artificial Economies", institution = "NEC Research Institute", year = "1998", address = "4 Independence Way, Princeto, NJ 08540, USA", month = "10 " # jul, keywords = "genetic algorithms, genetic programming", size = "53 pages", notes = "Hayek2 blocks world 'crossover is much better than headless chicken mutation' meta-agents, inherited wealth, rent, intellectual property, strong typing STGP. See also \cite{baum:1998:tceae}, \cite{oai:CiteSeerPSU:5199}", } @InProceedings{baum:1998:tceae, author = "Eric B. Baum and Igor Durdanovic", title = "Toward Code Evolution By Artificial Economies", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "14--22", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.7596.pdf", size = "9 pages", abstract = "We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray's Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hill climber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimise our hillclimbing program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our hillclimber, bearing on a controversy in the Genetic Programming literature", notes = "citeseerx abstract and PDF not identical GP-98LB. See also \cite{baum:1998:tceaeTR}", } @InProceedings{oai:CiteSeerPSU:5199, author = "Eric B. Baum and Igor Durdanovic", title = "Toward Code Evolution By Artificial Economies (Extended Abstract)", booktitle = "Evolution as Computation, DIMACS Workshop, Princeton, January 1999", year = "2001", editor = "Laura F. Landweber and Erik Winfree", series = "Natural Computing Series", pages = "314--332", address = "Princeton University", month = "11-12 " # jan, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66709-1", URL = "http://citeseer.ist.psu.edu/5199.html", URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/2215/http:zSzzSzwww.neci.nj.nec.comzSzhomepageszSzericzSzevpap.pdf/toward-code-evolution-by.pdf", DOI = "doi:10.1007/978-3-642-55606-7_16", size = "16 pages", abstract = "We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray's Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hillclimber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimize our hillclimbing program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our hillclimber, bearing on a controversy in the Genetic Programming literature.", notes = "see also \cite{baum:1998:tceaeTR}, http://dimacs.rutgers.edu/Workshops/Evolution/ Published Jan 2001 http://www.amazon.com/exec/obidos/ASIN/3540667091/dominantsystems/107-7663466-9560554", } @Article{baum:2000:NeurComp, author = "Eric B. Baum and Igor Durdanovic", title = "Evolution of Cooperative Problem Solving in an Artificial Economy", journal = "Neural Computation", year = "2000", volume = "12", number = "12", pages = "2743--2775", month = dec, keywords = "genetic algorithms, genetic programming, STGP", ISSN = "0899-7667", URL = "https://www.eecs.harvard.edu/cs286r/courses/spring06/papers/baum_nc00.pdf", DOI = "doi:10.1162/089976600300014700", eprint = "https://direct.mit.edu/neco/article-pdf/12/12/2743/814655/089976600300014700.pdf", size = "33 pages", abstract = "We address the problem of how to reinforce learning in ultracomplex environments, with huge state-spaces, where one must learn to exploit a compact structure of the problem domain. The approach we propose is to simulate the evolution of an artificial economy of computer programs. The economy is constructed based on two simple principles so as to assign credit to the individual programs for collaborating on problem solutions. We find empirically that starting from programs that are random computer code, we can develop systems that solve hard problems. In particular, our economy learned to solve almost all random Blocks World problems with goal stacks that are 200 blocks high. Competing methods solve such problems only up to goal stacks of at most 8 blocks. Our economy has also learned to unscramble about half a randomly scrambled Rubik's cube and to solve several commercially sold puzzles.", notes = "PMID: 11112253", } @Article{Baumes2008, author = "Laurent A. Baumes and Pierre Collet", title = "Examination of genetic programming paradigm for high-throughput experimentation and heterogeneous catalysis", journal = "Computational Materials Science", year = "2009", volume = "45", number = "1", pages = "27--40", month = mar, keywords = "genetic algorithms, genetic programming, Heterogeneous catalysis, High-throughput, Materials, Combinatorial, Representation, Data structure", ISSN = "0927-0256", DOI = "doi:10.1016/j.commatsci.2008.03.051", URL = "http://www.sciencedirect.com/science/article/B6TWM-4T4J19Y-1/2/809324138cc0b8f49634eae7f22e995f", size = "14 pages", abstract = "The strong feature dependencies that exist in catalyst description do not permit using common algorithms while not loosing crucial information. Data treatments are restricted by the form of input data making the full use of the experimental information impossible, confining the experimentation studies, and reducing one of the primary goals of HTE: to enlarge the search space. Consequently, an advanced representation of the catalytic data supporting the intrinsic complexity of heterogeneous catalyst data structure is proposed. Likewise, an optimization strategy that can manipulate efficiently such data type, permitting a valuable connection between algorithms, high-throughput (HT) apparatus, and databases, is depicted. Such a new methodology enables the integration of domain knowledge through its configuration considering the study to be investigated. For the first time in heterogeneous catalysis, a conceptual examination of genetic programming (GP) is achieved.", notes = "Selected papers from the E-MRS 2007 Fall Meeting Symposium G: Genetic Algorithms in Materials Science and Engineering - GAMS-2007", } @Article{BBSTLCC09, author = "L. A. Baumes and A. Blansche and P. Serna and A. Tchougang and N. Lachiche and P. Collet and A. Corma", title = "Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts", journal = "Materials and Manufacturing Processes", year = "2009", volume = "24", number = "3", pages = "282--292", month = mar, keywords = "genetic algorithms, genetic programming, Data mining, Heterogeneous catalysis, High-throughput, Materials science", ISSN = "1042-6914", publisher = "Taylor and Francis", URL = "http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09", DOI = "doi:10.1080/10426910802679196", size = "11 pages", abstract = "Beside the ease and speed brought by automated synthesis stations and reactors technologies in materials science, adapted informatics tools must be further developed in order to handle the increase of throughput and data volume, and not to slow down the whole process. This article reports the use of genetic programming (GP) in heterogeneous catalysis. Despite the fact that GP has received only little attention in this domain, it is shown how such an approach can be turned into a very singular and powerful tool for solid optimization, discovery, and monitoring. Jointly with neural networks, the GP paradigm is employed in order to accurately and automatically estimate the whole curve conversion vs. time in the epoxidation of large olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2, as catalysts. In contrast to previous studies in combinatorial materials science and high-throughput screening, it was possible to estimate the entire evolution of the catalytic reaction for unsynthesized catalysts. Consequently, the evaluation of the performance of virtual solids is not reduced to a single point (e.g., the conversion level at only one given reaction time or the initial reaction rate). The methodology is thoroughly detailed, while stressing on the comparison between the recently proposed Context Aware Crossover (CAX) and the traditional crossover operator.", notes = "Affiliations: Institute of Chemical Technology, CSIC-UPV, Valencia, Spain Louis Pasteur University, LSIIT, FDBT, Illkirch, France", } @Article{krueg11ease, author = "Laurent A. Baumes and Frederic Kruger and Pierre Collet", title = "{EASEA}: a generic optimization tool for {GPU} machines in asynchronous island model", journal = "Computer Methods in Materials Science", year = "2011", volume = "11", number = "3", pages = "489--499", keywords = "genetic algorithms, genetic programming, GPGPU, Evolutionary Algorithms, Island Model, Parallelism, Zeolite Materials", ISSN = "1641-8581", publisher = "The AGH University of Science and Technology Press, Open Access", URL = "http://icube-publis.unistra.fr/docs/7407/baumes.pdf", URL = "http://cmms-editorial.agh.edu.pl/abstract.php?p_id=373", size = "11 pages", abstract = "Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R and D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend for super-computers and cloud computing, is presented. This last improvement of the EASEA platform allows an effortless exploitation of hierarchical massively parallel systems. It is demonstrated that supra-linear speedup over one machine and linear speedup considering clusters of different sizes are obtained. Such an island implementation over several potentially heterogeneous machines opens new horizon for various domains of application where computation time for optimisation remains the principal bottleneck.", notes = "Address LSIIT , Illkirch, FRA", } @Article{Baumes:2011:PCCP, author = "Laurent A. Baumes and Frederic Kruger and Santiago Jimenez and Pierre Collet and Avelino Corma", title = "Boosting theoretical zeolitic framework generation for the determination of new materials structures using {GPU} programming", journal = "Physical Chemistry Chemical Physics", year = "2011", volume = "13", number = "10", pages = "4674--4678", keywords = "genetic algorithms, genetic programming, memetic genetic algorithm, EASEA, GPU, GPGPU, nVidia GTX 295, CUDA", ISSN = "1463-9076", publisher = "The Royal Society of Chemistry", DOI = "doi:10.1039/C0CP02833A", size = "5 pages", abstract = "Evolutionary algorithms have proved to be efficient for solving complicated optimization problems. On the other hand, the many-core architecture in graphical cards General Purpose Graphic Processing Unit (GPGPU) offers one of the most attractive cost/performance ratio. Using such hardware, the manuscript shows how an efficiently implemented genetic algorithm with a simple fitness function allows boosting the determination of zeolite structures. A case study is presented.", notes = "is this GP? Also known as \cite{C0CP02833A}", } @InProceedings{Baun:2023:ICBIR, author = "Jonah Jahara Baun and Adrian Genevie Janairo and Ronnie Concepcion and Kate Francisco and Mike Louie Enriquez and R-Jay Relano and Joseph Aristotle {de Leon} and Argel Bandala and Ryan Rhay Vicerra and Jonathan Dungca", booktitle = "2023 8th International Conference on Business and Industrial Research (ICBIR)", title = "Hybrid Stochastic Genetic Evolution-Based Prediction Model of Received Input Voltage for Underground Imaging Applications", year = "2023", pages = "549--555", abstract = "The capacitive resistivity technique in underground object detection comprises configured transmitter and receiver antennas that are capacitively coupled to the ground. However, underground imaging lacks a basis for determining the received voltage for precise data analysis. This study aimed to develop a prediction model of the received input voltage signal amplitude from the ground of a single-pair antenna underground imaging system. The receiver antenna circuit for this application is designed and simulated in Proteus Software. Genetic Programming (GP) is applied to predict the received input signal based on the shape of the received waveform signal, operating frequency, resistance of the waveform shaping circuit, and buffer amplifier output signal. The resulting fitness function of GP (4) is acceptable as it scored an R2 of 99.38percent with a negligible MSE of 0.0059 and an MAE of 12.3423. Then, the GP fitness function is optimised through Genetic Algorithm (GA), Differential Evolution (DE), and Evolutionary Strategy (ES) in which the GP-GA model outperformed the two hybrid models providing fast convergence and 2.49e-8 best fitness value. This study proved that GP can be effectively combined with stochastic genetic evolution algorithms to avoid lengthy mathematical calculations and accurately estimate the natural voltage received from the ground.", keywords = "genetic algorithms, genetic programming, Resistance, Imaging, Stochastic processes, Receiving antennas, Voltage, Predictive models, Conductivity, capacitive resistivity technique, underground imaging, voltage prediction, stochastic genetic evolution", DOI = "doi:10.1109/ICBIR57571.2023.10147464", month = may, notes = "Also known as \cite{10147464}", } @InProceedings{Bautu:2006:mmelsp, author = "Andrei Bautu and Elena Bautu", title = "Meteorological Data Analysis and Prediction by Means of Genetic Programming", booktitle = "Proceedings of the Fifth Workshop on Mathematical Modelling of Environmental and Life Sciences Problems", year = "2006", pages = "35--42", address = "Constanta, Romania", month = sep, keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.613.3355", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.613.3355", broken = "http://www.ima.ro/mmelsp_series/mmelsp_08_papers/bautu01_08.pdf", size = "8 pages", abstract = "Weather systems use extremely complex combinations of mathematical tools for analysis and forecasting. Unfortunately, due to phenomena in the world climate, such as the greenhouse effect, classical models may become inadequate mostly because they lack adaptation. Therefore, the weather prediction problem is suited for heuristic approaches, such as Evolutionary Algorithms. Experimentation with heuristic methods like Genetic Programming (GP) can lead to the development of new insights or promising models that can be fine tuned with more focused techniques. This paper describes a GP approach for analysis and prediction of data and provides experimental results of the afore mentioned method on real-world meteorological time series.", } @Article{Bautu20071q, author = "Andrei Bautu and Elena Bautu", title = "Quantum Circuit Design By Means Of Genetic Programming", journal = "Romanian Journal of Physics", year = "2007", volume = "52", number = "5-7", pages = "697--704", publisher = "Romanian Academy Publishing House", address = "Bucharest, Romania", keywords = "genetic algorithms, genetic programming, quantum gates", ISSN = "1221-146X", URL = "https://rjp.nipne.ro/2007_52_5-7.html", URL = "http://www.nipne.ro/rjp/2007_52_5-6/0697_0705.pdf", size = "8 pages", abstract = "Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The efficiency of quantum computing is considered to become so significant that the study of quantum algorithms has attracted widespread interest. Development of quantum algorithms and circuits is difficult for a human researcher, so automatic induction of computer programs by means of genetic programming, which uses almost no auxiliary information on the search space, proved to be useful in generating new quantum algorithms. This approach takes advantage of the intrinsic parallelism of the genetic algorithm and quantum computing parallelism. The paper begins with a brief review on some basic concepts in genetic algorithms and quantum computation. Next, it describes an application of genetic programming for evolving quantum computing circuits.", notes = "S-expressions. Paper presented at the 7th International Balkan Workshop on Applied Physics, 5-7 July 2006, Constanta, Romania. http://www.nipne.ro/rjp/", } @InProceedings{conf:synasc:bautu2005, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "A {GEP}-based approach for solving {Fredholm} first kind integral equations", booktitle = "Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2005", year = "2005", pages = "325", month = sep, publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, gene expression programming, Fredholm integral equations, GEA, GEP approach, evolutionary techniques, first kind integral equations, gene expression algorithm, symbolic technique, Fredholm integral equations, evolutionary computation, symbol manipulation", ISBN = "0-7695-2453-2", DOI = "doi:10.1109/SYNASC.2005.6", size = "4 pages", abstract = "Evolutionary techniques have been widely accepted as an effective meta-heuristic for a wide variety of problems in different domains. The main purpose of this paper is to present a symbolic technique based on the evolutionary paradigm gene expression programming (GEP) for solving Fredholm first type integral equations. We present the main traits of the gene expression algorithm (GEA), and our implementation for solving first kind integral equations of Fredholm type. The results obtained on four model problems using the symbolic technique described in this paper prove it to be suitable to handle this class of problems.", } @InProceedings{Bautu:2005:SYNASC, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming", booktitle = "Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05)", year = "2005", pages = "321--324", keywords = "genetic algorithms, genetic programming, Gene Expression Programming", DOI = "doi:10.1109/SYNASC.2005.70", abstract = "regression on a finite sample of noisy data. The purpose is to obtain a mathematical model for data which is both reliable and valid, yet the analytical expression is not restricted to any particular form. To obtain a statistical model of the noisy data set we use symbolic regression with pseudo-random number generators. We begin by describing symbolic regression and our implementation of this technique using genetic programming (GP) and gene expression programming (GEP). We present some results for symbolic regression on computer generated and real financial data sets in the final part of this paper.", } @InProceedings{conf/synasc/BautuBL07, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "Ada{GEP} - An Adaptive Gene Expression Programming Algorithm", booktitle = "Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, {SYNASC} 2007", year = "2007", editor = "Viorel Negru and Tudor Jebelean and Dana Petcu and Daniela Zaharie", pages = "403--406", address = "Timisoara, Romania", month = sep # " 26-29", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-0-7695-3078-9", DOI = "doi:10.1109/SYNASC.2007.51", abstract = "Many papers focused on fine-tuning the gene expression programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size). This is probably due to the fact that the no free lunch theorem states that no fixed values for these parameters will ever suit all problems. To counteract this fact, this paper presents a modified GEP algorithm, called AdaGEP, which automatically adapts the number of genes used by the chromosome. The adaptation process takes place at chromosome level, allowing chromosomes in the population to evolve with different number of genes.", notes = "p406 'The results presented in this paper demonstrate the superiority of AdaGEP over GEP on symbolic regression problems.'", bibdate = "2008-11-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/synasc/synasc2007.html#BautuBL07", } @Article{Bautu20071, author = "Elena Bautu and Elena Pelican", title = "Numerical Solution For {Fredholm} First Kind Integral Equations Occurring In Synthesis of Electromagnetic Fields", journal = "Romanian Journal of Physics", year = "2007", volume = "52", pages = "245--256", number = "3-4", publisher = "Romanian Academy Publishing House", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Fredholm integral equations of the first kind, inverse problems", ISSN = "1221-146X", URL = "http://www.nipne.ro/rjp/2007_52_3-4.html", URL = "http://www.nipne.ro/rjp/2007_52_3-4/0245_0257.pdf", size = "12 pages", abstract = "It is known that Fredholm integral equations of the first kind with the kernel occur when solving with problems of synthesis of electrostatic and magnetic fields (m, n nonnegative rational numbers). This paper presents two approaches for solving such an equation. The first one involves discretisation by a collocation method and numerical solution using an approximate orthogonalisation algorithm. The second method is based on a nature inspired heuristic, namely genetic programming. It applies genetically-inspired operators to populations of potential solutions in the form of program trees, in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. Results obtained in experiments are presented for both approaches.", notes = "Paper presented at the 7th International Balkan Workshop on Applied Physics, 5-7 July 2006, Constanta, Romania. http://www.nipne.ro/rjp/ Cited by \cite{Sammany:2011:Computing}", } @InProceedings{Bautu:2008:SYNASC, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "An Evolutionary Approach for Modeling Time Series", booktitle = "10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC '08", year = "2008", month = sep, pages = "507--513", keywords = "genetic algorithms, genetic programming, change point detection, data generation process, evolutionary approach, genetic operator, time series modeling, time series", DOI = "doi:10.1109/SYNASC.2008.63", abstract = "Change points in time series appear due to variations in the data generation process. We consider the problem of modeling time series generated by dynamic processes, and we focus on finding the change points using a specially tailored genetic algorithm. The algorithm employs a new representation, described in detail in the paper. Suitable genetic operators are also defined and explained. The results obtained on computer generated time series provide evidence that the approach can be used for change point detection, and has good potential for time series modeling.", notes = "Also known as \cite{5204862}", } @Article{Bautu20081, author = "Elena Bautu and Elena Pelican", title = "Symbolic approach for the generalized airfoil equation", journal = "Creative Mathematics and Informatics", year = "2008", volume = "17", number = "2", pages = "52--60", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Generalised airfoil equation, Fredholm integral equation of the first kind, airfoil equation", ISSN = "1584-286X", URL = "https://www.creative-mathematics.cunbm.utcluj.ro/article/symbolic-approach-for-the-generalized-airfoil-equation/", URL = "https://www.creative-mathematics.cunbm.utcluj.ro/wp-content/uploads/2008_vol_17_2/creative_2008_17_2_052_060.pdf", broken = "http://creative-mathematics.ubm.ro/issues/down.php?f=creative_17_2008_no2_052_060.pdf", size = "6 pages", abstract = "The generalised airfoil equation governs the pressure across an airfoil oscillating in a wind tunnel. In this paper we analyse the problem for an airfoil with a flap, by means of Gene Expression Programming (GEP). We present the main traits of the GEP metaheuristic and then we define its elements in order to be used for integral equations of the first kind. The results obtained by our symbolic approach confirm the suitability of this method for problems modelled by Fredholm first kind integral equations.", notes = " https://www.creative-mathematics.cunbm.utcluj.ro/", } @InProceedings{conf/cisis/BautuBL10, author = "Elena Bautu and Andrei Bautu and Henri Luchian", title = "Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market", booktitle = "The Fourth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010)", address = "Krakow, Poland", month = "15-18 " # feb, year = "2010", editor = "Leonard Barolli and Fatos Xhafa and Salvatore Vitabile and Hui-Huang Hsu", isbn13 = "978-0-7695-3967-6", pages = "108--115", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1109/CISIS.2010.101", publisher = "IEEE Computer Society", bibdate = "2010-04-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cisis/cisis2010.html#BautuBL10", } @PhdThesis{bautu:thesis, author = "Elena Bautu", title = "Intelligent Techniques for Data Modeling Problems", school = "Al. I. Cuza University", year = "2010", address = "Iasi, Romania", month = jun, note = "Romanian subtitle is Programare genetica pentru probleme de optimizare in Inteligenta artificiala", keywords = "genetic algorithms, genetic programming, gene expression programming, inverse problems, financial forecasting, data analysis, hypernetwork, hybridization", URL = "https://sites.google.com/site/ebautu/home/publications/thesis/thesis_elena_bautu.pdf", broken = "https://sites.google.com/site/ebautu/home/publications/thesis", size = "220 pages", abstract = "Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models for continuous output and classification models (classiffiers) for discrete output. This thesis addresses both regression and classiffication problems, with an emphasis on new applications and on proposing new evolutionary techniques. First, we address the regression domain. Symbolic regression by means of evolutionary techniques is recommended when there is little or no a priori information on the modelled process. It relies on a set of input-output observations to infer mathematical models, posing no constraints on the structure, the coefficients or the size of the model. We introduce inverse problems modeled by Fredholm integral equations of the first kind and the inverse problem of log synthesis to be modelled by symbolic regression by means of gene expression programming. A new genetic programming scheme is formulated for the problem of automatically designing quantum circuits. An adaptive version of the gene expression programming algorithm is presented, which automatically tunes the complexity of the model by a gene (de)activation mechanism. For modelling time series produced by dynamic processes, we propose an evolutionary approach that uses a novel representation (and suitable genetic operators) to partition the time series based on change points. Empirical results prove the approach to be promising. Research on building classifiers for a given problem is also extensive, since there exists no best classifier at all tasks. The problem of predicting the direction of change of stock price on the market can be formulated as the search for a classifier that links past evolution to an increase or decrease. We explore new techniques for classification, in the context of predicting the direction of change of stock price, formulated as a binary classification", notes = "See also \cite{Bautu:book}", } @Book{Bautu:book, author = "Elena Bautu", title = "Intelligent Techniques for Data Modeling Problems: Nature inspired meta-heuristics and learning models applied to time series modeling and forecasting", publisher = "Lambert Academic Publishing", year = "2012", address = "Moldova", month = "20 " # mar, keywords = "genetic algorithms, genetic programming, Gene Expression Programming", isbn13 = "978-3-8484-3479-4", URL = "https://www.lap-publishing.com/catalog/details/store/ru/book/978-3-8484-3479-4/intelligent-techniques-for-data-modeling-problems?search=978-3-8484-3479-4", URL = "https://www.amazon.com/Intelligent-Techniques-Data-Modeling-Problems/dp/3848434792", size = "224 pages", abstract = "Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models (for continuous output attributes) and classification models (for discrete output attributes). This thesis addresses both regression and classification problems with an emphasis on new applications and on presenting improved evolutionary techniques. Such techniques include Gene Expression Programming (classical and its adaptive version), Genetic Programming, and the hypernetwork model of learning (classical and its evolutionary version). Such methods can be successfully applied to many problems from various domains. This thesis presents applications for symbolic regression for inverse problems, quantum circuit design, modeling of dynamic processes, and forecasting price movement.", } @Article{Bautu2012, author = "Elena Bautu and Alina Barbulescu", title = "A Hybrid Approach for Modelling Financial Time Series", year = "2012", journal = "The International Arab Journal of Information Technology (IAJIT)", volume = "9", number = "4", pages = "327--335", month = jul, keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Financial time series, forecasting, ARMA, GEP, and hybrid methodolog", ISSN = "1683-3198", URL = "http://www.ccis2k.org/iajit/PDF/vol.9,no.4/2806-5.pdf", size = "9 pages", abstract = "The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate different linear and non-linear patterns. ARMA models, although flexible, assume a linear form for the models. GEP evolves models adapting to the data without any restrictions with respect to the form of the model or its coefficients. Our approach benefits from the capability of ARMA to identify linear trends as well as GEP's ability to obtain models that capture nonlinear patterns from data. Investigations are performed on real data sets. They show a definite improvement in the accuracy of forecasts of the hybrid method over pure ARMA and GEP used separately. Experimental results are analysed and discussed. Conclusions and some directions for further research end the paper.", notes = "Zarqa Private University, Zarqa Jordan, iajit@ccis2k.org", } @InProceedings{Baxter:2024:GI, author = "Hunter Baxter and Yu Huang and Kevin Leach", title = "Genetic Improvement for {DNN} Security", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "11--12", address = "Lisbon", month = "16 " # apr, publisher = "ACM", note = "Best Presentation", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Computer Security, ANN", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/Genetic_Improvement_for_DNN_Security.pdf", DOI = "doi:10.1145/3643692.3648261", slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/leach-gi24.pdf", video_url = "https://www.youtube.com/watch?v=OXiFldz3b1U", video_url = "https://youtu.be/D2qLipAIAvE", video_url = "https://www.youtube.com/watch?v=D2qLipAIAvE&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=4&pp=iAQB", size = "2 pages", abstract = "Genetic improvement (GI) in Deep Neural Networks (DNNs) has traditionally enhanced neural architecture and trained DNN parameters. Recently, GI has supported large language models by optimising DNN operator scheduling on accelerator clusters. However, with the rise of adversarial AI, particularly model extraction attacks, there is an unexplored potential for GI in fortifying Machine Learning as a Service (MLaaS) models. We suggest a novel application of GI, not to improve model performance, but to diversify operator parallelism for the purpose of a moving target defence against model extraction attacks. We discuss an application of GI to create a DNN model defense strategy that uses probabilistic isolation, offering unique benefits not present in current DNN defense systems.", notes = "https://youtu.be/D2qLipAIAvE recording made at live at the event in Portugal, including Q and A. GI @ ICSE 2024, part of \cite{an:2024:GI}", } @Article{DBLP:journals/remotesensing/BayatNZB19, author = "Mahmoud Bayat and Phan Thanh Noi and Rozita Zare and Dieu Tien Bui", title = "A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of \emph{Fagus orientalis} in Northern Iran", journal = "Remote. Sens.", volume = "11", number = "14", pages = "1680", year = "2019", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3390/rs11141680", DOI = "doi:10.3390/rs11141680", timestamp = "Mon, 11 May 2020 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/remotesensing/BayatNZB19.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bayazidi:2014:MPiE, author = "Alireza Mohammadi Bayazidi and Gai-Ge Wang and Hamed Bolandi and Amir H. Alavi and Amir H. Gandomi", title = "Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete", journal = "Mathematical Problems in Engineering", year = "2014", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi", URL = "http://dx.doi.org/10.1155/2014/474289", DOI = "doi:10.1155/2014/474289", size = "10 pages", abstract = "This paper presents a new multigene genetic programming (MGGP) approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behaviour by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalised prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools.", notes = "Article ID 474289", } @InProceedings{Baydar:2000:GECCO, author = "Cem M. Baydar and Kazuhiro Saitou", title = "A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems", pages = "756", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW036.pdf", size = "1 page", abstract = "The advantages and performance of genetic programming in the use of error recovery planning in robotic assembly systems are presented. A framework is developed and coupled with a 3D robotic simulation software for the generation of error recovery logic in assembly systems to generate robust recovery programs in robot language itself. Performance of the system is evaluated with the simulations made on a three dimensionally modeled automated assembly line. The obtained results showed that the system is efficient of generating robust recovery plans for different error states.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{oai:CiteSeerPSU:538284, author = "Cem M. Baydar and Kazuhiro Saitou", title = "Off-Line Error Recovery Logic Synthesis in Automated Assembly Lines by using Genetic Programming", booktitle = "Proceedings Of The 2000 Japan/USA Symposium On Flexible Automation", year = "2000", editor = "Steven Y. Liang and Tatsuo Arai", address = "Ann Arbor, MI, USA", month = "23-26 " # jul, organisation = "ASME", email = "kazu@umich.edu", keywords = "genetic algorithms, genetic programming, Error Recovery Synthesis, Off-line Programming, Automated Assembly Lines", ISBN = "0-7918-1998-1", broken = "http://www-personal.engin.umich.edu/~cbaydar/japan-usa-00.pdf", URL = "http://citeseer.ist.psu.edu/538284.html", size = "8 pages", abstract = "Unexpected failures are one of the most important problems, which cause costly shutdowns in an assembly line. Generally the recovery process is done by the experts or automated error recovery logic controllers embedded in the system. The previous work in the literature is focused on the on-line recovery of the assembly lines which makes the process, time and money consuming. Therefore a novel approach is necessary which requires less time and hardware effort for the generation of error recovery logic. The proposed approach is based on three-dimensional geometric modelling of the assembly line coupled with the evolutionary computation techniques to generate error recovery logic in an off-line manner. The scope of this work is focused on finding an error recovery algorithm from a predefined error case. An automated assembly line is virtually modeled and the validity of the recovery algorithm is evaluated in a generate and test fashion by using a commercial software package. The obtained results showed that the developed framework is capable of generating recovery algorithms from an arbitrary part positioning error case. It is aimed that this approach will be coupled with the error generation in the future, providing efficient ways for the study of error recovery in automated assembly lines.", notes = "http://www.asme.org/divisions/med/enewsletter/2000oct/JapanUSAsymp.html http://members.asme.org/catalog/ItemView.cfm?ItemNumber=I464CD ASME Order #: I464CD", } @InProceedings{oai:CiteSeerPSU:535775, author = "Cem M Baydar and Kazuhiro Saitou", title = "Generation of Robust Recovery Logic in Assembly Systems using Multi-Level Optimization and Genetic Programming", booktitle = "Proceedings of DETC-00 ASME 2000 Design Engineering Technical Conferences and Computers and Information in Engineering Conference", year = "2000", address = "Baltimore, Maryland, USA", month = "10-13 " # sep, keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:87724; oai:CiteSeerPSU:467824; oai:CiteSeerPSU:161643", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:535775", rights = "unrestricted", URL = "http://citeseer.ist.psu.edu/535775.html", size = "8 pages", abstract = "Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, these errors are handled by human experts or logic controllers. However, these controller codes are based on anticipated error scenarios and are deficient in dealing with unforeseen situations. In our previous work (Baydar and Saitou, 2000a), an approach for the automated generation of error recovery logic was discussed. The method is based on three-dimensional geometric modeling of the assembly line to generate error recovery logic in an {"}off-line{"} manner using Genetic Programming. The scope of our previous work was focused on finding an error recovery algorithm from a predefined error case. However due to the geometrical features of the assembly lines, there may be cases which can be detected as the same type of error by the sensors. Therefore robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. In this paper, an extension of our previous study is presented to overcome this problera An assembly line is modeled and from the given error cases optimum way of error recovery is investigated using multi-level optimization. The obtained results showed that the infrastructure is capable of finding robust error recovery algorithms and multi-level optimization procedure improved the process. It is expected that the results of this study will be combined with the automatic error generation, resulting in efficient ways to automated error recovery logic synthesis.", notes = "not verified", } @InProceedings{Baydar:2001:ICRA, author = "Cem M. Baydar and Kazuhiro Saitou", title = "Off-line error prediction, diagnosis and recovery using virtual assembly systems", booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2001", year = "2001", volume = "1", pages = "818--823", address = "Seoul, Korea", month = "21-26 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, 3D model, Bayesian reasoning, Monte Carlo simulation, assembly line, automated assembly systems, error scenarios, peg-in-hole assembly, unexpected failures, virtual assembly systems, Bayes methods, Monte Carlo methods, assembling, fault diagnosis, industrial robots, inference mechanisms, robot programming", ISSN = "1050-4729", ISBN = "0-7803-6576-3", DOI = "doi:10.1109/ROBOT.2001.932651", size = "6 pages", abstract = "Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing on online diagnosing and recovering the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3D model of the assembly line to predict the possible errors in an offline manner. After that, these predicted errors can be diagnosed and recovered using Bayesian reasoning and genetic programming. A case study composed of a peg-in-hole assembly was performed and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtime of robotic assembly systems will be reduced.", notes = "GP creates code in RAPID language. Also known as \cite{932651}", } @PhdThesis{Baydar:thesis, author = "Cem Mehmet Baydar", title = "Off-line Error Prediction and Recovery Logic Synthesis using Virtual Assembly Systems", school = "The University of Michigan", year = "2001", address = "USA", keywords = "genetic algorithms, genetic programming, Applied sciences, Robotic assembly, Failure diagnosis, Off-line, Error prediction, Recovery logic, Virtual assembly", URL = "http://mirlyn.lib.umich.edu/Record/004198436", URL = "http://books.google.co.uk/books?id=fZMfAQAAMAAJ", URL = "http://search.proquest.com/docview/275835638", ISBN = "0-493-27766-8", size = "141 pages", abstract = "The advent of industrial robots has enabled large-scale automation in assembly lines with high productivity and minimum human intervention. However, growing complexity of robotic assembly systems makes them vulnerable to perturbations in process parameters, causing unexpected failures . Generally, the recovery process from this type of failures is carried out in a limited way by human experts or automated error recovery logic controllers embedded in the system. It is not possible to predict all failures and previous work in the literature focused on 'on-line' recovery of assembly lines when a failure occurs. Extensive downtime of a production system is costly and a failure recovery process that requires less time and hardware effort would be valuable. This dissertation offers a new approach for error prediction, diagnosis and recovery in assembly systems. It combines three-dimensional geometric model of assembly system with statistical distributions of process parameters and uses Monte Carlo simulation to predict possible failures, which may not be foreseen by human experts. The calculation of the likelihood of occurrence of each failure for a detected sensory symptom is achieved by Bayesian Reasoning and Genetic Programming is used to generate the requisite error recovery codes in an 'off-line' manner. The proposed approach is implemented and its validity is demonstrated in several case studies. Although main disadvantage was identified as costly computation time because of Monte Carlo simulation and Genetic Programming, two major advantages are expected to be achieved by this approach: Reducing lengthy ramp-up time for new systems (since most of pre-launch testing is debugging error recovery codes), and diagnosing and recovering unexpected errors accurately so that costly downtimes are reduced. Future work is suggested on the application of this method to manufacturing systems and exploration of a sampling algorithm which reduces the costly computation time of Monte Carlo simulation.", notes = "Something odd with 1.5 pdf seems ok only in some readers. UMI microfilm 3016800. Chair: Kazuhiro Saitou broken http://me.engin.umich.edu/news/pubs/ar/200209annualreportbw.pdf OCLC Number: 68913755", } @Article{Baydar200155, author = "Cem M. Baydar and Kazuhiro Saitou", title = "Automated generation of robust error recovery logic in assembly systems using genetic programming", journal = "Journal of Manufacturing Systems", volume = "20", number = "1", pages = "55--68", year = "2001", ISSN = "0278-6125", DOI = "doi:10.1016/S0278-6125(01)80020-0", URL = "http://www.sciencedirect.com/science/article/B6VJD-441R1H8-6/2/cdebaddb30a67a67dc7cb6dd41fabf9f", keywords = "genetic algorithms, genetic programming, robotics, Automated Assembly Systems, Error Recovery, Multi-Level Optimization", abstract = "Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done 'on-line' by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an 'off-line' manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a 'generate and test' fashion. The obtained results showed that with the improved convergence gained by using multi-level optimisation, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.", notes = "IRB6000 KAREL2, ROUTINE GPcode26, Move to POS, Move Relative...", } @Article{Baydar:2004:JIM, author = "Cem Baydar and Kazuhiro Saitou", title = "Off-line error prediction, diagnosis and recovery using virtual assembly systems", journal = "Journal of Intelligent Manufacturing", year = "2004", volume = "15", number = "5", pages = "679--692", month = oct, keywords = "genetic algorithms, genetic programming, Off-line programming, robotic assembly systems, virtual factories, error diagnosis and recovery", ISSN = "0956-5515", publisher = "Springer", DOI = "doi:10.1023/B:JIMS.0000037716.69868.d0", size = "14 pages", abstract = "Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly down times of robotic assembly systems will be reduced.", notes = "GP section 3.3. They generate error recovery code p688. linear chromosome Fig 4. Workspace Software. Pictures much better than \cite{Baydar:2001:ICRA}", } @InProceedings{Bayer:2021:GPTP, author = "Caleidgh Bayer and Ryan Amaral and Robert Smith and Alexandru Ianta and Malcolm Heywood", title = "Accelerating Tangled Program Graph Evolution under Visual Reinforcement Learning Tasks with Mutation and Multi-actions", booktitle = "Genetic Programming Theory and Practice XVIII", year = "2021", editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel", series = "Genetic and Evolutionary Computation", pages = "1--19", address = "East Lansing, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-16-8112-7", DOI = "doi:10.1007/978-981-16-8113-4_1", abstract = "Tangled Program Graphs (TPG) represents a genetic programming framework in which emergent modularity incrementally composes programs into teams of programs into graphs of teams of programs. To date, the framework has been demonstrated on reinforcement learning tasks with stochastic partially observable state spaces or time series prediction. However, evolving solutions to reinforcement tasks often requires agents to demonstrate/ juggle multiple properties simultaneously. Hence, we are interesting in maintaining a population of diverse agents. Specifically, agent performance on a reinforcement learning task controls how much of the task they are exposed to. Premature convergence might therefore preclude solving aspects of a task that the agent only later encounters. Moreover, pointless complexity may also result in which graphs largely consist of hitchhikers. In this research we benchmark the use of rampant mutation (multiple mutations applied simultaneously for offspring creation) and action programs (multiple actions per state). Several parameterizations are also introduced that potentially penalize the introduction of hitchhikers. Benchmarking over five VizDoom tasks demonstrates that rampant mutation reduces the likelihood of encountering pathologically bad offspring while action programs appears to improve performance in four out of five tasks. Finally, use of TPG parameterizations that actively limit the complexity of solutions appears to result in very efficient low dimensional solutions that generalize best across all combinations of 3, 4 and 5 VizDoom tasks.", notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022", } @Article{Baykasoglu:2004:CCR, author = "Adil Baykasoglu and Turkay Dereli and Serkan Tanis", title = "Prediction of cement strength using soft computing techniques", journal = "Cement and Concrete Research", year = "2004", volume = "34", pages = "2083--2090", number = "11", abstract = "we aim to propose prediction approaches for the 28-day compressive strength of Portland composite cement (PCC) by using soft computing techniques. Gene expression programming (GEP) and neural networks (NNs) are the soft computing techniques that are used for the prediction of compressive cement strength (CCS). In addition to these methods, stepwise regression analysis is also used to have an idea about the predictive power of the soft computing techniques in comparison to classical statistical approach. The application of the genetic programming (GP) technique GEP to the cement strength prediction is shown for the first time in this paper. The results obtained from the computational tests have shown that GEP is a promising technique for the prediction of cement strength.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6TWG-4CBVDJS-1/2/46a55d4141904806cf09f3c92f56beb4", month = nov, keywords = "genetic algorithms, genetic programming, Gene expression programming, Modelling, Compressive strength, Cement manufacture", DOI = "doi:10.1016/j.cemconres.2004.03.028", notes = " ", } @InProceedings{Baykasoglu:2005:ICRM, author = "Adil Baykasoglu", title = "Soft computing approaches to production line design", booktitle = "ICRM'2005 3rd International Conference on Responsive Manufacturing", year = "2005", editor = "Nabil Gindy", pages = "273--279", address = "Guangzhou, China", month = "12-14 " # sep, organisation = "University of Nottingham, Guangdong University of Technology", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Manufacturing system design, soft computing", URL = "http://delta.cs.cinvestav.mx/~ccoello/EMOO/baykasoglu05a.pdf.gz", size = "5 pages", abstract = "Gene Expression Programming (GEP) is used to develop a meta-model for the multiobjective design of a hypothetical production line. The developed meta-model is used to optimize production line design with Multiple Objective Tabu Search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to solve production line design problems which are known as complex design problems.", notes = "http://www.icrm2005.org/ broken Nov 2005 ", } @Article{Baykasoglu2007767, author = "Adil Baykasoglu and Lale Ozbakir", title = "MEPAR-miner: Multi-expression programming for classification rule mining", journal = "European Journal of Operational Research", volume = "183", number = "2", pages = "767--784", year = "2007", ISSN = "0377-2217", DOI = "DOI:10.1016/j.ejor.2006.10.015", URL = "http://www.sciencedirect.com/science/article/B6VCT-4MJS038-M/2/f780e675b2900eb28473dcbf6cfa03fb", keywords = "genetic algorithms, genetic programming, Data mining, Classification rules, Multi-expression programming, Evolutionary programming", abstract = "Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression Programming (MEP) which is named as MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed. Multi-Expression Programming (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP, multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and efficient manner. MEP is generally applied to prediction problems; in this paper a new algorithm is presented which enables MEP to discover classification rules. The performance of the developed algorithm is tested on nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules.", } @Article{Baykasoglu2008111, author = "Adil Baykasoglu and Hamza Gullu and Hanifi Canakci and Lale Ozbakir", title = "Prediction of compressive and tensile strength of limestone via genetic programming", journal = "Expert Systems with Applications", year = "2008", volume = "35", number = "1-2", pages = "111--123", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, multi expression programming, gene expression programming, Prediction, Limestone, Strength of materials", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2007.06.006", broken = "http://www.sciencedirect.com/science/article/B6V03-4NYJ0NK-1/2/00b6bf799aaf3df77a5e0fd846b85f20", abstract = "Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.", } @Article{Baykasoglu2008, author = "Adil Baykasoglu and Ahmet Oztas and Erdogan Ozbay", title = "Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches", journal = "Expert Systems with Applications", year = "2009", volume = "36", number = "3", pages = "6145--6155", month = apr, keywords = "genetic algorithms, genetic programming, gene expression programming, Multiple objective optimization, Meta-heuristics, Prediction, High-strength concrete", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2008.07.017", URL = "http://www.sciencedirect.com/science/article/B6V03-4T0WJSK-G/2/2dd2cbea4bb9a919e91f3953aecaaa06", ISSN = "0957-4174", size = "11 pages", abstract = "The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper.", } @Article{Baykasoglu:2009:ESA, author = "Adil Baykasoglu and Mustafa Gocken", title = "Gene expression programming based due date assignment in a simulated job shop", journal = "Expert Systems with Applications", year = "2009", volume = "36", pages = "12143--12150", number = "10", keywords = "genetic algorithms, genetic programming, Gene expression programming, Due date assignment", DOI = "doi:10.1016/j.eswa.2009.03.061", ISSN = "0957-4174", URL = "http://www.sciencedirect.com/science/article/B6V03-4VY2C6B-1/2/d174ebf2e7f0566d9c964be7d6f4f2ab", abstract = "In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods.", } @Article{Baykasoglu:2010:S, title = "Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop", author = "Adil Baykasoglu and Mustafa Gocken and Lale Ozbakir", journal = "Simulation", year = "2010", number = "12", volume = "86", pages = "715--728", keywords = "genetic algorithms, genetic programming, data mining, dispatching rules", DOI = "doi:10.1177/0037549709346561", size = "14 pages", bibdate = "2011-02-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/simulation/simulation86.html#BaykasogluGO10", abstract = "In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on simulated data.", } @Article{journals/jifs/BaykasogluM14, title = "Fuzzy functions via genetic programming", author = "Adil Baykasoglu and Sultan Maral", journal = "Journal of Intelligent and Fuzzy Systems", year = "2014", number = "5", volume = "27", pages = "2355--2364", keywords = "genetic algorithms, genetic programming", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jifs/jifs27.html#BaykasogluM14", URL = "http://dx.doi.org/10.3233/IFS-141205", } @Article{baykasoglu:2015:IJAMT, author = "Adil Baykasoglu and Lale Ozbakir", title = "Discovering task assignment rules for assembly line balancing via genetic programming", journal = "The International Journal of Advanced Manufacturing Technology", year = "2015", volume = "76", number = "1-4", pages = "417--434", keywords = "genetic algorithms, genetic programming, Assembly line balancing, Automatic rule generation, Evolutionary intelligence", URL = "http://link.springer.com/article/10.1007/s00170-014-6295-4", DOI = "doi:10.1007/s00170-014-6295-4", size = "18 pages", abstract = "Assembly line is one of the most commonly used manufacturing processes to produce final products in a flow line. Design of efficient assembly lines has considerable importance for the production of high-quantity standardized products. Several solution approaches such as exact, heuristic, and metaheuristics have been developed since the problem is first formulated. In this study, a new approach based on genetic programming so as to generate composite task assignment rules is proposed for balancing simple assembly lines. The proposed approach can also be applied to other types of line balancing problems. The present method makes use of genetic programming to discover task assignment rules which can be used within a single-pass constructive heuristic in order to balance a given assembly line quickly and effectively. Suitable parameters affecting the balance of the assembly line are evaluated and employed to discover highly efficient composite task assignment rules. Extensive computational results and comparisons proved the efficiency of the proposed approach in producing generic composite task assignment rules for balancing assembly lines.", } @Article{bayne:1997:ve, author = "Michael D. Bayne", title = "Vive l'evolution", journal = "Deep Magic", year = "1997", month = "12 " # feb, note = "www page", keywords = "genetic algorithms, genetic programming, Java, www", broken = "http://www.go2net.com/internet/deep/1997/02/12/", URL = "http://samskivert.com/internet/deep/1997/02/12/", size = "html 1 page", abstract = "Evolutionary computing is a blanket term encompassing a host of methodologies and philosophies, all based upon the premise that mother nature is darned good at solving problems. The world is literally crawling with problem solvers of infinite variety. Although Charles Darwin planted the idea in 1859 with the publication of The Origin of Species, the concept of mimicking mother nature's problem solving techniques didn't start to flower until the mid-1960s, when the computing power to actually investigate such techniques was readily available.", notes = "Quick overview of GP, ants GP java demo, http links to interesting places. Deep magic at http://www.go2net.com/internet/deep/ broken Feb 2012", } @Article{Baziar2011, author = "Mohammad H. Baziar and Yaser Jafarian and Habib Shahnazari and Vahid Movahed and Mohammad Amin Tutunchian", title = "Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach", journal = "Computer \& Geosciences", volume = "37", number = "11", pages = "1883--1893", year = "2011", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2011.04.008", URL = "http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2", keywords = "genetic algorithms, genetic programming, Liquefaction, Capacity energy, Sand, Silt, Wildlife", abstract = "Liquefaction is a catastrophic type of ground failure, which usually occurs in loose saturated soil deposits under earthquake excitations. A new predictive model is presented in this study to estimate the amount of strain energy density, which is required for the liquefaction triggering of sand-silt mixtures. A wide-ranging database containing the results of cyclic tests on sand-silt mixtures was first gathered from previously published studies. Input variables of the model were chosen from the available understandings evolved from the previous studies on the strain energy-based liquefaction potential assessment. In order to avoid over training, two sets of validation data were employed and a particular monitoring was made on the behaviour of the evolved models. Results of a comprehensive parametric study on the proposed model are in accord with the previously published experimental observations. Accordingly, the amount of strain energy required for liquefaction onset increases with increase in initial effective overburden pressure, relative density, and mean grain size. The effect of nonplastic fines on strain energy-based liquefaction resistance shows a more complicated behavior. Accordingly, liquefaction resistance increases with increase in fines up to about 10-15percent and then starts to decline for a higher increase in fines content. Further verifications of the model were carried out using the valuable results of some down hole array data as well as centrifuge model tests. These verifications confirm that the proposed model, which was derived from laboratory data, can be successfully used under field conditions.", } @Article{beade:2023:NC, author = "Angel Beade and Manuel Rodriguez and Jose Santos", title = "Evolutionary feature selection approaches for insolvency business prediction with genetic programming", journal = "Natural Computing", year = "2023", volume = "22", number = "4", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11047-023-09951-4", DOI = "doi:10.1007/s11047-023-09951-4", notes = "Business Department, University of A Coruna, Campus de Elvina, s/n, 15071, Spain", } @Article{BEADE:2024:knosys, author = "Angel Beade and Manuel Rodriguez and Jose Santos", title = "Variable selection in the prediction of business failure using genetic programming", journal = "Knowledge-Based Systems", volume = "289", pages = "111529", year = "2024", ISSN = "0950-7051", DOI = "doi:10.1016/j.knosys.2024.111529", URL = "https://www.sciencedirect.com/science/article/pii/S0950705124001643", keywords = "genetic algorithms, genetic programming, Business failure, Dimensionality reduction, Feature selection, Evolutionary computation", abstract = "This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes into account the relative frequency of occurrence of the explanatory variables in the evolved solutions, as well as the statistical relevance of that frequency. For a better evaluation of the proposed method and its comparison with other well-tested and widely used variable selection methods, the prediction of business failure in three temporal horizons (1, 5 and 9 years prior to failure) is considered. Additionally, a comparison of the sets of variables selected with different feature selection methods is performed, also considering different classifiers in the comparison, among which Genetic Programming is included as a classifier. The results indicate that the proposed method (using Genetic Programming as a variable selection method) is superior to the most tested and widely used methods analyzed, and this superiority increases if Genetic Programming is also used as a classification method", } @InProceedings{Beadle:2008:CEC, author = "Lawrence Beadle and Colin Johnson", title = "Semantically Driven Crossover in Genetic Programming", booktitle = "Proceedings of the IEEE World Congress on Computational Intelligence", year = "2008", pages = "111--116", editor = "Jun Wang", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, Program Semantics, Crossover, Reduced Ordered Binary Decision Diagrams", isbn13 = "978-1-4244-1823-7", file = "EC0044.pdf", DOI = "doi:10.1109/CEC.2008.4630784", URL = "http://results.ref.ac.uk/Submissions/Output/1423275", abstract = "Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments.", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", uk_research_excellence_2014 = "This paper was the first to introduce semantic methods in genetic programming. We show that by avoiding semantically-redundant crossover, the speed of learning by these algorithms can be accelerated. We provide a substantial analysis of results against other established methods, and statistical techniques are used to determine which problem types the new method works well on. We followed this with further semantic methods in papers for CEC2009 and GPEM Journal, 2009. This work has led to a number of papers by other research groups (e.g. UCD, TU Poznan) and a PhD at Dublin has extended the work.", } @Article{Beadle:2009:GPEM, author = "Lawrence Beadle and Colin G. Johnson", title = "Semantic Analysis of Program Initialisation in Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "3", pages = "307--337", month = sep, keywords = "genetic algorithms, genetic programming, Program initialisation, Program semantics, Program structure", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9082-5", abstract = "Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.", } @InProceedings{Beadle:2009:cec, author = "Lawrence Beadle and Colin G Johnson", title = "Semantically Driven Mutation in Genetic Programming", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "1336--1342", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P009.pdf", DOI = "doi:10.1109/CEC.2009.4983099", abstract = "Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark genetic programming problems over two different domains.", keywords = "genetic algorithms, genetic programming, Genetic programming, program semantics, semantically driven mutation, reduced ordered binary decision diagrams.", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @PhdThesis{Beadle:thesis, author = "Lawrence Charles John Beadle", title = "Semantic and Structural Analysis of Genetic Programming", school = "School of Computing, University of Kent", year = "2009", address = "Canterbury, UK", month = jul, keywords = "genetic algorithms, genetic programming, determinacy analysis, Craig interpolants", URL = "http://www.beadle.me/Me/LBeadle_PhD_Thesis.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=24&uin=uk.bl.ethos.509628", URL = "https://kar.kent.ac.uk/id/eprint/30599", URL = "http://www.cs.kent.ac.uk/pubs/2009/3056/", URL = "http://www.cs.kent.ac.uk/pubs/2009/3056/content.pdf", size = "194 pages", abstract = "Genetic programming (GP) is a subset of evolutionary computation where candidate solutions are evaluated through execution or interpreted execution. The candidate solutions generated by GP are in the form of computer programs, which are evolved to achieve a stated objective. Darwinian evolutionary theory inspires the processes that make up GP which include crossover, mutation and selection. During a GP run, crossover, mutation and selection are performed iteratively until a program that satisfies the stated objectives is produced or a certain number of time steps have elapsed. The objectives of this thesis are to empirically analyse three different aspects of these evolved programs. These three aspects are diversity, efficient representation and the changing structure of programs during evolution. In addition to these analyses, novel algorithms are presented in order to test theories, improve the overall performance of GP and reduce program size. This thesis makes three contributions to the field of GP. Firstly, a detailed analysis is performed of the process of initialisation (generating random programs to start evolution) using four novel algorithms to empirically evaluate specific traits of starting populations of programs. It is shown how two factors simultaneously effect how strong the performance of starting population will be after a GP run. Secondly, semantically based operators are applied during evolution to encourage behavioural diversity and reduce the size of programs by removing inefficient segments of code during evolution. It is demonstrated how these specialist operators can be effective individually and when combined in a series of experiments. Finally, the role of the structure of programs is considered during evolution under different evolutionary parameters considering different problem domains. This analysis reveals some interesting effects of evolution on program structure as well as offering evidence to support the success of the specialist operators.", notes = "EpochX GP software Supervisor: Colin Johnson uk.bl.ethos.509628", } @InProceedings{beale:2002:RTIC, author = "Stuart Beale", title = "Traffic Data: Less is More", booktitle = "Road Transport Information and Control", year = "2002", address = "Savoy Place, London, UK", month = "19-21 " # mar, organisation = "IEE", email = "rtic2002@iee.org.uk", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1049/cp:20020233", abstract = "In support of the Governments 10 Year Transport Plan the Highways Agency has an ambitious programme to roll-out traffic systems on the English motorway network. The control methodologies within these systems can be further developed which will help meet the Government's targets to reduce congestion and accidents. This paper describes three innovative projects being undertaken by the Highways Agency. The approach to these projects departs from the traditional engineering approach, instead we have used mathematical techniques to evolve control functions that learn and operate on the available traffic data.", notes = "RTIC 2002 http://conferences.iee.org.uk/RTIC/ For {"}genetic algorithm{"} read {"}genetic programming{"}", } @Article{ga:Beard93a, author = "Nick Beard", title = "The joy of genetic programming", journal = "Personal Computer World", year = "1993", pages = "471--472", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga_beard93a.pdf", size = "2 pages", notes = "overview/introduction", } @InProceedings{Bearpark:2000:ACDM, author = "K. Bearpark and A. J. Keane", title = "Short term memory in genetic programming", booktitle = "Fourth International Conference on Adaptive Computing in Design and Manufacture, ACDM '00", year = "2000", editor = "I. C. Parmee", pages = "309--320", address = "University of Plymouth, Devon, UK", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-85233-300-3", URL = "http://eprints.soton.ac.uk/21399/1/bear_00.pdf", URL = "http://eprints.soton.ac.uk/21399/", URL = "http://www.springer.com/engineering/mechanical+engineering/book/978-1-85233-300-3", URL = "http://www.amazon.co.uk/Evolutionary-Design-Manufacture-Selected-Papers/dp/1852333006", DOI = "doi:10.1007/978-1-4471-0519-0_25", size = "12 pages", abstract = "The recognition of useful information, its retention in memory, and subsequent use plays an important part in the behaviour of many biological species. Information gained by experience in one generation can be propagated to subsequent generations by some form of teaching. Each generation can then supplement its taught learning by its own experience. In this paper we explore the role of memorised information in the performance of a Genetic Programming (GP) system that uses a tree structure as its representation. Memory is implemented in the form of a set of subtrees derived from successful members of each generation. The memory is used by a genetic operator similar to the mutation operator but with the following difference. In a tree-structured system the mutation operator replaces randomly selected sub-trees by new randomly-generated sub-trees. The memory operator replaces randomly selected sub-trees by sub-trees randomly randomly selected from the memory. To study the memory operator's impact a GP system is used to evolve a well-known expression from classical kinetics using fitness-based selection. The memory operator is used together with the common crossover and mutation operators. It is shown that the addition of a memory operator increases the probability of a successful evolution for this particular problem. At this stage we make no claim for its impact on other problems that have been successfully addressed by Genetic Programming", notes = "Evolutionary Design and Manufacture: Selected Papers from . (ACDM '00) One example physics integration of u*t+0.5*a*t*t t=1...10, u=20 or u=200 a=980 Reverse Polish RPN except for first (in Lisp) max length=11?? 19??, roulette wheel, crossover, mutation. Memory operator: when fitness improves over best of previous generation whole of tree and its subtrees are saved in memory. Later random choices from memory. elitism.Pop=2000, gen=20 40000 tests per minute (300 MHz).", } @PhdThesis{Bearpark:thesis, author = "Keith Bearpark", title = "Learning and memory in genetic programming", school = "School of Engineering Sciences, University of Southampton", year = "2000", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://eprints.soton.ac.uk/45930/", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327359", abstract = "Genetic Programming is a form of Evolutionary Computation in which computer programs are evolved by methods based on simulating the natural evolution of biological species. A new generation of a species acquires the characteristics of previous generations through the inheritance of genes by sexual reproduction and through random changes in alleles by random mutation. The new generation may enhance its ability to survive by the acquisition of cultural knowledge through learning processes. This thesis combines the transfer of knowledge by genetic means with the transfer of knowledge by cultural means. In particular, it introduces a new evolutionary operator, memory operator. In conventional genetic programming systems, a new generation is formed from a mating pool whose members are selected from the fittest members of previous generation. The new generation is produced by the exchange of genes between members of the mating pool and the random replacement of genes in the offspring. The new generation may or may not be able to survive better than its predecessor in a given environment. The memory operator augments the evolutionary process by inserting into new chromosomes genetic material known to often result in fitness improvements. This material is acquired through a learning process in which the system is required to evolve generations that survive in a less demanding environment. The cultural knowledge acquired in this learning process is applied as an intelligent form of mutation to aid survival in a more demanding environment.", notes = "uk.bl.ethos.327359", } @InProceedings{beaulieu:2002:gecco, author = "Julie Beaulieu and Christian Gagn{\'e} and Marc Parizeau", title = "Lens System Design And Re-engineering With Evolutionary Algorithms", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "155--162", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, evolvable hardware, evolutionary reengineering, evolvable optics, lens system design", URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/gecco02-lens.pdf", URL = "http://vision.gel.ulaval.ca/en/publications/Id_44/PublDetails.php", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/EH274.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-04.pdf", URL = "http://www.gel.ulaval.ca/~cgagne/pubs/lens-gecco02.pdf", URL = "http://citeseer.ist.psu.edu/532763.html", size = "8 pages", abstract = "presents some lens system design and re-engineering experimentations with genetic algorithms and genetic programming. These Evolutionary Algorithms (EA) were successfully applied to a design problem that was previously presented to expert participants of an international lens design conference. Comparative results demonstrate that the use of EA for lens system design is very much human-competitive.", ISBN = "1-55860-878-8", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{Beaumont:2009:cec, author = "Darren Beaumont and Susan Stepney", title = "Grammatical Evolution of L-systems", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2446--2453", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P007.pdf", DOI = "doi:10.1109/CEC.2009.4983247", abstract = "L-systems are parallel generative grammars that can model branching structures. Taking a graphical object and attempting to derive an L-system describing it is a hard problem. Grammatical Evolution (GE) is an evolutionary technique aimed at creating grammars describing the legal structures an object can take. We use GE to evolve L-systems, and investigate the effect of elitism, and the form of the underlying grammar.", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Bechmann:2010:ICES, author = "Matthias Bechmann and Angelika Sebald and Susan Stepney", title = "From Binary to Continuous Gates - and Back Again", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "335--347", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", isbn13 = "978-3-642-15322-8", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.386.7390", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.7390", URL = "http://www-users.cs.york.ac.uk/~susan/bib/ss/nonstd/ices10.pdf", DOI = "doi:10.1007/978-3-642-15323-5_29", abstract = "We describe how nuclear magnetic resonance (NMR) spectroscopy can serve as a substrate for the implementation of classical logic gates. The approach exploits the inherently continuous nature of the NMR parameter space. We show how simple continuous NAND gates with sin/sin and sin/sinc characteristics arise from the NMR parameter space. We use these simple continuous NAND gates as starting points to obtain optimised target NAND circuits with robust, error-tolerant properties. We use Cartesian Genetic Programming (CGP) as our optimisation tool. The various evolved circuits display patterns relating to the symmetry properties of the initial simple continuous gates. Other circuits, such as a robust XOR circuit built from simple NAND gates, are obtained using similar strategies. We briefly mention the possibility to include other target objective functions, for example other continuous functions. Simple continuous NAND gates with sin/sin characteristics are a good starting point for the creation of error-tolerant circuits whereas the more complicated sin/sinc gate characteristics offer potential for the implementation of complicated functions by choosing some straightforward, experimentally controllable parameters appropriately.", affiliation = "Department of Chemistry, University of York, YO10 5DD UK", } @Article{Beck:2014:PLoSONE, author = "Daniel Beck and James A. Foster", title = "Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics", journal = "PLoS ONE", year = "2014", volume = "9", number = "2", pages = "e87830", month = feb # " 3", keywords = "genetic algorithms, genetic programming, Bacterial vaginosis, Microbiome, Lactobacillus, Vagina, Community ecology, Machine learning algorithms", keywords = "genetic algorithms, genetic programming", bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov", publisher = "Public Library of Science", oai = "oai:pubmedcentral.nih.gov:3912131", URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912131", URL = "http://dx.doi.org/10.1371/journal.pone.0087830", DOI = "doi:10.1371/journal.pone.0087830", size = "8 pages", abstract = "Microbial communities are important to human health. Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. While the causes of BV are unknown, the microbial community in the vagina appears to play a role. We use three different machine-learning techniques to classify microbial communities into BV categories. These three techniques include genetic programming (GP), random forests (RF), and logistic regression (LR). We evaluate the classification accuracy of each of these techniques on two different datasets. We then deconstruct the classification models to identify important features of the microbial community. We found that the classification models produced by the machine learning techniques obtained accuracies above 90percent for Nugent score BV and above 80percent for Amsel criteria BV. While the classification models identify largely different sets of important features, the shared features often agree with past research.", notes = "16 S rRNA, Random Forests, Logistic Regression. pre-select 15 features. R package glmnet, lasso. ROC. pop15000 14 functions in function set. PMID:24498380", } @PhdThesis{Beck:thesis, author = "Daniel Beck", title = "Investigating the use of classification models to study microbial community associations with bacterial vaginosis", school = "University of Idaho", year = "2014", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, grammatical evolution, Linear GP, Push, Bioinformatics, Biology", URL = "https://www.lib.uidaho.edu/digital/etd/items/beck_idaho_0089e_10212.html", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/daniel_beck_dissertation.pdf", size = "82 pages", abstract = "Microbial communities are highly complex, often composed of hundreds or thousands of different microbe types. They are found nearly everywhere; in soil, water, and in close association with other organisms. Microbial communities are difficult to study. Many microbes are not easily grown in laboratory conditions. Interactions between microbes may limit the applicability of observations collected using isolated taxa. However, new sequencing technology is allowing researchers to study microbial communities in novel ways. Among these new techniques is 16S rRNA fingerprinting, which enables researchers to estimate the relative abundance of most microbes in the community. These techniques are often used to study microbial communities living on or in the human body. These microbiomes are found at many different body sites and have been linked to the health of their human host. In particular, the vagina microbiome has been linked to bacterial vaginosis (BV). BV is highly prevalent with symptoms including odour, discharge, and irritation. While no single microbe has been shown to cause BV, the structure of the microbial community as a whole is associated with BV. In this thesis, I explore methods that may be used to discover associations between microbial communities and phenotypes of those communities. I focus on associations between the vagina microbiome and BV. The first two chapters of this thesis describe software tools used to explore and visualise ecological datasets. In the last two chapters, I explore the use of machine learning techniques to model the relationships between the vagina microbiome and BV. Machine learning techniques are able to produce complex models that classify microbial communities by BV characteristics. These models may capture interactions that simpler models miss.", notes = "Beck_idaho_0089E_10212 Supervisor James A Foster", } @Article{Beck:2015:BDM, author = "Daniel Beck and James A. Foster", title = "Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis", journal = "BioData Mining", year = "2015", volume = "8", number = "23", month = "12 " # aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1186/s13040-015-0055-3", size = "9 pages", abstract = "Background: Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. It is highly prevalent and is characterized by symptoms including odour, discharge and irritation. No single microbe has been found to cause BV. In this paper we use random forests and logistic regression classifiers to model the relationship between the microbial community and BV. We use subsets of the microbial community features in order to determine which features are important to the classification models. Results: We find that models generated using logistic regression and random forests perform nearly identically and identify largely similar important features. Only a few features are necessary to obtain high BV classification accuracy. Additionally, there appears to be substantial redundancy between the microbial community features. Conclusions: These results are in contrast to a previous study in which the important features identified by the classifiers were dissimilar. This difference appears to be the result of using different feature importance measures. It is not clear whether machine learning classifiers are capturing patterns different from simple correlations.", } @InProceedings{beck:1999:EAM, author = "M. A. Beck and I. C. Parmee", title = "Extending the bounds of the search space: A Multi-Population approach", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1469--1476", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-762.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-762.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Misc{oai:arXiv.org:cs/0212019, title = "Thinking, Learning, and Autonomous Problem Solving", author = "Joerg D. Becker", year = "2002", month = dec # "~10", abstract = "Ever increasing computational power will require methods for automatic programming. We present an alternative to genetic programming, based on a general model of thinking and learning. The advantage is that evolution takes place in the space of constructs and can thus exploit the mathematical structures of this space. The model is formalized, and a macro language is presented which allows for a formal yet intuitive description of the problem under consideration. A prototype has been developed to implement the scheme in PERL. This method will lead to a concentration on the analysis of problems, to a more rapid prototyping, to the treatment of new problem classes, and to the investigation of philosophical problems. We see fields of application in nonlinear differential equations, pattern recognition, robotics, model building, and animated pictures.", note = "Comment: 9 pages, 4 figures", oai = "oai:arXiv.org:cs/0212019", URL = "http://arXiv.org/abs/cs/0212019", size = "27702 bytes", } @InProceedings{Becker:2021:GECCOcomp, author = "Kory Becker and Justin Gottschlich", title = "{AI} Programmer: Autonomously Creating Software Programs Using Genetic Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2021", pages = "1513--1521", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, machine learning, evolutionary computation, artificial intelligence, genetic algorithm, program synthesis, code generation and optimization, programming languages, Brain-dead", isbn13 = "978-1-4503-8351-6", DOI = "doi:10.1145/3449726.3463125", size = "8 pages", abstract = "we present AI Programmer, a machine learning (ML) system that can automatically generate full software programs, while requiring only minimal human guidance. At its core, AI Programmer uses a genetic algorithm (GA), coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer's novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of classic GA to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer's system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs.", notes = "Bloomberg See also: BF-Programmer: A Counterintuitive Approach to Autonomously Building Simplistic Programs Using Genetic Algorithms Also known as \cite{10.1145/3449726.3463125} GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @TechReport{becker:2003-09, author = "Lee A. Becker and Mukund Seshadri", title = "Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules", institution = "Worcester Polytechnic Institute", year = "2003", month = may, email = "mukund@cs.wpi.edu", keywords = "genetic algorithms, genetic programming, comprehensibility , Occam's razor, overfitting, complexity penalising, S&P500, technical analysis, market timing", URL = "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-09.pdf", URL = "http://citeseer.ist.psu.edu/574013.html", abstract = "This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.", } @TechReport{becker:2003-15, author = "Lee A. Becker and Mukund Seshadri", title = "Cooperative Coevolution of Technical Trading Rules", institution = "Worcester Polytechnic Institute", year = "2003", month = may, email = "mukund@cs.wpi.edu", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-15.pdf", abstract = "This paper describes how cooperative coevolution can be used for GP of technical trading rules. A number of different methods of choosing collaborators for fitness evaluation are investigated. Several of the methods outperformed, at a statistically significant level, a buy-and-hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs.", } @InProceedings{becker:2003:CINC, author = "Lee A. Becker and Mukund Seshadri", title = "GP-evolved Technical Trading Rules Can Outperform Buy and Hold", booktitle = "Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing", year = "2003", address = "Embassy Suites Hotel and Conference Center, Cary, North Carolina USA", month = sep # " 26-30", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/W.Yan/gp-evolved-technical-trading.pdf", size = "4 pages", abstract = "This paper presents a number of experiments in which GP-evolved technical trading rules outperform a buy-and-hold strategy on the S&P500, even taking into account transaction costs. Several methodology changes from previous work are discussed and tested. These include a complexity-penalising factor, a fitness function that considers consistency of performance, and coevolution of a separate buy and sell rule.", notes = "http://axon.cs.byu.edu/Dan/cinc03.html/index.html/ Broken Jan 2013 http://www.ee.duke.edu/JCIS/ Worcester Polytechnic Institute", } @Misc{Becker:2020:TPOT, author = "Nick Becker and Dante Gama Dessavre and John Zedlewski", title = "{Faster AutoML with TPOT and RAPIDS}", howpublished = "www blog", year = "2020", month = nov # " 5", keywords = "genetic algorithms, genetic programming, TPOT, AutoML, GPU, Data Science, Machine Learning, Artificial Intelligence, AI, Big Data, Python, Higgs Boson, Airline delays", URL = "https://medium.com/rapids-ai/faster-automl-with-tpot-and-rapids-758455cd89e5", video_url = "https://youtu.be/7z4OJQdY_mw", size = "6 min read", notes = "youtu.be seems to have lost sound track NVIDIA dual Intel Xeon Platinum 8168 CPUs and one NVIDIA V100 GPU See also https://developer.nvidia.com/blog/advancing-the-state-of-the-art-in-automl-now-10x-faster-with-nvidia-gpus-and-rapids/ Jun 09, 2021 By Carol McDonald, Nick Becker and Nick Erickson", } @InCollection{Becker:2006:GPTP, author = "Ying Becker and Peng Fei and Anna M. Lester", title = "Stock Selection : An Innovative Application of Genetic Programming Methodology", booktitle = "Genetic Programming Theory and Practice {IV}", year = "2006", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", volume = "5", series = "Genetic and Evolutionary Computation", pages = "315--334", address = "Ann Arbor", month = "11-13 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, equity market, stock selection, quantitative asset management Capital Asset Pricing Model, Arbitrage Pricing Model, Technical trading rules, S&P 500, Stock selection models, Information ratio, Information coefficient, Quantitative asset management", ISBN = "0-387-33375-4", DOI = "doi:10.1007/978-0-387-49650-4_19", size = "16 pages", abstract = "One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA's previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model.", notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop Principal, Head of US Active Equity Research, Advanced Research Center, State Street Global Advisors, Boston, MA 02111", } @InCollection{Becker:2007:GPTP, author = "Ying L. Becker and Harold Fox and Peng Fei", title = "An Empirical Study of Multi-Objective Algorithms for Stock Ranking", booktitle = "Genetic Programming Theory and Practice {V}", year = "2007", editor = "Rick L. Riolo and Terence Soule and Bill Worzel", series = "Genetic and Evolutionary Computation", chapter = "14", pages = "239--259", address = "Ann Arbor", month = "17-19" # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0-387-76308-8", DOI = "doi:10.1007/978-0-387-76308-8_14", size = "21 pages", abstract = "Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts of financial data. Genetic programming using simple objective fitness functions has been shown to be an effective technique for selecting factors and constructing multi-factor models for ranking stocks, but the resulting models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess returns that are consistent across time and the cross-sectional dimensions of the investment universe. In this study, we implement and evaluate three multi-objective algorithms to simultaneously optimise the information ratio, information coefficient, and intra-fractile hit rate of a portfolio. These algorithms the constrained fitness function, sequential algorithm, and parallel algorithm take widely different approaches to combine these different portfolio metrics. The results show that the multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi-objective algorithms. Moreover, this algorithm generalises to the held-out test data set much better than any of the single fitness algorithms.", affiliation = "Advanced Research Center, State Street Global Advisors Boston MA 02111", notes = "part of \cite{Riolo:2007:GPTP} published Jan 2008", } @InProceedings{BeckerO:2009:GEC, author = "Ying L. Becker and Una-May O'Reilly", title = "Genetic programming for quantitative stock selection", booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation", year = "2009", editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding", bibsource = "DBLP, http://dblp.uni-trier.de", pages = "9--16", address = "Shanghai, China", organisation = "SigEvo", DOI = "doi:10.1145/1543834.1543837", publisher = "ACM", publisher_address = "New York, NY, USA", month = jun # " 12-14", isbn13 = "978-1-60558-326-6", keywords = "genetic algorithms, genetic programming", abstract = "We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by experts. We describe the multi-stage training, testing and validation process that we have integrated with GP selection to be appropriate for financial panel data and how the GP solutions are situated within a portfolio selection strategy. We share our experience with the pros and cons of evolved linear and non-linear models, and outline how we have used GP extensions to balance different objectives of portfolio managers and control the complexity of evolved models.", notes = "Also known as \cite{DBLP:conf/gecco/BeckerO09} part of \cite{DBLP:conf/gec/2009}", } @InCollection{Bedner:1997:elca, author = "Ilja Bedner", title = "Evolving Light Cycle Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming, games", ISBN = "0-18-205981-2", abstract = "Evolution of autonomous agents that must compete for survival in the light-cycle game as seen in the movie tron", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{Beham:2008:ieeeIPDPS, author = "Andreas Beham and Stephan Winkler and Stefan Wagner and Michael Affenzeller", title = "A genetic programming approach to solve scheduling problems with parallel simulation", booktitle = "IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008", year = "2008", month = apr, pages = "1--5", keywords = "genetic algorithms, genetic programming, dispatching, fitness evaluation, parallel simulation, production planning, scheduling problem, dispatching, production planning, scheduling", DOI = "doi:10.1109/IPDPS.2008.4536379", DOI = "doi:10.1109/IPDPS.2008.4536362", ISSN = "1530-2075", abstract = "Scheduling and dispatching are two ways of solving production planning problems. In this work, based on preceding works, it is explained how these two approaches can be combined by the means of an automated rule generation procedure and simulation. Genetic programming is applied as the creator and optimizer of the rules. A simulator is used for the fitness evaluation and distributed over a number of machines. Some example results suggest that the approach could be successfully applied in the real world as the results are more than human competitive.", notes = "Also known as \cite{4536379} \cite{4536362}", } @InProceedings{3360, author = "Andreas Beham and Erik Pitzer and Michael Affenzeller", title = "Fitness Landscape based Parameter Estimation for Robust Taboo Search", booktitle = "Computer Aided Systems Theory, Eurocast 2013", year = "2013", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "8111", series = "LNCS", pages = "292--299", address = "Las Palmas, Spain", month = "10-15 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Problem Instance, Problem Size, Fitness Landscape, Quadratic Assignment Problem, Large Problem Size", isbn13 = "978-3-642-53856-8", URL = "https://link.springer.com/chapter/10.1007/978-3-642-53856-8_37", DOI = "doi:10.1007/978-3-642-53856-8_37", abstract = "Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behaviour [1]. Adjusting these parameters allows to increase the algorithms performances with respect to different problem- and problem instance characteristics.", } @InProceedings{Beham:2016:OKC:2908961.2931724, author = "Andreas Beham and Stefan Wagner and Michael Affenzeller", title = "Optimization Knowledge Center: A Decision Support System for Heuristic Optimization", booktitle = "Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion", year = "2016", series = "GECCO '16 Companion", pages = "1331--1338", address = "New York, NY, USA", publisher = "ACM", acmid = "2931724", DOI = "doi:10.1145/2908961.2931724", isbn13 = "978-1-4503-4323-7", keywords = "decision-support-system, heuristic optimization, knowledge base", location = "Denver, Colorado, USA", numpages = "8", URL = "http://doi.acm.org/10.1145/2908961.2931724", keywords = "genetic algorithms, genetic programming", } @Article{BEHBAHANI:2020:CBM, author = "Hamid Behbahani and Gholam Hossein Hamedi and Vahid {Najafi Moghaddam Gilani}", title = "Predictive model of modified asphalt mixtures with nano hydrated lime to increase resistance to moisture and fatigue damages by the use of deicing agents", journal = "Construction and Building Materials", volume = "265", pages = "120353", year = "2020", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2020.120353", URL = "http://www.sciencedirect.com/science/article/pii/S0950061820323588", keywords = "genetic algorithms, genetic programming, Asphalt mixture, Moisture susceptibility, Fatigue life, Surface free energy, Deicing agents, Nano hydrated lime, Two-objective optimization", abstract = "Deicing agents are used to dissolving the frost on road surfaces in winter and cold areas. Researchers have evaluated the impact of different deicing agents on the moisture susceptibility performance of asphalt mixtures, but they have not investigated the effect of these agents on fatigue failure and thermodynamic parameters of asphalt mixtures. Therefore, in this research, by investigating the effect of two new deicing agents of calcium magnesium acetate (CMA) and potassium acetate (PA) as well as sodium chloride (NaCl) traditional agent on moisture and fatigue performances of asphalt mixtures, predictive model of the tensile strength ratio (TSR) and the fatigue life ratio (NFR) using genetic programming (GP) based on the surface free energy (SFE) components and other properties of asphalt mixtures were presented. Nano hydrated lime (NHL) was applied as an asphalt binder modifier and an anti-stripping agent to improve the strength of asphalt mixtures. The results indicated that the saturated mixtures in CMA had the highest indirect tensile strength (ITS) and fatigue life in lower freeze-thaw cycles, while the NaCl-saturated samples had more ITS and fatigue life in higher cycles. The CMA-saturated samples had the greatest TSR and NFR. Using NHL in all saturated samples resulted in increasing TSR and NFR values. Results of SFE method showed that using NHL increased the polar, non-polar and basic components of asphalt binders and decreased their acidic components. Also, using NHL increased the total SFE amount of asphalt binder, enhancing the adhesion of aggregate and asphalt binder and cohesion in asphalt binder membrane, and as a result, improving the moisture resistance in asphalt mixtures. Using 1.5percent NHL had the greatest effect on improving adhesion free energy (AFE), cohesion free energy (CFE) and detachment energy (DE). Among deicing solutions, CMA had the highest CFE, in general, and NaCl had the best DE values. PA-saturated samples had the greatest permeability of asphalt mixture (PAM) values. GP model had a high R2 96.4percent and 98.3percent for TSR and NFR, respectively. Using GP model to achieve the maximum TSR and NFR, the Pareto curve showed that 1.32percent NHL was the optimum value for simultaneously increasing moisture resistance and fatigue life", } @Article{Behbahani:2012:transMechtron, author = "Saeed Behbahani and Clarence W. {de Silva}", title = "Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming", journal = "IEEE/ASME Transactions on Mechatronics", year = "2013", volume = "18", number = "1", pages = "190--199", month = feb, keywords = "genetic algorithms, genetic programming, Bond graphs, electrohydraulic systems", ISSN = "1083-4435", DOI = "doi:10.1109/TMECH.2011.2165958", size = "10 pages", abstract = "A typical mechatronic problem (modelling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and genetic programming (GP) for design optimisation of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological realisation of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic design problems.", notes = "Also known as \cite{6029337}", } @Article{Behbahani:2013:Mechatronics, author = "Saeed Behbahani and Clarence W. {de Silva}", journal = "IEEE/ASME Transactions on Mechatronics", title = "Niching Genetic Scheme With Bond Graphs for Topology and Parameter Optimization of a Mechatronic System", year = "2014", month = feb, volume = "19", number = "1", pages = "269--277", ISSN = "1083-4435", abstract = "This paper presents a novel multimodal evolutionary optimisation algorithm for the complex problem of concurrent and integrated design of a mechatronic system, with the objective of realising the best topology and the best parameters from a multicriteria viewpoint and with different preferences. The associated search space can be large and complex due to the existence of different classes of configurations, possible topologies, and the parameter values of the elements. The proposed algorithm efficiently explores the search space to find several elite configurations for different preferences, with more detailed competition by incorporating the domain knowledge of experts and considering some criteria that are not included in the course of regular evolutionary optimisation. The developed approach consists of a two-loop optimisation. For each topology, a genetic algorithm-based optimisation is performed to find an elite representative of the topology. The elites will compete with each other to become the best design. A strategy of restricted competition selection is employed in the competition of topologies, with the aim of finding alternative elites from which the one that best satisfies the customer preference may be chosen. The designer may incorporate a higher level competition between elites in order to obtain the global optimum.", keywords = "genetic algorithms, genetic programming, Bond graphs, car suspension, genetic algorithms (GAs), genetic programming (GP), niching genetic schemes, skyhook", DOI = "doi:10.1109/TMECH.2012.2230013", ISSN = "1083-4435", notes = "Also known as \cite{6389779}", } @InCollection{beheler:1995:UGACFOSGPI, author = "Joey Beheler", title = "Using Genetic Algorithms and Convolution to Find Optimal Strategies in Games without Perfect Information", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "11--18", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @Article{Behera:2012:ACEEijcsi, author = "R. Behera and B. B. Pati and B. P. Panigrahi and S. Misra", title = "An Application of Genetic Programming for Power System Planning and Operation", journal = "ACEEE International Journal on Control System and Instrumentation", year = "2012", volume = "3", number = "2", pages = "15--20", month = mar, note = "Special Issue", keywords = "genetic algorithms, genetic programming Computer Aided Engineering, Mutation, Fitness Function", ISSN = "2158-0006", broken = "http://searchdl.org/index.php/journals/journalList/1", searchdl = "ID: 01.IJCSI.3.2.59", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", language = "ENG", oai = "oai:hal.archives-ouvertes.fr:hal-00741655", broken = "http://hal.archives-ouvertes.fr/hal-00741655", URL = "http://hal.archives-ouvertes.fr/docs/00/74/16/55/PDF/59.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.7439", size = "6 pages", abstract = "This work incorporates the identification of model in functional form using curve fitting and genetic programming technique which can forecast present and future load requirement. Approximating an unknown function with sample data is an important practical problem. In order to forecast an unknown function using a finite set of sample data, a function is constructed to fit sample data points. This process is called curve fitting. There are several methods of curve fitting. Interpolation is a special case of curve fitting where an exact fit of the existing data points is expected. Once a model is generated, acceptability of the model must be tested. There are several measures to test the goodness of a model. Sum of absolute difference, mean absolute error, mean absolute percentage error, sum of squares due to error (SSE), mean squared error and root mean squared errors can be used to evaluate models. Minimising the squares of vertical distance of the points in a curve (SSE) is one of the most widely used method .Two of the methods has been presented namely Curve fitting technique and Genetic Programming and they have been compared based on (SSE)sum of squares due to error.", notes = "broken April 2019 http://ijcsi.theaceee.org/", } @Article{Beiki20101091, author = "Morteza Beiki and Ali Bashari and Abbas Majdi", title = "Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "47", number = "7", pages = "1091--1103", year = "2010", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2010.07.007", URL = "http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4", keywords = "genetic algorithms, genetic programming, Deformation modulus of rock mass, Relative strength of effect (RSE), Sensitivity analysis about the mean", abstract = "We use genetic programming (GP) to determine the deformation modulus of rock masses. A database of 150 data sets, including modulus of elasticity of intact rock (Ei), uniaxial compressive strength (UCS), rock mass quality designation (RQD), the number of joint per meter (J/m), porosity, and dry density for possible input parameters, and the modulus deformation of the rock mass determined by a plate loading test for output, was established. The values of geological strength index (GSI) system were also determined for all sites and considered as another input parameter. Sensitivity analyses are considered to find out the important parameters for predicting of the deformation modulus of rock mass. Two approaches of sensitivity analyses, based on statistical analysis of RSE values and sensitivity analysis about the mean, are performed. Evolution of the sensitivity analyses results establish the fact that variable of UCS, GSI, and RQD play more prominent roles for predicting modulus of the rock mass, and so those are considered as the predictors to design the GP model. Finally, two equations were achieved by GP. The statistical measures of root mean square error (RMSE) and variance account for (VAF) have been used to compare GP models with the well-known existing empirical equations proposed for predicting the deformation modulus. These performance criteria proved that the GP models give higher predictions over existing empirical models.", } @Article{Beiki:2013:IJRMMS, author = "Morteza Beiki and Abbas Majdi and Ali Dadi Givshad", title = "Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks", journal = "International Journal of Rock Mechanics and Mining Sciences", volume = "63", pages = "159--169", year = "2013", keywords = "genetic algorithms, genetic programming", ISSN = "1365-1609", DOI = "doi:10.1016/j.ijrmms.2013.08.004", URL = "http://www.sciencedirect.com/science/article/pii/S1365160913001196", } @InProceedings{bekavac-vsnajder:2013:BSNLP, author = "Marko Bekavac and Jan Snajder", title = "GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts", booktitle = "Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing", year = "2013", editor = "Jakub Piskorski and Lidia Pivovarova and Hristo Tanev and Roman Yangarber", pages = "43--47", address = "Sofia, Bulgaria", publisher_address = "209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 acl@aclweb.org", month = "8-9 " # aug, publisher = "Association for Computational Linguistics", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-937284-59-6", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.397.588", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.397.588", URL = "http://www.aclweb.org/anthology/W13-2407", URL = "http://www.aclweb.org/anthology/W13-2407.pdf", size = "5 pages", abstract = "We describe GPKEX, a key-phrase extraction method based on genetic programming. We represent Keyphrase scoring measures as syntax trees and evolve them to produce rankings for key phrase candidates extracted from text. We apply and evaluate GPKEX on Croatian newspaper articles. We show that GPKEX can evolve simple and interpretable key-phrase scoring measures that perform comparably to more complex machine learning methods previously developed for Croatian.", notes = "The annotated dataset is available under CC BY-NC-SA license from http://takelab.fer.hr/gpkex ACL 2013 http://www.aclweb.org/anthology/W/W13/#2400 ", } @Article{DBLP:journals/csjm/Bekkouche23, author = "Mohammed Bekkouche", title = "Correcting Instruction Expression Logic Errors with {GenExp}: A Genetic Programming Solution", journal = "Computer Science Journal of Moldova", volume = "31", number = "2", pages = "217--247", year = "2023", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, error correction, instruction expression, plausible patch, crossover, mutation", timestamp = "Tue, 22 Aug 2023 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/csjm/Bekkouche23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://www.math.md/publications/csjm/issues/v31-n2/13785/", DOI = "doi:10.56415/csjm.v31.12", size = "31 pages", abstract = "Correcting logical errors in a program is not simple even with the availability of an error locating tool. we introduce GenExp, a genetic programming approach to automate the task of repairing instruction expressions from logical errors. Starting from an error location specified by the programmer, we search for a replacement instruction that passes all test cases. Specifically, we generate expressions that will substitute the selected instruction expression until we obtain one that corrects the input program. The search space is exponentially large, making exhaustive methods inefficient. Therefore, we use a genetic programming meta-heuristic that organises the search process into stages, with each stage producing a group of individuals. The results showed that our approach can find at least one plausible patch for almost all cases considered in experiments and outperforms a notable state-of-the-art error repair approach like ASTOR. Although our tool is slower than ASTOR, it provides greater precision in detecting plausible repairs, making it a suitable option for users who prioritise accuracy over speed.", } @Article{Beldek:2007:IS, author = "Ulas Beldek and Kemal Leblebicioglu", title = "Strategy creation, decomposition and distribution in particle navigation", journal = "Information Sciences", year = "2007", volume = "177", number = "3", pages = "755--770", month = "1 " # feb, keywords = "genetic algorithms, genetic programming, Rule-base, Strategy planning, Robot navigation, Maze solving, Optimization, Multi-agent systems", DOI = "doi:10.1016/j.ins.2006.07.008", abstract = "Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles.", } @Article{Belem:2014:IPM, author = "Fabiano M. Belem and Eder F. Martins and Jussara M. Almeida and Marcos A. Goncalves", title = "Personalized and object-centered tag recommendation methods for Web 2.0 applications", journal = "Information Processing \& Management", volume = "50", number = "4", pages = "524--553", year = "2014", ISSN = "0306-4573", DOI = "doi:10.1016/j.ipm.2014.03.002", URL = "http://www.sciencedirect.com/science/article/pii/S0306457314000181", keywords = "genetic algorithms, genetic programming, Tag recommendation, Relevance metrics, Personalisation", abstrct = "Several Web 2.0 applications allow users to assign keywords (or tags) to provide better organisation and description of the shared content. Tag recommendation methods may assist users in this task, improving the quality of the available information and, thus, the effectiveness of various tag-based information retrieval services, such as searching, content recommendation and classification. This work addresses the tag recommendation problem from two perspectives. The first perspective, centred at the object, aims at suggesting relevant tags to a target object, jointly exploiting the following three dimensions: (i) tag co-occurrences, (ii) terms extracted from multiple textual features (e.g., title, description), and (iii) various metrics to estimate tag relevance. The second perspective, centered at both object and user, aims at performing personalised tag recommendation to a target object-user pair, exploiting, in addition to the three aforementioned dimensions, a metric that captures user interests.", } @InProceedings{Belgasem:2002:ACDM, author = "A. Belgasem and T. Kalganova and A. Almaini", title = "Extrinsic Evolution of Finite State Machine", booktitle = "Proc. of ACDM2002", year = "2002", editor = "I. C. Parmee", pages = "157--168", publisher = "Springer", month = apr # " 16-18", keywords = "genetic algorithms, genetic programming, evolvable hardware", URL = "http://bura.brunel.ac.uk/handle/2438/2514", URL = "http://bura.brunel.ac.uk/bitstream/2438/2514/1/2002_Belgasem_ACDM.pdf", size = "12 pages", abstract = "extrinsic evolvable hardware approach to evolve finite state machines (FSM). Both the genetic algorithm (GA) and Evolvable Hardware (EHW) are combined together to produce optimal logic circuit. GA is used to optimise the state assignment problem. EHW is used to design the combinational parts of the desired circuit. The approach is tested on a number of finite state machines from MCNC benchmark set. These circuits have been evolved using different functional sets of logic gates and GA parameters. The results show promise for the use of this approach as a design method for sequential logic circuits.", } @InProceedings{Belhor:2016:AICCSA, author = "M. Belhor and F. Jemili", booktitle = "2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)", title = "Intrusion detection based on genetic fuzzy classification system", year = "2016", abstract = "Information system is vital for any company. However, the opening to the outside world makes the computer system more vulnerable to attack. It is essential to protect it. Intrusion Detection System (IDS) is an auditing mechanism that analyses the traffic system and applications to identify normal use of the system and an intrusion attempt and also it prevent security managers. Despite the advantages of IDS, they suffer from a few problems. The major problem in the field of intrusion detection is the classification problem. Genetic Fuzzy System (GFS) are models capable of integrating accuracy and high comprehensibility in their results. They have been widely employed to solve classification problems. In this paper, we use a new GFS model called Genetic Programming Fuzzy Inference System for Classification (GPFIS-Class). It based on Multi-Gene Genetic Programming (MGGP). This model is not used in the intrusion detection area. We use an efficient feature selection method to eliminate data redundancy and irrelevant features in order to analyse the huge data namely the NSL-KDD data set.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AICCSA.2016.7945690", month = nov, notes = "Also known as \cite{7945690}", } @InProceedings{beligiannis:1999:EMPFNS, author = "G. N. Beligiannis and E. N. Demiris and S. D. Likothanassis", title = "Evolutionary Multimodel Partitioning Filters for Nonlinear Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1227", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, EHW, evolvable hardware, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-452.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-452.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Beligiannis:2005:tIM, title = "Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique", author = "Grigorios N. Beligiannis and Lambros V. Skarlas and Spiridon D. Likothanassis and Katerina G. Perdikouri", journal = "IEEE Transactions on Instrumentation and Measurement", year = "2005", volume = "54", number = "6", pages = "2184--2190", month = dec, keywords = "genetic algorithms, genetic programming, medical signal processing, nonlinear dynamical systems complex biomedical data identification, evolutionary multimodel partitioning filters, nonlinear model structure", DOI = "doi:10.1109/TIM.2005.858573", ISSN = "0018-9456", size = "7 pages", abstract = "In this contribution, a genetic programming (GP)-based technique, which combines the ability of GP to explore both automatically and effectively, the whole set of candidate model structures and the robustness of evolutionary multimodel partitioning filters, is presented. The method is applied to the nonlinear system identification problem of complex biomedical data. Simulation results show that the algorithm identifies the true model and the true values of the unknown parameters for each different model structure, thus assisting the GP technique to converge more quickly to the (near) optimal model structure. The method has all the known advantages of the evolutionary multi model partitioning filters, that is, it is not restricted to the Gaussian case; it is applicable to on-line/adaptive operation and is computationally efficient. Furthermore, it can be realized in a parallel processing fashion, a fact which makes it amenable to very large scale integration implementation.", notes = "Fig. 3. Plot of the real (solid line) versus the predicted (dashed line) values for an epoch consisting of 300 samples of an epileptic MEG (MEG measured in pT = 10 T). Fig. 4. Plot of the real (solid line) versus the predicted (dashed line) values of an f-MCG in a normal pregnancy (f-MCG measured in pT = 10 T). TABLE II ABILITY OF THE ESTIMATED NONLINEAR MODEL IN PREDICTING ABNORMAL PREGNANCIES", } @InCollection{bell:1999:ESWRNNGA, author = "Matt Bell", title = "Evolving the Structure and Weights of Recurrent Neural Network though Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "11--20", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @InProceedings{DBLP:conf/ijcci/BellangerBCH23, author = "Thibaut Bellanger and Matthieu {Le Berre} and Manuel Clergue and Jin-Kao Hao", title = "A One-Vs-One Approach to Improve Tangled Program Graph Performance on Classification Tasks", booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023", year = "2023", editor = "Niki {van Stein} and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe", address = "Rome, Italy", publisher = "SCITEPRESS", month = nov # " 13-15", pages = "53--63", keywords = "genetic algorithms, genetic programming, Classification, Tangled Program Graph, Ensemble Learning, Evolutionary Machine Learning, Evolutionary Search and Meta-Heuristics", timestamp = "Fri, 08 Dec 2023 12:42:26 +0100", biburl = "https://dblp.org/rec/conf/ijcci/BellangerBCH23.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "https://www.insticc.org/node/TechnicalProgram/ijcci/2023/presentationDetails/121677", DOI = "doi:10.5220/0012167700003595", abstract = "We propose an approach to improve the classification performance of the Tangled Programs Graph (TPG). TPG is a genetic programming method that aims to discover Directed Acyclic Graphs (DAGs) through an evolutionary process, where the edges carry programs that allow nodes to create a route from the root to a leaf, and the leaves represent actions or labels in classification. Despite notable successes in reinforcement learning tasks, TPG performance in classification appears to be limited in its basic version, as evidenced by the scores obtained on the MNIST dataset. However, the advantage of TPG compared to neural networks is to obtain, like decision trees, a global decision that is decomposable into simple atomic decisions and thus more easily explainable. Compared to decision trees, TPG has the advantage that atomic decisions benefit from the expressiveness of a pseudo register-based programming language, and the graph evolutionary construction prevents the emergence of overfitting. Our approach consists of decomposing the multi-class problem into a set of one-vs-one binary problems, training a set of TPG for each of them, and then combining the results of the TPGs to obtain a global decision, after selecting the best ones by a genetic algorithm. We test our approach on several benchmark datasets, and the results obtained are promising and tend to validate the proposed method.", notes = "ECTA23-RP-32", } @InProceedings{Bellanger:2024:GGP, author = "Thibaut Bellanger and Matthieu {Le Berre} and Manuel Clergue and Jin-Kao Hao", title = "Directed Acyclic Program Graph Applied to Supervised Classification", booktitle = "2nd GECCO workshop on Graph-based Genetic Programming", year = "2024", editor = "Dennis G. Wilson and Roman Kalkreuth and Eric Medvet and Giorgia Nadizar and Giovanni Squillero and Alberto Tonda and Yuri Lavinas", pages = "1676--1680", address = "Melbourne, Australia", series = "GECCO '24", month = "14 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, evolutionary algorithm, local search, supervised classification", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664115", size = "5 pages", abstract = "In the realm of Machine Learning, the pursuit of simpler yet effective models has led to increased interest in decision trees due to their interpretability and efficiency. However, their inherent simplicity often limits their ability to handle intricate patterns in data. This paper introduces a novel approach termed Directed Acyclic Graphs of Programs, inspired by evolutionary strategies, to address this challenge. By iteratively constructing program graphs from binary decision makers, our method offers a balance of simplicity and performance for classification tasks. Notably, we emphasize the preservation of model interpretability and expressiveness, avoiding the use of ensemble techniques like voting. Experimental evaluations demonstrate the superiority of our approach over existing methods in terms of both effectiveness and interpretability.", notes = "wksp117s2 also known as \cite{bellanger:2024:GECCOcomp} GECCO-2024 GGP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @PhdThesis{Bellucci:thesis, author = "Michael Anthony Bellucci", title = "Theoretical Studies of Excited State 1,3 Dipolar Cycloadditions", school = "Boston University", year = "2012", address = "USA", keywords = "genetic algorithms, genetic programming, Chemistry, Dipolar cycloaddition, Excited state, Hydroxy flavone, Methyl cinnamate, Physical chemistry, Proton transfer, Pure science, Quantum physics", URL = "http://www.bu.edu/phpbin/calendar/event.php?id=127428&cid=17", URL = "http://adsabs.harvard.edu/abs/2013PhDT........20B", abstract = "The 1,3 dipolar photocycloaddition reaction between 3-hydroxy-4',5,7-trimethoxyflavone (3-HTMF) and methyl cinnamate is investigated in this work. Since its inception in 2004 [JACS, 124, 13260 (2004)], this reaction remains at the forefront in the synthetic design of the rocaglamide natural products. The reaction is multi-faceted in that it involves multiple excited states and is contingent upon excited state intramolecular proton transfer (ESIPT) in 3-HTMF. Given the complexity of the reaction, there remain many questions regarding the underlying mechanism. Consequently, throughout this work we investigate the mechanism of the reaction along with a number of other properties that directly influence it. To investigate the photocycloaddition reaction, we began by studying the effects of different solvent environments on the ESIPT reaction in 3-hydroxyflavone since this underlying reaction is sensitive to the solvent environment and directly influences the cycloaddition. To study the ESIPT reaction, we developed a parallel multi-level genetic program to fit accurate empirical valence bond (EVB) potentials to ab initio data. We found that simulations with our EVB potentials accurately reproduced experimentally determined reaction rates, fluorescence spectra, and vibrational frequency spectra in all solvents. Furthermore, we found that the ultrafast ESIPT process results from a combination of ballistic transfer and intramolecular vibrational redistribution. To investigate the cycloaddition reaction mechanism, we used the string method to obtain minimum energy paths on the ab initio potential. These calculations demonstrated that the reaction can proceed through formation of an exciplex in the S1 state, followed by a non-adiabatic transition to the ground state. In addition, we investigated the enantioselective catalysis of the reaction using alpha,alpha,alpha',alpha'-tetraaryl-1,3-dioxolan-4,5-dimethanol alcohol (TADDOL). We found that TADDOL lowered the energy barrier by 10-12 kcal/mol through stabilizing hydrogen bond interactions. Using temperature accelerated molecular dynamics, we obtained the potential of mean force (PMF) associated with 3-HTMF attacking the TADDOL/methyl cinnamate complex. We found that the exo reaction is inhibited through steric interactions with the aryl substituents on TADDOL. Furthermore, we found that the exo configuration breaks the intramolecular hydrogen bond in TADDOL, which stabilizes the individual reactants apart from each other. The role of the T1 state is also discussed.", notes = "Broken June 2022 http://www.bu.edu/commencement/files/2013/05/Redbook_2013.pdf", } @MastersThesis{Belmadani2016-bn, title = "{MotifGP}: {DNA} Motif Discovery Using Multiobjective Evolution", author = "Manuel Belmadani", school = "School of Electrical Engineering and Computer Science, University of Ottawa", year = "2016", type = "Master degree in Computer Science Specialization in Bioinformatics", address = "Canada", keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10393/34213", URL = "https://ruor.uottawa.ca/bitstream/10393/34213/1/Belmadani_Manuel_2016_thesis.pdf", DOI = "doi:10.20381/ruor-5077", size = "119 pages", abstract = "The motif discovery problem is becoming increasingly important for molecular biologists as new sequencing technologies are producing large amounts of data, at rates which are unprecedented. The solution space for DNA motifs is too large to search with naive methods, meaning there is a need for fast and accurate motif detection tools. We propose MotifGP, a multiobjective motif discovery tool evolving regular expressions that characterize overrepresented motifs in a given input dataset. This thesis describes and evaluates a multiobjective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimization in the context of this specific motif discovery problem.", notes = "supervisor Dr. Marcel Turcotte", } @InProceedings{Belmadani:2016:CIBCB, author = "Manuel Belmadani and Marcel Turcotte", booktitle = "2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)", title = "MotifGP: Using multi-objective evolutionary computing for mining network expressions in DNA sequences", year = "2016", abstract = "This paper describes and evaluates a multi-objective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-the-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimisation in the context of this specific motif discovery problem.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIBCB.2016.7758133", month = oct, notes = "Also known as \cite{7758133}", } @InProceedings{belpaeme:1999:evfd, author = "Tony Belpaeme", title = "Evolution of Visual Feature Detectors", booktitle = "Late Breaking Papers at EvoISAP'99: the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing", year = "1999", editor = "Riccardo Poli and Stefano Cagnoni and Hans-Michael Voigt and Terry Fogarty and Peter Nordin", pages = "1--10", address = "Goteborg, Sweden", month = "28 " # may, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", URL = "http://arti.vub.ac.be/~tony/papers/EvoIASP99.ps.gz", URL = "http://citeseer.ist.psu.edu/362631.html", abstract = "This paper describes how sets of visual feature detectors are evolved starting from simple primitives. The primitives, of which some are inspired on visual processing observed in mammalian visual pathways, are combined using genetic programming to form a feed-forward feature-extraction hierarchy. Input to the feature detectors consists of a series of real-world images, containing objects or faces. The results show how each set of feature detectors self-organizes into a set which is capable of returning feature vectors for discriminating the input images. We discuss the influence of different settings on the evolution of the feature detectors and explain some phenomena.", notes = "EvoIASP'99 Available as CSRP-99-10 from the School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. STGP. Information returned by each (of 5) feature detector, entropy of the output vector p4 {"}if the outputs are weel spread, meaning the feature detectors return useful information, the fitness will be high. Explicit parsimony preasure, but not needed p8? LilGP.", } @Article{BELSCHNER199619, author = "R. Belschner", title = "Evaluation of Real-Time Requirements by Simulation Based Analysis", journal = "IFAC Proceedings Volumes", year = "1996", volume = "29", number = "6", pages = "19--26", month = nov, note = "20th IFAC/IFIP Workshop on Real Time Programming 1995 (WRTP '95), Fort Lauderdale, USA, 6-10 November", keywords = "genetic algorithms, genetic programming, SBSE, EPOSIX, event driven simulation, distributed real-time systems, timing requirements, worst case analysis, evolutionary strategies", ISSN = "1474-6670", URL = "http://www.sciencedirect.com/science/article/pii/S1474667017437414", DOI = "doi:10.1016/S1474-6670(17)43741-4", size = "8 pages", abstract = "This paper addresses issues related to the software design in distributed real-time automation systems. Beside the influence of the real-time operating system and the behaviour of the technical process, the properties of the communication network play an important part for the temporal correctness of the software design. In consideration of the enormous complexity, an attempt to improve the software correctness concerning the temporal behaviour by a three layered Simulation Based Analysis (SBA) System is presented. The SBA system automatically evaluates timing requirements, which can be specified by a problem adapted language, and estimates the system's behaviour in both the best case and especially the worst case by using evolutionary strategies. The SBA system is currently implemented into a prototype.", notes = "Network, Ethernet, messages. Empirical fitness landscape (fig 9) page 25. University of Stuttgart", } @InProceedings{Belur:1997:CORElb, author = "Sheela V. Belur", title = "CORE: Constrained Optimization by Random Evolution", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "280--286", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 MATLAB", } @Article{benabdallah:2023:AJSE, author = "Fatma Zohra Benabdallah and Leila Djerou", title = "Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in {3D} Ultrasound Images", journal = "Arabian Journal for Science and Engineering", year = "2023", volume = "48", number = "2", pages = "2429--2440", keywords = "genetic algorithms, genetic programming, multi-gene genetic programming (MGGP), GPTIPS 2, segmentation, Ultrasound images, Thyroid gland, Volume estimation", URL = "http://link.springer.com/article/10.1007/s13369-022-07286-3", DOI = "doi:10.1007/s13369-022-07286-3", size = "12 pages", abstract = "The segmentation and estimation of thyroid volume in 3D ultrasound images have attracted the research community's attention because of their great importance in clinical diagnosis. Usually, thyroid volume estimation is based on the segmentation of 3D ultrasound images, which is difficult due to various disorders, including non-homogeneous texture distribution within the thyroid region, artifacts, speckles, and the nature of the thyroid shape. This paper presents an approach to segmenting all individual slices and then reconstructing them into a 3D object to overcome these difficulties. The process involves four techniques. The VOI initialization encompasses the probable thyroid gland; it greatly affects the segmentation results. Multi-gene genetic programming determines the appropriate textural features. The block-matching technique estimates the thyroid gland's change in size and location from slice to slice. Finally, the ITKSNAP software reconstructs the 3D volume. The proposed method is compared with state-of-the-art methods to prove its effectiveness in medical image analysis. Sixteen 3D images from an ultrasound thyroid image dataset were used for the experiments. The analysis of the results based on performance evaluation metrics shows that the proposed method is more efficient than the state-of-the-art methods", notes = "Laboratory of LESIA, University of Biskra, 07000 Biskra, Algeria", } @InProceedings{Benbassat:2010:CIGPU, author = "Amit Benbassat and Moshe Sipper", title = "Evolving Lose-Checkers Players using Genetic Programming", booktitle = "IEEE Conference on Computational Intelligence and Game", year = "2010", pages = "30--37", address = "IT University of Copenhagen, Denmark", month = "18-21 " # aug, keywords = "genetic algorithms, genetic programming, explicitly defined intron, full knowledge board game, genetic programming tree, local mutation, lose checker player, multitree individual, state evaluator, computer games, trees (mathematics)", URL = "http://game.itu.dk/cig2010/proceedings/papers/cig10_005_011.pdf", DOI = "doi:10.1109/ITW.2010.5593376", size = "8 pages", abstract = "We present the application of genetic programming (GP) to the zero-sum, deterministic, full-knowledge board game of Lose Checkers. Our system implements strongly typed GP trees, explicitly defined introns, local mutations, and multitree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reservoir for possible future use. Multi-tree individuals are implemented by a method inspired by structural genes in living organisms, whereby we take a single tree describing a state evaluator and split it.", notes = "http://game.itu.dk/cig2010/proceedings/wp-content/acceptedpapers.html Also known as \cite{5593376}", } @InProceedings{Benbassat:2011:GECCOcomp, author = "Amit Benbassat and Moshe Sipper", title = "Evolving board-game players with genetic programming", booktitle = "GECCO 2011 Graduate students workshop", year = "2011", editor = "Miguel Nicolau", isbn13 = "978-1-4503-0690-4", keywords = "genetic algorithms, genetic programming", pages = "739--742", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001858.2002080", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present the application of genetic programming (GP) to zero-sum, deterministic, full-knowledge board games. Our work expands previous results in evolving board-state evaluation functions for Lose Checkers to a 10x10 variant of Checkers, as well as Reversi. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method.", notes = "Also known as \cite{2002080} Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.", } @InCollection{Benbassat:2012:GPTP, author = "Amit Benbassat and Achiya Elyasaf and Moshe Sipper", title = "More or Less? Two Approaches to Evolving Game-Playing Strategies", booktitle = "Genetic Programming Theory and Practice X", year = "2012", series = "Genetic and Evolutionary Computation", editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore", publisher = "Springer", chapter = "12", pages = "171--185", address = "Ann Arbor, USA", month = "12-14 " # may, keywords = "genetic algorithms, genetic programming, alpha-beta search, Checkers, Dodgem, Freecell, Hyper heuristic, Reversi", isbn13 = "978-1-4614-6845-5", URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_12", DOI = "doi:10.1007/978-1-4614-6846-2_12", abstract = "We present two opposing approaches to the evolution of game strategies, one wherein a minimal amount of domain expertise is injected into the process, the other infusing the evolutionary setup with expertise in the form of domain heuristics. We show that the first approach works well for several popular board games, while the second produces top-notch solvers for the hard game of FreeCell.", notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013", } @InProceedings{Benbassat:2012:GECCOcomp, author = "Amit Benbassat and Moshe Sipper", title = "Evolving players that use selective game-tree search with genetic programming", booktitle = "GECCO 2012 Late breaking abstracts workshop", year = "2012", editor = "Katya Rodriguez and Christian Blum", isbn13 = "978-1-4503-1178-6", keywords = "genetic algorithms, genetic programming", pages = "631--632", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330784.2330894", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We present the application of genetic programming (GP) to evolving game-tree search in board games. Our work expands previous results in evolving board-state evaluation functions for multiple board games, now evolving a search-guiding evaluation function alongside it. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method.", notes = "Also known as \cite{2330894} Distributed at GECCO-2012. ACM Order Number 910122.", } @InProceedings{Benbassat:2012:CIG, author = "Amit Benbassat and Moshe Sipper", title = "Evolving both search and strategy for {Reversi} players using genetic programming", booktitle = "IEEE Conference on Computational Intelligence and Games, CIG 2012", year = "2012", pages = "47--54", address = "Granada", month = "11-14 " # sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, computer games, search problems, trees (mathematics), Reversi players, deterministic board game, full-knowledge board game, game-tree pruning, search algorithm, selective directional crossover method, zero-sum board game, Games, Humans, Receivers, Sociology, Statistics", isbn13 = "978-1-4673-1193-9", URL = "https://bibtex.github.io/CIG-2012-BenbassatS.html", DOI = "doi:10.1109/CIG.2012.6374137", size = "8 pages", abstract = "We present the application of genetic programming to the zero-sum, deterministic, full-knowledge board game of Reversi. Expanding on our previous work on evolving boardstate evaluation functions, we now evolve the search algorithm as well, by allowing evolved programs control of game-tree pruning. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. We show that our system regularly churns out highly competent players and our results prove easy to scale.", notes = "Also known as \cite{6374137}", } @InProceedings{Benbassat:2013:CIG, author = "Amit Benbassat and Moshe Sipper", booktitle = "IEEE Conference on Computational Intelligence in Games (CIG 2013)", title = "{EvoMCTS:} Enhancing MCTS-based players through genetic programming", year = "2013", month = "11-13 " # aug, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CIG.2013.6633631", ISSN = "2325-4270", size = "8 pages", abstract = "We present EvoMCTS, a genetic programming method for enhancing level of play in games. Our work focuses on the zero-sum, deterministic, perfect-information board game of Reversi. Expanding on our previous work on evolving board-state evaluation functions for alpha-beta search algorithm variants, we now evolve evaluation functions that augment the MTCS algorithm. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our system regularly evolves players that outperform MCTS players that use the same amount of search. Our results prove scalable and EvoMCTS players whose search is increased offline still outperform MCTS counterparts. To demonstrate the generality of our method we apply EvoMCTS successfully to the game of Dodgem.", notes = "Also known as \cite{6633631}", } @PhdThesis{BenbassatDissertation, author = "Amit Benbassat", title = "Finding Methods for Evolving Competent Agents in Multiple Domains", school = "Ben-Gurion University of the Negev", year = "2014", address = "Israel", month = sep, keywords = "genetic algorithms, genetic programming, MTCS", URL = "https://dl.dropboxusercontent.com/u/36726425/ThesisFinalSubmissionWithTitle.pdf", size = "124 pages", abstract = "We present the application of genetic programming (GP) to search in zero-sum, deterministic, full-knowledge board games. We use multiple board games and multiple search algorithms as test cases in order to exhibit the flexibility of our system. We conduct experiments evolving players for variants of Checkers, Reversi, Dodgem, Nine Men's Morris and Hex, evolving them in conjunction with the Alpha-Beta search and Monte Carlo Tree Search (MCTS) algorithms. Throughout our research we rely on modern neo-Darwinian theory specifically, the gene-centred view of evolution to guide the design of our setup. Our evolutionary system implements strongly typed GP trees, explicitly defined introns, various mutation operators, a novel selective crossover operator, and multi-tree individuals. Explicitly defined introns in the genome allow for information selected out of the population to be kept as a reserve for possible future use. Selective genetic operators allow us to apply additional selection pressure during the procreation stage. Multi-tree individuals allow us to evolve software components that can be integrated into existing search algorithms where they improve play level over hand-crafted baseline players. Our results demonstrate patent improvement in play level for every game, clearly showing that GP is applicable to evolving search in board games. Results show differing levels of scalability, with the best scalability shown when using the MCTS algorithm. We also present our highly scalable EvoMCTS system designed as a scalable, easy-to-use, quick learning tool to improve the play level in games without need for any expert domain knowledge. Pursuing the goal of general game playing (GGP) we present a system that can serve as a stepping stone on the way to general game learning (GGL), where a system can learn a game upon getting its rule set, and the human developer can improve the resulting players by supplying the learning system with relevant information about the game.", notes = "Supervisor: Moshe Sipper", } @Article{Benbassat:2014:ieeegames, author = "Amit Benbassat and Moshe Sipper", journal = "IEEE Transactions on Computational Intelligence and AI in Games", title = "{EvoMCTS:} A Scalable Approach for General Game Learning", year = "2014", volume = "6", number = "4", pages = "382--394", month = dec, keywords = "genetic algorithms, genetic programming, STGP, MCTS, Board Games, Monte Carlo Methods, Search", DOI = "doi:10.1109/TCIAIG.2014.2306914", ISSN = "1943-068X", size = "29 pages", abstract = "We present the application of genetic programming as a generic game learning approach to zero-sum, deterministic, full knowledge board games by evolving board-state evaluation functions to be used in conjunction with Monte Carlo Tree Search (MCTS). Our method involves evolving board-evaluation functions that are then used to guide the MCTS play out strategy. We examine several variants of Reversi, Dodgem, and Hex using strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our results show a proficiency that surpasses that of baseline handcrafted players using equal and in some cases a greater amount of search, with little domain knowledge and no expert domain knowledge. Moreover, our results exhibit scalability.", notes = "Also known as \cite{6744581}", } @Article{benbouras:2021:AS, author = "Mohammed {Amin Benbouras} and Alexandru-Ionut Petrisor", title = "Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils", journal = "Applied Sciences", year = "2021", volume = "11", number = "2", keywords = "genetic algorithms, genetic programming", ISSN = "2076-3417", URL = "https://www.mdpi.com/2076-3417/11/2/536", DOI = "doi:10.3390/app11020536", abstract = "Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modelling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are used to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modelling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.", notes = "also known as \cite{app11020536}", } @Article{BENCHAABENE:2021:CBM, author = "Wassim {Ben Chaabene} and Moncef L. Nehdi", title = "Genetic programming based symbolic regression for shear capacity prediction of {SFRC} beams", journal = "Construction and Building Materials", volume = "280", pages = "122523", year = "2021", ISSN = "0950-0618", DOI = "doi:10.1016/j.conbuildmat.2021.122523", URL = "https://www.sciencedirect.com/science/article/pii/S095006182100283X", keywords = "genetic algorithms, genetic programming, Steel fiber, Concrete, Beam, Shear strength, Symbolic regression, Generative adversarial network, Synthetic data", abstract = "The complexity of shear transfer mechanisms in steel fiber-reinforced concrete (SFRC) has motivated researchers to develop diverse empirical and soft-computing models for predicting the shear capacity of SFRC beams. Yet, such existing methods have been developed based on limited experimental databases, which makes their generalization capability uncertain. To account for the limited experimental data available, this study pioneers a novel approach based on tabular generative adversarial networks (TGAN) to generate 2000 synthetic data examples. A {"}train on synthetic - test on real{"} philosophy was adopted. Accordingly, the entire 2000 synthetic data were used for training a genetic programming-based symbolic regression (GP-SR) model to develop a shear strength equation for SFRC beams without stirrups. The model accuracy was then tested on the entire set of 309 real experimental data examples, which thus far are unknown to the model. Results show that the novel GP-SR model achieved superior predictive accuracy, outperforming eleven existing equations. Sensitivity analysis revealed that the shear-span-to-depth ratio was the most influential parameter in the proposed equation. The present study provides an enhanced predictive model for the shear capacity of SFRC beams, which should motivate further research to effectively train evolutionary algorithms using synthetic data when acquiring large and comprehensive experimental datasets is not feasible", } @InProceedings{Benchaji:2018:CSNet, author = "Ibtissam Benchaji and Samira Douzi and Bouabid {El Ouahidi}", title = "Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection", booktitle = "2018 2nd Cyber Security in Networking Conference (CSNet)", year = "2018", month = "24-26 " # oct, address = "Paris", keywords = "genetic algorithms, genetic programming, Credit cards, Sociology, Statistics, Biological cells, Clustering algorithms, Training, Fraud Detection, Imbalanced dataset, K-means clustering, Autoencoder", isbn13 = "978-1-5386-7046-0", DOI = "doi:10.1109/CSNET.2018.8602972", abstract = "With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection method has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. Thus, many of traditional classifiers often fail to detect minority class objects for these skewed data sets. This paper aims first: to enhance classified performance of the minority of credit card fraud instances in the imbalanced data set, for that we propose a sampling method based on the K-means clustering and the genetic algorithm. We used K-means algorithm to cluster and group the minority kind of sample, and in each cluster we use the genetic algorithm to gain the new samples and construct an accurate fraud detection classifier.", notes = "Also known as \cite{8602972}", } @Article{Benes201392, author = "Radek Benes and Jan Karasek and Radim Burget and Kamil Riha", title = "Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images", journal = "Computer Methods and Programs in Biomedicine", volume = "109", number = "1", pages = "92--103", year = "2013", keywords = "genetic algorithms, genetic programming, Common carotid artery, Localisation, Machine vision system", ISSN = "0169-2607", URL = "http://www.sciencedirect.com/science/article/pii/S0169260712001964", DOI = "doi:10.1016/j.cmpb.2012.08.014", size = "12 pages", abstract = "The common carotid artery (CCA) is a source of important information that doctors can use to evaluate the patients' health. The most often measured parameters are arterial stiffness, lumen diameter, wall thickness, and other parameters where variation with time is usually measured. Unfortunately, the manual measurement of dynamic parameters of the CCA is time consuming, and therefore, for practical reasons, the only alternative is automatic approach. The initial localisation of artery is important and must precede the main measurement. This article describes a novel method for the localization of CCA in the transverse section of a B-mode ultrasound image. The novel method was designed automatically by using the grammar-guided genetic programming (GGGP). The GGGP searches for the best possible combination of simple image processing tasks (independent building blocks). The best possible solution is represented with the highest detection precision. The method is tested on a validation database of CCA images that was specially created for this purpose and released for use by other scientists. The resulting success of the proposed solution was 82.7percent, which exceeded the current state of the art by 4percent while the computation time requirements were acceptable. The paper also describes an automatic method that was used in designing the proposed solution. This automatic method provides a universal approach to designing complex solutions with the support of evolutionary algorithms.", notes = "Brno University of Technology, Department of Telecommunications, Purkynova 118, Brno, Czech Republic", } @InProceedings{Bengio:1994:GPslrNN, author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier", title = "Use of genetic programming for the search of a new learning rule for neutral networks", booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence", year = "1994", volume = "1", pages = "324--327", address = "Orlando, Florida, USA", month = "27-29 " # jun, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ANN", size = "4 pages", URL = "http://www.idiap.ch/~bengio/cv/publications/ps/bengio_1994_wcci.ps.gz", URL = "http://citeseer.ist.psu.edu/465154.html", DOI = "doi:10.1109/ICEC.1994.349932", abstract = "In previous work ([1, 2, 3]) we explained how to use standard optimization methods such as simulated annealing, gradient descent and genetic algorithms to optimize a parametric function which could be used as a learning rule for neural networks. To use these methods, we had to choose a fixed number of parameters and a rigid form for the learning rule. In this article, we propose to use genetic programming to find not only the values of rule parameters but also the optimal number of parameters and the form of the rule. Experiments on classification tasks suggest genetic programming finds better learning rules than other optimization methods. Furthermore, the best rule found with genetic programming outperformed the well-known backpropagation algorithm for a given set of tasks", notes = "Uses GP to produce a learning rule for training a neural network. Evolved rule like back-propergation but better, differential is cubed. Says neural network is fully connected, ", } @Article{bengio:1995:npl, author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier", title = "On the Search for New Learning Rules for {ANN}s", journal = "Neural Processing Letters", year = "1995", volume = "2", number = "4", pages = "26--30", keywords = "genetic algorithms, genetic programming, ANN, Neural Network, Artificial Intelligence, Complex System, Nonlinear Dynamics, Practical Experiment", ISSN = "1370-4621", URL = "http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1995_npl.pdf", broken = "http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/131", DOI = "doi:10.1007/BF02279935", size = "12 pages", abstract = "we present a framework where a learning rule can be optimized within a parametric learning rule space. We define what we call parametric learning rules and present a theoretical study of their generalization properties when estimated from a set of learning tasks and tested over another set of tasks. We corroborate the results of this study with practical experiments.", } @PhdThesis{BenHamid:thesis, author = "Sana {Ben Hamida}", title = "Evolutionary Algorithms: Handling Constraints and Real-World Application", school = "Ecole Polytechnique", year = "2001", address = "Paris, France", month = mar, keywords = "genetic algorithms, genetic programming", URL = "http://www.cmap.polytechnique.fr/~sana/these.ps.gz", URL = "http://www.cmap.polytechnique.fr/~sana/indexAng.html", size = "225 pages", abstract = "The present work is a heuristic and experimental study in the evolutionary computation domain, and starts with an introduction to the artificial evolution with a synthesis of the principal approaches. The first part is a heuristic study devoted to constraint handling in evolutionary computation. It presents an extensive review of previous constraint handling methods in the literature and their limitations. Two solutions are then proposed. The first idea is to improve genetic operator exploration capacity for constrained optimisation problems. The logarithmic mutation operator is conceived to explore both locally and globally the search space. The second solution introduces the original Adaptive Segregational Constraint Handling Evolutionary Algorithm (ASCHEA), the main idea of which is to maintain population diversity. In order to achieve this goal, three main ingredients are used: An original adaptive penalty method, a constraint-driven recombination, and a segregation selection that distinguishes between feasible and infeasible individuals to enhance the chances of survival of the feasible ones. Moreover, a niching method with an adaptive radius is added to ASCHEA in order to handle multimodal functions. Finally, to complete the ASCHEA system, a new equality constraint handling strategy is introduced, that reduces progressively the feasible domain in order to approach the actual null-measured domain as close as possible at the end of the evolution. The second part is a case study tackling a real-world problem. The goal is to design the 2-dimensional profile of an optical lens (phase plate) in order to control focal-plane irradiance of some laser beam. The aim is to design the phase plate such that a small circular target on the focal plane is uniformly illuminated without energy loss.", notes = "In French. Chapter 7 GP v ES on laser. Supervisor: Marc Schoenauer", } @Misc{oai:HAL:hal-02489115v1, title = "Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility", author = "Sana {Ben Hamida} and Tristan Cazenave", howpublished = "HAL", year = "2020", month = feb # "~24", note = "working paper or preprint", publisher = "HAL CCSD", keywords = "genetic algorithms, genetic programming, computer science, artificial intelligence, AI", hal_id = "hal-02489115", hal_version = "v1", URL = "https://hal-univ-paris10.archives-ouvertes.fr/hal-02489115", abstract = "We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learning from financial time series to generate nonlinear functions for market volatility prediction. The input data, that is a series of daily prices of European S\&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub-sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples.", annote = "Laboratoire d'analyse et modelisation de systemes pour l'aide a la decision (LAMSADE) ; Universite Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS); Universite Paris Nanterre - UFR Sciences economiques, gestion, mathematiques, informatique (UPN SEGMI) ; Universite Paris Nanterre (UPN)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Laboratoire d'analyse et modelisation de systemes pour l'aide a la decision", identifier = "hal-02489115", language = "en", oai = "oai:HAL:hal-02489115v1", type = "info:eu-repo/semantics/preprint", notes = "Also known as \cite{benhamida:hal-02489115} See also \cite{DBLP:journals/ijait/Cazenave13}", } @InCollection{benini:1995:GFESOADF, author = "Luca Benini", title = "Genetic Fitting: Evolutionary Search of Optimal Approximations for Discrete Functions", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "19--28", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-195720-5", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{conf/idc/BenitezWL14, author = "Cesar Manuel Vargas Benitez and Wagner Rodrigo Weinert and Heitor Silverio Lopes", title = "Gene Expression Programming for Evolving Two-Dimensional Cellular Automata in a Distributed Environment", publisher = "Springer", booktitle = "IDC", year = "2014", volume = "570", series = "Studies in Computational Intelligence", pages = "107--117", keywords = "genetic algorithms, genetic programming", editor = "David Camacho and Lars Braubach and Salvatore Venticinque and Costin Badica", isbn13 = "978-3-319-10421-8", bibdate = "2014-10-09", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/idc/idc2014.html#BenitezWL14", URL = "http://dx.doi.org/10.1007/978-3-319-10422-5", } @InProceedings{Benjamin:2008:cec, author = "Simon C. Benjamin", title = "Evolutionary Route to Computation in Self-Assembled Nanoarrays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3094--3101", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1823-7", file = "EC0685.pdf", DOI = "doi:10.1109/CEC.2008.4631216", abstract = "Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesised in a range of systems. In this paper I study a form of array computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array would need to be ' wired' to conventional technologies only at its boundary. Any self assembled array would have a unique set of defects, therefore I employ an ab initio evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within the array.", keywords = "genetic algorithms, genetic programming", notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Benkhelifa:2009:cec, author = "E. Benkhelifa and G. Dragffy and A. G. Pipe and M. Nibouche", title = "Design Innovation for Real World Applications, Using Evolutionary Algorithms", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "918--924", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P692.pdf", DOI = "doi:10.1109/CEC.2009.4983043", abstract = "This paper discusses two important features of electronic design through evolutionary processes; creativity and innovation. Hence, conventional design methodologies are discussed and compared with their counterparts via evolutionary processes. An evolutionary search is used as an engine for discovering new designs for a real world application. Attempts to extract some useful principles from the evolved designs are presented and results are compared to conventional design topologies for the same problems.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Benkhelifa:2010:cec, author = "Elhadj Benkhelifa and Ashutosh Tiwari and Anthony Pipe", title = "Evolutionary design optimisation of a 32-Step Traffic Lights Controller", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, EHW", isbn13 = "978-1-4244-6910-9", DOI = "doi:10.1109/CEC.2010.5586108", size = "5 pages", abstract = "This paper shows a successful application of evolutionary algorithms for the design and optimisation of complex real world digital circuit that is a 32-Step Traffic Lights Controller. It discusses two important features of electronic design through evolutionary processes; creativity and innovation. Results are compared to conventional design topologies; and attempt to analyse the evolved designs is presented.", notes = "WCCI 2010. Also known as \cite{5586108} Decision Engineering Centre, School of applied Sciences, Cranfield University, Bedfordshire, MK43 0AL, UK.", } @InProceedings{Bennett:2007:SGAI, author = "Andrew Bennett and Derek Magee", title = "Learning Sets of Sub-Models for Spatio-Temporal Prediction", booktitle = "AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence", year = "2007", editor = "Max Bramer and Richard Ellis", address = "Cambridge, UK", month = "10-12 " # dec, organisation = "British Computer Society's Specialist Group on Artificial Intelligence (SGAI)", keywords = "genetic algorithms, genetic programming, card game playing", URL = "http://www.bcs-sgai.org/ai2007/admin/papers2.php?f=techpapers", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6694", URL = "http://citeseerx.ist.psu.edu/viewdoc/download/10.1.1.150.6694.pdf", size = "14 page", abstract = "In this paper we describe a novel technique which implements a spatio-temporal model as a set of sub-models based on first order logic. These sub-models model different, typically independent, parts of the dataset; for example different spatio or temporal contexts. To decide which sub-models to use in different situations a context chooser is used. By separating the sub-models from where they are applied allows greater flexibility for the overall model. The sub-models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to spatio-temporal data. This includes learning the rules of snap by observation, learning the rules of a traffic light sequence, and finally predicting a person's course through a network of CCTV cameras.", notes = "University of Leeds, UK", } @InProceedings{Bennett:2008:CIMA, author = "Andrew Bennett and Derek Magee", title = "Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data", booktitle = "Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications", year = "2008", editor = "Ioannis Hatzilygeroudis and Constantinos Koutsojannis and Vasile Palade", address = "Patras, Greece", month = jul # " 22", organisation = "CEUR", keywords = "genetic algorithms, genetic programming", URL = "http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf", URL = "http://www.comp.leeds.ac.uk/andrewb/Publications/CIMA08.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8374", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758", URN = "urn:nbn:de:0074-375-1", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.142.8374", oai = "oai:CiteSeerXPSU:10.1.1.150.6758", abstract = "In this paper we describe a novel technique for learning predictive models from non-deterministic spatio-temporal data. Our technique learns a set of sub-models that model different, typically independent, aspects of the data. By using temporal relations, and implicit feature selection, based on the use of 1st order logic expressions, we make the sub-models general, and robust to irrelevant variations in the data.We use Allen's intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time. These are added to the system as background knowledge in the form of functions. To combine the sub-models into a single model a context chooser is used. This probabilistically picks the most appropriate set of sub-models to predict in a certain context, and allows the system to predict in non-deterministic situations. The models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to learning the rules of snap, and uno by observation; and predicting a person's course through a network of CCTV cameras.", notes = "CIMA'08 Combinations of Intelligent Methods and Applications http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/", } @PhdThesis{bennett_a, author = "Andrew David Bennett", title = "Using genetic programming to learn predictive models from spatio-temporal data", school = "School of Computing, University of Leeds", year = "2010", address = "UK", month = jul, keywords = "genetic algorithms, genetic programming", URL = "http://etheses.whiterose.ac.uk/1376/", URL = "http://etheses.whiterose.ac.uk/1376/1/bennett_a.pdf", URL = "http://ethos.bl.uk/OrderDetails.do?did=43&uin=uk.bl.ethos.530613", size = "211 pages", abstract = "This thesis describes a novel technique for learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically independent, aspects of the spatio-temporal data. The conflict resolver is used to decide which sub-set of enabled production rules should be fired to produce a prediction. The conflict resolver in this thesis can probabilistically decide which set of production rules to fire, and allows the system to predict in non-deterministic situations. The predictive models are learnt by a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and predicting a person's course through a network of CCTV cameras. This thesis also describes the incorporation of qualitative temporal relations within these methods. Allen's intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time are used. The methods are evaluated on the card game Uno, and predicting a person's course through a network of CCTV cameras. This work is then extended to allow the methods to use qualitative spatial relations. The methods are evaluated on predicting a person's course through a network of CCTV cameras, aircraft turnarounds, and the game of Tic Tac Toe. Finally, an adaptive bloat control method is shown. This looks at adapting the amount of bloat control used during a run of STGP, based on the ratio of the fitness of the current best predictive model to the initial fitness of the best predictive model.", notes = "noughts and crosses. Not strongly typed GP. uk.bl.ethos.530613", } @InProceedings{bennett:1996:emaa, author = "Forrest H {Bennett III}", title = "Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "30--38", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "9 pages", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap4.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96", } @InProceedings{bennett:1996:emaant, author = "Forrest H {Bennett III}", title = "Emergence of a Multi-Agent Architecture and New Tactics For the Ant Colony Foraging Problem Using Genetic Programming", booktitle = "Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4", year = "1996", editor = "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson", pages = "430--439", address = "Cape Code, USA", publisher_address = "Cambridge, MA, USA", month = "9-13 " # sep, publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-63178-4", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6291906", DOI = "doi:10.7551/mitpress/3118.003.0044", DOI = "doi:10.7551/mitpress/3118.001.0001", size = "10 pages", abstract = "Previous work in multi-agent systems has required the human designer to make up-front decisions about the multi-agent architecture, including the number of agents to employ and the specific tasks to be performed by each agent. This paper describes the automatic evolution of these decisions during a run of genetic programming using architecture-altering operations.Genetic programming is extended to the discovery of multi-agent solutions for a central-place foraging problem for an ant colony. In this problem each individual ant is controlled by a set of agents, where agent is used in the sense of Minsky's Society of Mind.Two new tactics for the central-place food foraging problem that were discovered by genetic programming are presented in this paper.Genetic programming was able to evolve time-efficient solutions to this problem by distributing the functions and terminals across successively more agents in such a way as to reduce the maximum number of functions executed per agent. The other source of time-efficiency in the evolved solution was the cooperation that emerged among the ants in the ant colony.", notes = "SAB-96 Each tree within individual treated as an {"}agent{"}. Uses koza add/delete adf genetic operations to evolve the number of agents as well as their code.", } @InProceedings{bennet:1996:ices60db, author = "Forrest H {Bennett III} and John R. Koza and David Andre and Martin A. Keane", title = "Evolution of a 60 Decibel op amp using genetic programming", booktitle = "Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES-96)", year = "1996", editor = "Tetsuya Higuchi and Iwata Masaya and Weixin Liu", volume = "1259", series = "Lecture Notes in Computer Science", address = "Tsukuba, Japan", publisher_address = "Berlin, Germany", month = "7-8 " # oct, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-63173-9", ISSN = "0302-9743", LCCN = "QA76.618 .I57 1996", bibdate = "Mon Nov 24 10:31:37 1997", acknowledgement = ack-nhfb, URL = "http://www.genetic-programming.com/jkpdf/ices1996fhbamplifier60.pdf", size = "18 pages", abstract = "Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier with good frequency generalization.", notes = "URL=version 1 as presented at the conference http://www.etl.go.jp:8080/etl/kikou/ICES96/", } @InProceedings{bennet:1997:msrrrdpe, author = "Forrest H {Bennett III}", title = "A Multi-Skilled Robot that Recognizes and Responds to Different Problem Environments", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "genetic algorithms, genetic programming", pages = "44--51", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/bennet_1997_msrrrdpe.pdf", size = "8 pages", notes = "GP-97 two memory cells SET-D0 and SET-D1. Max rpb size 600, up to 2 ADFs (up to 200 each). Architecture altering operations. OBJECT-DIST OBJECT-KIND and ROOM-COLOR. Fitness includes time penalty. 4 rooms in continous (ie floating point) world. Program is repeatedly evaluated until 1000 timesteps or hits mine. Claims [page 49] code cant remember locations", } @InProceedings{bennett:1999:SCASE, author = "Forrest H {Bennett III} and John R. Koza and Martin A. Keane and David Andre", title = "Darwinian Programming and Engineering Design using Genetic Programming", booktitle = "Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering", year = "1999", editor = "Conor Ryan and Jim Buckley", pages = "31--40", address = "University of Limerick, Ireland", month = "12-14 " # apr, organisation = "SCARE", publisher = "Limerick University Press", keywords = "genetic algorithms, genetic programming", ISBN = "1-874653-52-6", URL = "http://www.genetic-programming.com/jkpdf/scase1999.pdf", abstract = "One of the central challenges of computer science is to build a system that can automatically create computer programs that are competitive with those produced by humans. This paper presents a candidate set of criteria that identify when a machine-created solution is competitive with a human-produced result. We argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents.", notes = "broken http://scare.csis.ul.ie/scase99/ SCASE'99 Automatic analog electrical circuit synthesis: Campbell 1917 Ladder Filter patent, Zobel 1925 {"}M-Derived Half Section{"} patent, Cauer 1934 - 1936 Elliptic patents, Darlington 1952 Emitter-Follower patent", } @InProceedings{bennet:1999:astsaecGP, author = "Forrest H {Bennett III} and Martin A. Keane and David Andre and John R. Koza", title = "Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming", booktitle = "Evolutionary Algorithms in Engineering and Computer Science", year = "1999", editor = "Kaisa Miettinen and Marko M. Makela and Pekka Neittaanmaki and Jacques Periaux", pages = "199--229", address = "Jyvaskyla, Finland", publisher_address = "Chichester, UK", month = "30 " # may # " - 3 " # jun, publisher = "John Wiley \& Sons", keywords = "genetic algorithms, genetic programming", ISBN = "0-471-99902-4", URL = "http://www.genetic-programming.com/jkpdf/eurogen1999circuits.pdf", size = "46 pages", abstract = "The design (synthesis) of an analog electrical circuit entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has previously been no general automated technique for automatically creating the design for an analog electrical circuit from a high-level statement of the circuit's desired behavior. We have demonstrated how genetic programming can be used to automate the design of seven prototypical analog circuits, including a lowpass filter, a highpass filter, a passband filter, a bandpass filter, a frequency-measuring circuit, a 60 dB amplifier, a differential amplifier, a computational circuit for the square root function, and a time-optimal robot controller circuit. All seven of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence. The approach described herein can be directly applied to many other problems of analog circuit synthesis.", notes = "EUROGEN'99 http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471999024.html ", } @InProceedings{bennett:1999:AISB, author = "Forrest H {Bennett III} and John R. Koza and Martin A. Keane and David Andre", title = "Genetic programming: Biologically inspired computation that exhibits creativity in solving non-trivial problems", booktitle = "Proceedings of the AISB'99 Symposium on Scientific Creativity", year = "1999", pages = "29--38", address = "Edingburgh", month = "8-9 " # apr, organisation = "The Society for the Study of Artificial Intelligence and Simulation of Behaviour", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.com/jkpdf/aisb1999.pdf", abstract = "This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. We argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents. This paper presents a candidate set of criteria that identify when a machine-created solution to a problem is competitive with a human-produced result.", notes = "AISB-99", } @InProceedings{bennett:1999:BPCSPHPD, author = "Forrest H {Bennett III} and John R. Koza and James Shipman and Oscar Stiffelman", title = "Building a Parallel Computer System for \$18,000 that Performs a Half Peta-Flop per Day", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1484--1490", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications, parallel computing", ISBN = "1-55860-611-4", URL = "http://www.genetic-programming.com/jkpdf/gecco1999beowulf.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-788.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-788.ps", abstract = "Techniques of evolutionary computation generally require significant computational resources to solve non-trivial problems of interest. Increases in computing power can be realized either by using a faster computer or by parallelizing the application. Techniques of evolutionary computation are especially amenable to parallelization. This paper describes how to build a 10-node Beowulf-style parallel computer system for $18,000 that delivers about a half peta-flop (1015 floating-point operations) per day on runs of genetic programming. Each of the 10 nodes of the system contains a 533 MHz Alpha processor and runs with the Linux operating system. This amount of computational power is sufficient to yield solutions (within a couple of days per problem) to 14 published problems where genetic programming has produced results that are competitive with human-produced results.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bennett:1999:EMGPACPDF, author = "Forrest H {Bennett III} and John R. Koza and Martin A. Keane and Jessen Yu and William Mydlowec and Oscar Stiffelman", title = "Evolution by Means of Genetic Programming of Analog Circuits that Perform Digital Functions", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1477--1483", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, real world applications", ISBN = "1-55860-611-4", URL = "http://www.genetic-programming.com/jkpdf/gecco1999analog.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-787.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-787.ps", abstract = "This paper demonstrates the ability of genetic programming to evolve analog circuits that perform digital functions and mixed analog-digital circuits. The evolved circuits include two purely digital circuits (a 100 nano-second NAND circuit and a two-instruction arithmetic logic unit circuit) and one mixed-signal circuit, namely a three-input digital-to-analog converter.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bennett:2000:ICES, author = "Forrest H {Bennett III} and John R. Koza and Jessen Yu and William Mydlowec", title = "Automatic synthesis, placement, and routing of an amplifier circuit by means of genetic programming", booktitle = "Evolvable Systems: From Biology to Hardware Third International Conference, ICES 2000", year = "2000", editor = "Julian Miller and Adrian Thompson and Peter Thomson and Terence C. Fogarty", volume = "1801", series = "LNCS", pages = "1--10", address = "Edinburgh, Scotland, UK", month = "17-19 " # apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67338-5", URL = "http://www.genetic-programming.com/jkpdf/ices2000.pdf", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67338-5", URL = "http://citeseer.ist.psu.edu/471655.html", abstract = "The complete design of a circuit typically includes the tasks of creating the circuit's placement and routing as well as creating its topology and component sizing. Design engineers perform these four tasks sequentially. Each of these four tasks is, by itself, either vexatious or computationally intractable. This paper describes an automatic approach in which genetic programming starts with a high-level statement of the requirements for the desired circuit and simultaneously creates the circuit's topology, component sizing, placement, and routing as part of a single integrated design process. The approach is illustrated using the problem of designing a 60 decibel amplifier. The fitness measure considers the gain, bias, and distortion of the candidate circuit as well as the area occupied by the circuit after the automatic placement and routing.", notes = "ICES-2000", } @InProceedings{Bennett:2000:GECCOlb, author = "Forrest H {Bennett III} and Eleanor G. Rieffel", title = "Using Genetic Programming to Design Decentralized Controllers for Self-Reconfigurable Modular Robots", pages = "35--42", booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference", year = "2000", editor = "Darrell Whitley", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "Part of \cite{whitley:2000:GECCOlb}", } @InProceedings{bennett:2000:EH, author = "Forrest H {Bennett III} and Eleanor G. Rieffel", title = "Design of Decentralized Controllers for Self-Reconfigurable Modular Robots Using Genetic Programming", booktitle = "Proceedings of the Second NASA / DoD Workshop on Evolvable Hardware", year = "2000", pages = "43--52", address = "Palo Alto, California", month = jul # " 13-15", organisation = "Jet Propulsion Laboratory, California Institute of Technology", publisher = "IEEE Computer Society", keywords = "genetic algorithms, genetic programming, connectivity topology, control software, decentralized controllers, distributed controllers, physical adaptability, robustness, self-reconfigurable modular robots, control engineering computing, controllers, decentralised control", ISBN = "0-7695-0762-X", DOI = "doi:10.1109/EH.2000.869341", abstract = "Advantages of self-reconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale. Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robots differ enough from traditional robots that new techniques must be found to create software to control them. The novel difficulties are due to the fact that modular robots are ideally controlled in a decentralized manner, dynamically change their connectivity topology, may contain hundreds or thousands of modules, and are expected to perform tasks properly even when some modules fail. We demonstrate the use of genetic programming to automatically create distributed controllers for self-reconfigurable modular robots. .", notes = "EH-2000 http://ic-www.arc.nasa.gov/ic/eh2000/index.html http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm", } @InProceedings{bennett:2001:EuroGP, author = "Forrest H {Bennett III} and Brad Dolin and Eleanor G. Rieffel", title = "Programmable Smart Membranes: Using Genetic Programming to Evolve Scalable Distributed Controllers for a Novel Self-Reconfigurable Modular Robotic Application", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "234--245", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, modular robot, distributed control, smart membrane, self-reconfigurable, scalable, robust: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_18", size = "12 pages", abstract = "Self-reconfigurable modular robotics represents a new approach to robotic hardware, in which the robot is composed of many simple, identical interacting modules. We propose a novel application of modular robotics: the programmable smart membrane, a device capable of actively filtering objects based on numerous measurable attributes. Creating control software for modular robotic tasks like the smart membrane is one of the central challenges to realizing their potential advantages. We use genetic programming to evolve distributed control software for a 2-dimensional smart membrane capable of distinguishing objects based on color. The evolved controllers exhibit scalability to a large number of modules and robustness to the initial configurations of the robotic filter and the particles.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{benolic:2022:SICAAI, author = "Leo Benolic and Zlatan Car and Nenad Filipovic", title = "Mathematical Modeling of {COVID-19} Spread Using Genetic Programming Algorithm", booktitle = "1st Serbian International Conference on Applied Artificial Intelligence", year = "2022", address = "Kragujevac, Serbia", month = may # " 19-20", publisher = "Springer", keywords = "genetic algorithms, genetic programming, artificial intelligence, COVID-19, mathematical prediction models, variants", URL = "http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip", URL = "http://link.springer.com/chapter/10.1007/978-3-031-29717-5_19", DOI = "doi:10.1007/978-3-031-29717-5_19", notes = "http://www.aai2022.kg.ac.rs/aai-2022-papers/ SICAAI proceedings published by Springer in 2023 doi:10.1007/978-3-031-29717-5", } @Article{benouhiba:2021:FI, author = "Toufik Benouhiba", title = "A Multi-level Refinement Approach for Structural Synthesis of Optimal Probabilistic Models", journal = "Fundamenta Informaticae", year = "2021", volume = "179", number = "1", pages = "1--33", keywords = "genetic algorithms, genetic programming, model synthesis, refinement, Search-based software engineering, SBSE, constraint satisfaction, probabilistic model checking", publisher = "IOS press", URL = "https://journals.sagepub.com/doi/pdf/10.3233/FI-2021-2011", DOI = "doi:10.3233/FI-2021-2011", size = "33 pages", abstract = "Probabilistic models play an important role in many fields such as distributed systems and simulations. Like non-probabilistic systems, they can be synthesized using classical refinement-based techniques, but they also require identifying the probability distributions to be used and their parameters. Since a fully automated and blind refinement is generally undecidable, many works tried to synthesize them by looking for the parameters of the distributions. Syntax-guided synthesizing approaches are more powerful, they try to synthesize models structurally by using context-free grammars. However, many problems arise like huge search space, the complexity of generated models, and the limitation of context-free grammars to define constraints over the structure. In this paper, we propose a multi-step refinement approach, based on meta-models, offering several abstraction levels to reduce the size of the search space. More specifically, each refinement step is divided into two stages in which the desired shape of models is first described by context-sensitive constraints. In the second stage, model templates are instantiated by using global optimization techniques. We use our approach to a synthesize a set of optimal probabilistic models and show that context-sensitive constraints coupled with the multi-level abilities of the approach make the synthesis task more effective.", notes = "LISCO Laboratory - Department of computer science, Badji Mokthar Annaba University, PO BOX 12, Annaba, Algeria", } @InProceedings{benson:2000:E, author = "Karl Benson", title = "Evolving automatic target detection algorithms", booktitle = "Graduate Student Workshop", year = "2000", editor = "Conor Ryan and Una-May O'Reilly and William B. Langdon", pages = "249--252", address = "Las Vegas, Nevada, USA", month = "8 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}", } @InProceedings{Benson:2000:GECCO, author = "Karl A Benson and David Booth and James Cubillo and Colin Reeves", title = "Automatic Detection of Ships in Spaceborne {SAR} Imagery", pages = "767", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming, ANN, Poster", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW002.ps", size = "1 page", abstract = "This paper examines the evolution of automatic target detection algorithms and their application to the detection of shipping in spaceborne SAR imagery. ... . The FSM(GP) is clearly superior.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO} ", } @InProceedings{benson:2000:efsmegpatdsi, author = "Karl A Benson", title = "Evolving Finite State Machines with Embedded Genetic Programming for Automatic Target Detection within SAR Imagery", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "1543--1549", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, image processing applications, Kohonen neural networks, automatic target detection, control structure, embedded genetic programming, evolving finite state machines, node cardinality, symbiotic relationship, embedded systems, finite state machines, object detection, self-organising feature maps", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870838", abstract = "This paper presents a model comprising Finite State Machines (FSMs) with embedded Genetic Programs (GPs) which co-evolve to perform the task of Automatic Target Detection (ATD). The fusion of a FSM and GPs allows for a control structure (main program), the FSM, and sub-programs, the GPs, to co-evolve in a symbiotic relationship. The GP outputs along with the FSM state transition levels are used to construct confidence intervals that enable each pixel within the image to be classified as either target or non-target, or to cause a state transition to take place and further analysis of the pixel to be performed. The algorithms produced using this method consist of nominally four GPs, with a typical node cardinality of less than ten, that are executed in an order dictated by the FSM. The results of the experimentation performed are compared to those obtained in two independent studies of the same problem using Kohonen Neural Networks and a two stage Genetic Programming strategy.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 ", } @InProceedings{benson:2000:PCEMMA, author = "Karl Benson", title = "Performing Classification with an Environment Manipulating Mutable Automata (EMMA)", booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00", year = "2000", pages = "264--271", address = "La Jolla Marriott Hotel La Jolla, California, USA", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "6-9 " # jul, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, system modelling and control, EMMA algorithm, classification, complex decision space, environment manipulating mutable automata, feature selection, finite state automata, hypersurface discriminant functions, object detection, symbiotic architecture, finite state machines, object detection, pattern classification", ISBN = "0-7803-6375-2", DOI = "doi:10.1109/CEC.2000.870305", size = "8 pages", abstract = "In this paper a novel approach to performing classification is presented. Hypersurface Discriminant functions are evolved using Genetic Programming. These discriminant functions reside in the states of a Finite State Automata, which has the ability to reason 1 and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each dis-criminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used.", notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 ", } @InProceedings{benson4, author = "Karl A Benson and David Booth and James Cubillo and Colin Reeves", title = "On the use of evolution to construct finite state machines and mathematical functions to perform automatic target detection", booktitle = "Proceedings of the 3rd {IMA} conference on image processing: mathematical methods, algorithms and applications", year = "2000", address = "Leicester, UK", month = "13-15 " # sep, publisher = "IEE, Horwood Publishing Ltd", organisation = "The Institute of Mathematics and its Applications, The Institute of Physics, The Institute of Electrical Engineers", editor = "Martin J. Turner and Jonathan M. Blacklege", publisher_address = "Chichester, UK", keywords = "genetic algorithms, genetic programming", ISBN = "1-898563-72-1", URL = "http://www.amazon.co.uk/Image-Processing-III-Mathematical-Applications/dp/1898563721", notes = "Book derived from proceedings IMA http://www.ima.org.uk/ ", } @InProceedings{benson5, author = "Karl A Benson", title = "Evolving Automatic Target Detection Algorithms that logically Combine Decision Spaces", booktitle = "Proceedings of the 11th British Machine Vision Conference", year = "2000", pages = "685--694", address = "Bristol, UK", month = "11-14 " # sep, editor = "Majid Mirmehdi and Barry Thomas", publisher = "BMVA Press", keywords = "genetic algorithms, genetic programming, classification", ISBN = "1-901725-13-8", URL = "http://www.bmva.ac.uk/bmvc/2000/papers/p69.pdf", size = "10 pages", abstract = "In this paper a novel approach to performing classification is presented. Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as + - times divide max min. These discriminant functions are capable of forming nonlinear discontinuous hypersurfaces. For multimodal data more than one discriminant function maybe combined with logical operators before classification is performed. An algorithm capable of making decisions as to whether a combination of discriminant functions is needed to classify a data sample, or whether a single discriminant function will suffice, is developed. The algorithms used to perform classification are not written by a human. The algorithms are learnt, or rather evolved, using Evolutionary Computing techniques.", notes = " https://hdl.handle.net/1983/32ddefe3-ebe8-4571-b80b-4f7ad4f949d9", } @Misc{bensoussan2024acceleratingquantumeigensolveralgorithms, author = "Avner Bensoussan and Elena Chachkarova and Karine Even-Mendoza and Sophie Fortz and Connor Lenihan", title = "Accelerating Quantum Eigensolver Algorithms With Machine Learning", howpublished = "arXiv:2409.13587 v1", year = "2024", month = "20 " # sep, keywords = "genetic algorithms, genetic programming, genetic improvement, quant-ph", URL = "https://arxiv.org/abs/2409.13587", size = "19 pages", abstract = "we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver hyperparameters. These models predict hyperparameter values, leading to a 0.13-0.15 percent reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters.", } @PhdThesis{Bensusan:thesis, author = "Hilan N. Bensusan", title = "Automatic bias learning: an inquiry into the inductive basis of induction", school = "University of Sussex", year = "1999", type = "D. Phil.", address = "UK", month = feb, keywords = "genetic algorithms, genetic programming, CIGA", URL = "http://www.cs.bris.ac.uk/Publications/Papers/1000410.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.787184", size = "217 pages", abstract = "This thesis combines an epistemological concern about induction with a computational exploration of inductive mechanisms. It aims to investigate how inductive performance could be improved by using induction to select appropriate generalisation procedures. The thesis revolves around a meta-learning system, called The Entrencher, designed to investigate how inductive performances could be improved by using induction to select appropriate generalisation procedures. The performance of The Entrencher is discussed against the background of epistemological issues concerning induction, such as the role of theoretical vocabularies and the value of simplicity. After an introduction about machine learning and epistemological concerns with induction, Part I looks at learning mechanisms. It reviews some concepts and issues in machine learning and presents The Entrencher. The system is the first attempt to develop a learning system that induces over learning mechanisms through algorithmic representations of tasks. Part II deals with the need for theoretical terms in induction. Experiments where The Entrencher selects between different strategies for representation change are reported. The system is compared to other methods and some conclusions are drawn concerning how best to use the system. Part III considers the connection between simplicity and inductive success. Arguments for Occam's razor are considered and experiments are reported where The Entrencher is used to select when, how and how much a decision tree needs to be pruned. Part IV looks at some philosophical consequences of the picture of induction that emerges from the experiments with The Entrencher and goes over the motivations for meta-learning. Based on the picture of induction that emerges in the thesis, a new position in the scientific realism debate, transcendental surrealism, is proposed and defended. The thesis closes with some considerations concerning induction, justification and epistemological naturalism.", notes = "System in \cite{bensusan:1996:ciGP} called CIGA Constructive induction with a Genetic Algorithm COGS supervisor: Peter Williams", } @PhdThesis{Katie_Bentley:thesis, author = "Katie Anne Bentley", title = "Adaptive Behaviour through Morphological Plasticity in Natural and Artificial Systems", school = "Computer Science, UCL, University of London", year = "2006", address = "Gower Street, London, UK", keywords = "genetic algorithms, alife", URL = "http://discovery.ucl.ac.uk/1444539/1/U591845.pdf", size = "222 pages", notes = "Not GP. UMI U591845 Published by ProQuest Supervisor: Chris Clack", } @InProceedings{Bentley:1997:WSC2, author = "P. J. Bentley and J. P. Wakefield", title = "Generic Evolutionary Design", booktitle = "Soft Computing in Engineering Design and Manufacturing", year = "1997", editor = "Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant", publisher_address = "Godalming, GU7 3DJ, UK", month = "23-27 " # jun, publisher = "Springer-Verlag", pages = "289--298", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-76214-0", URL = "http://eprints.hud.ac.uk/4053/", URL = "http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0", DOI = "doi:10.1007/978-1-4471-0427-8_31", size = "10 pages", abstract = "Generic evolutionary design means the creation of a range of different designs by evolution. This paper introduces generic evolutionary design by a computer, describing a system capable of the evolution of a wide range of solid object designs from scratch, using a genetic algorithm. The paper reviews relevant literature, and outlines a number of advances necessitated by the development of the system, including: a new generic representation of solid objects, a new multiobjective fitness ranking method, and variable-length chromosomes. A library of modular evaluation software is also described, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs. Finally, the feasibility of generic evolutionary design by a computer is demonstrated by presenting the successful evolution of both conventional and unconventional designs for a range of different solid-object design tasks, e.g. tables, heatsinks, prisms, boat hulls, aerodynamic cars.", notes = "published 1998?", } @InProceedings{Bentley97, author = "P. J. Bentley and J. P. Wakefield", title = "Finding Acceptable Solutions in the {Pareto-Optimal} Range using Multiobjective Genetic Algorithms", booktitle = "Soft Computing in Engineering Design and Manufacturing", year = "1997", editor = "P. K. Chawdhry and R. Roy and R. K. Pant", pages = "231--240", publisher_address = "Godalming, GU7 3DJ, UK", month = "23-27 " # jun, publisher = "Springer-Verlag London", keywords = "genetic algorithms, MOGA", ISBN = "3-540-76214-0", URL = "http://eprints.hud.ac.uk/4052/", abstract = "This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to converge on the subset of acceptable solutions is then introduced. The paper then describes the application of six multiobjective techniques (three established methods and three new, or less commonly used methods) to four test functions. The previously unpublished distribution of solutions produced in the P-O range(s) by each method is described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with the conclusion that two of the new multiobjective ranking methods are most useful.", notes = "cited by \cite{Ross:2011:GPEM}. WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing", size = "10 pages", } @InProceedings{Bentley:1999:AVOCAAD, author = "Peter J. Bentley", title = "The Future of Evolutionary Design Research", booktitle = "AVOCAAD Second International Conference", year = "1999", pages = "349--350", address = "Brussels, Belgium", month = "8-10 " # apr, keywords = "genetic algorithms, genetic programming, Computer, design, International", URL = "http://cumincad.scix.net/cgi-bin/works/BrowseTree?field=series&separator=:&recurse=0&order=AZ&value=AVOCAAD", URL = "http://cumincad.scix.net/cgi-bin/works/Show?616c", URL = "http://papers.cumincad.org/data/works/att/616c.content.pdf", broken = "http://eprints.ucl.ac.uk/171652/", size = "2 pages", abstract = "The use of evolutionary algorithms to optimise designs is now well known, and well understood. The literature is overflowing with examples of designs that bear the hallmark of evolutionary optimisation: bridges, cranes, electricity pylons, electric motors, engine blocks, flywheels, satellite booms -the list is extensive and ever growing. But although the optimisation of engineering designs is perhaps the most practical and commercially beneficial form of evolutionary design for industry, such applications do not take advantage of the full potential of evolutionary design. Current research is now exploring how the related areas of evolutionary design such as evolutionary art, music and the evolution of artificial life can aid in the creation of new designs. By employing techniques from these fields, researchers are now moving away from straight optimisation, and are beginning to experiment with explorative approaches. Instead of using evolution as an optimiser, evolution is now beginning to be seen as an aid to creativity - providing new forms, new structures and even new concepts for designers.", notes = "Workshop on Morphogenetic Design, in the 2nd International Conference on Added Value of Computer Aided Architectural Design (AVOCAAD), Brussels, Belgium See also UCL CS technical report RN/99/12 http://www.cs.ucl.ac.uk/research/rns/rns99.html Sep 2018 616c.content.pdf suggests pages are 337--338", publicationstatus = "published", } @InProceedings{Bentley:1999:AISB, author = "P. J. Bentley", title = "Is evolution creative?", booktitle = "Proceedings of the AISB'99 Symposium on Creative Evolutionary Systems", year = "1999", editor = "P. J. Bentley and D. Corne", pages = "28--34", address = "Edinburgh", publisher = "The Society for the Study of Artificial Intelligence and Simulation of Behaviour", keywords = "genetic algorithms, genetic programming, gades, CE, sussex, System, systems", ISBN = "1-902956-03-6", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC6.pdf", publicationstatus = "published", size = "7 pages", abstract = "Can evolution demonstrate some of the properties of creativity? This paper argues that it can, and provides examples which the author feels illustrate some of the awesome power and feats of design which resemble creativity. Is evolution, then, truly creative? This is clearly a much harder question, for it requires a definition of creativity that most subjective and controversial of words. This paper explores and discusses various aspects of creativity, attempting to determine to what extent evolution satisfies each definition. The paper ends by summarising the discussion, and presenting amalgamations of four different worldviews.", notes = "The AISB'99 Convention took place in March 1999, hosted jointly by the University of Edinburgh and the Edinburgh College of Art", } @InProceedings{bentley:1999:TWGDACEEDP, author = "Peter Bentley and Sanjeev Kumar", title = "Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "35--43", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-611-4", URL = "http://www.cs.ucl.ac.uk/staff/p.bentley/BEKUC1.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-329.ps", URL = "http://dl.acm.org/citation.cfm?id=2933923.2933928", acmid = "2933928", size = "9 pages", abstract = "This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant features of embryogenies, the three main types of embryogenies in Evolutionary Computation are then identified and explained: external, explicit and implicit. An experimental comparison between these three different embryogenies and an evolutionary algorithm with no embryogeny is performed. The problem set to the four evolutionary systems is to evolve tessellating tiles. In order to assess the scalability of the embryogenies, the problem is increased in difficulty by enlarging the size of tiles to be evolved. The results are surprising, with the implicit embryogeny outperforming all other techniques by showing no significant increase in the size of the genotypes or decrease in accuracy of evolution as the scale of the problem is increased.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bentley:1999:EA, author = "Peter J. Bentley", title = "Evolving fuzzy detectives: An investigation into the evolution of fuzzy rules", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "38--47", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC7.pdf", abstract = "This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise.", notes = "GECCO-99LB, fraud detection, pre-GP 3-way clustering of each attribute multi-objective fitness function. variable size tree genotypes, bitstring in tree specifies input field, start small. Newer version available \cite{bentley:2000:EA} Iris and Wisconsin Breast Cancer. Perfomance falls lineraly or quadratically with noise.", } @Book{Bentley:evdes, editor = "Peter J. Bentley", title = "Evolutionary Design by Computers", publisher = "Morgan Kaufmann", year = "1999", keywords = "genetic algorithms, genetic programming, Computers", ISBN = "1-55860-605-X", isbn13 = "9781558606050", URL = "http://www.cs.ucl.ac.uk/staff/p.bentley/evdes.html", URL = "http://www.amazon.com/Evolutionary-Design-Computers-Peter-Bentley/dp/155860605X", abstract = "By bringing together the highest achievers in these fields for the first time, including a foreword by Richard Dawkins, this book provides the definitive ...", } @InCollection{Bentley:1999:intro, author = "Peter Bentley", title = "An introduction to evolutionary design by computers", booktitle = "Evolutionary Design by Computers", publisher = "Morgan Kaufman", year = "1999", editor = "Peter J. Bentley", chapter = "1", pages = "1--73", address = "San Francisco, USA", keywords = "genetic algorithms, genetic programming, Computer, Computers, design", notes = "Part of \cite{Bentley:evdes}", } @InProceedings{Bentley:1999:WSC, author = "Peter J. Bentley", booktitle = "Soft Computing in Industrial Applications", publisher = "Springer-Verlag London", title = "Evolving fuzzy detectives: an investigation into the evolution of fuzzy rules", year = "1999", editor = "Yukinori Suzuki and Seppo J. Ovaska and Takeshi Furuhashi and Rajkumar Roy and Yasuhiko Dote", pages = "89--106", month = sep, keywords = "genetic algorithms, genetic programming, evolution, fuzzy, industrial, industrial application, Rules", ISBN = "1-85233-293-X", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BECH4.pdf", URL = "http://www.amazon.com/Computing-Industrial-Applications-Yukinori-Suzuki/dp/185233293X", DOI = "doi:10.1007/978-1-4471-0509-1_8", size = "18 pages", abstract = "This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate to the square of noise.", notes = "WSC4", } @InProceedings{Bentley:2000:ACDM, author = "P. J. Bentley", title = "Exploring component-based representations - the secret of creativity by evolution?", booktitle = "Evolutionary Design and Manufacture: Selected Papers from ACDM'00", year = "2000", editor = "I. C. Parmee", pages = "161--172", address = "University of Plymouth, Devon, UK", publisher_address = "Berlin/Heidelberg, Germany", month = apr, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Adaptive, design", isbn13 = "9781852333003", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC9.pdf", URL = "http://www.springer.com/engineering/mechanical+eng/book/978-1-85233-300-3", URL = "http://www.springer.com/engineering/mechanical+engineering/book/978-1-85233-300-3", size = "12 pages", abstract = "This paper investigates one of the newest and most exciting methods in computer science to date: employing computers as creative problem solvers by using evolution to explore for new solutions. The paper introduces and discusses the new understanding that explorative evolution relies upon a representation based on components rather than a parameterisation of a known solution. Evolution explores how the components can be arranged, how many are needed, and the type or function of each. The extra freedom provided by this simple idea is remarkable. By using evolutionary computation for exploration instead of optimisation, this technique enables us to expand the capabilities of computers. The paper describes how the approach has already shown impressive results in the creation of novel designs and architecture, fraud detection, composition of music, and creation of art. A framework for explorative evolution is provided, with discussion of the significance and difficulties posed by each element. The paper ends with an example of creative problem solving for a simple application- showing how evolution can shape pieces of paper to make them fall slowly through the air, by spiraling down like sycamore seeds.", notes = "The Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM 2000) was held at the Evolutionary design and manufacture: selected papers from ACDM'00", } @InProceedings{Bentley:2000:EA, author = "Peter J. Bentley", title = "``Evolutionary, my dear Watson'' Investigating Committee-based Evolution of Fuzzy Rules for the Detection of Suspicious Insurance Claims", pages = "702--709", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW074.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/RW074.ps", size = "8 pages", notes = "See also \cite{bentley:1999:EA} A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{bentley:2001:NTEC, author = "Peter J. Bentley and Timothy Gordon and Jungwon Kim and Sanjeev Kumar", title = "New Trends in Evolutionary Computation", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", volume = "1", pages = "162--169", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, new trends, creative evolution, computation embryology, evolvable hardware, artificial immune systems", ISBN = "0-7803-6658-1", DOI = "doi:10.1109/CEC.2001.934385", size = "8 pages", abstract = "In the last five years, the field of evolutionary computation (EC) has seen a resurgence of new ideas, many stemming from new biological inspirations. The paper outlines four of these new branches of research: creative evolutionary systems, computational embryology, evolvable hardware and artificial immune systems, showing how they aim to extend the capabilities of EC. Recent, unpublished results by researchers in each area at the Department of Computer Science, UCL are provided", notes = "gades CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =", } @InProceedings{Bentley:2001:geccowks, author = "Peter J. Bentley and Una-May O'Reilly", title = "Ten steps to make a perfect creative evolutionary design system", booktitle = "Non-Routine Design with Evolutionary Systems, GECCO-2001 Workshop", year = "2001", editor = "Peter Bentley and Mary Lou Maher and Josiah Poon", month = "7 " # jul, keywords = "genetic algorithms, genetic programming, Agency GP, design, evolutionary, SYSTEM, SYSTEMS, WORKSHOP", URL = "http://sydney.edu.au/engineering/it/~josiah/gecco_workshop_bentley.pdf", size = "7 pages", abstract = "A perfect creative evolutionary design system is impossible to achieve, but in this position paper we discuss 10 steps that might bring us a little closer to this dream. These important problems and requirements have been identified as a result of both authors' experiences on a number of projects in this area. While our solutions may not solve all of the problems, they illustrate what we regard as the current state of the art in creative evolutionary design.", notes = "broken Jun 2020 http://sydney.edu.au/engineering/it/~josiah/gecco2001_workshop_schedule.html", } @InCollection{bentley:2001:CES, author = "Peter J. Bentley and David W. Corne", title = "An Introduction to Creative Evolutionary Systems", booktitle = "Creative Evolutionary Systems", publisher = "Morgan Kaufmann", year = "2001", editor = "Peter J. Bentley and David W. Corne", pages = "1--75", month = jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-673-4", URL = "http://www.sciencedirect.com/science/book/9781558606739", DOI = "doi:10.1016/B978-155860673-9/50035-5", abstract = "This chapter presents an overview of evolutionary algorithms explaining the main algorithms, and showing how all evolutionary algorithms are fundamentally the same. Evolutionary computation is all about search. In computer science and in artificial intelligence, when we use a search algorithm, we define a computational problem in terms of a search space, which can be viewed as a massive collection of potential solutions to the problem. Any position, or point, in the search space defines a particular solution, and the process of search can be viewed as the task of navigating that space. The chapter then defines and describes creative evolutionary systems, explaining why they were developed and how user interaction and changes of representation can expand the capabilities of evolution. The chapter also explores whether a creative evolutionary system can be said to actually work creatively. This chapter provides a mere introduction to the diversity of techniques that fall under the heading of creative evolutionary systems.", notes = "GP include amongst other EC techniques. Part of \cite{Bentley:2002:bookCES}", size = "75 pages", } @Book{Bentley:2002:bookCES, editor = "Peter Bentley and David Corne", title = "Creative evolutionary systems", year = "2002", publisher = "Morgan Kaufmann", address = "USA", keywords = "genetic algorithms, genetic programming, Computers", ISBN = "1-55860-673-4", isbn13 = "9781558606739", URL = "http://www.amazon.com/Creative-Evolutionary-Kaufmann-Artificial-Intelligence/dp/1558606734", abstract = "This book concentrates on applying important ideas in evolutionary computation to creative areas, such as art, music, architecture, and design.", notes = "Chapters on GP", } @Book{Bentley:2002:DB, author = "Peter J. Bentley", title = "Digital Biology. How Nature is Transforming Our Technology and Our Lives", publisher = "Simon and Schuster", year = "2002", address = "USA", keywords = "genetic algorithms, genetic programming, biology, digital, nature, technology", ISBN = "0-7432-0447-6", URL = "http://www.amazon.com/Digital-Biology-Peter-J-Bentley/dp/0743204476", notes = "Hardback", size = "272 pages", } @Article{bentley:2003:GPEM, author = "Peter J. Bentley and Jon Timmis", title = "Guest Editorial Special Issue on Artificial Immune Systems", journal = "Genetic Programming and Evolvable Machines", year = "2003", volume = "4", number = "4", pages = "307--309", month = dec, keywords = "artificial immune systems", ISSN = "1389-2576", DOI = "doi:10.1023/A:1026182810701", notes = "Special issue on artificial immune systems. Article ID: 5144845", } @Article{bentley:2004:GPEM, author = "Peter J. Bentley", title = "Fractal Proteins", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "1", pages = "71--101", month = mar, keywords = "genetic algorithms, fractal proteins, development, evolvability, scalability, complexity", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000017011.51324.d2", abstract = "The fractal protein is a new concept intended to improve evolvability, scalability, exploitability and provide a rich medium for evolutionary computation. Here the idea of fractal proteins and fractal proteins with concentration levels are introduced, and a series of experiments showing how evolution can design and exploit them within gene regulatory networks is described.", notes = "Article ID: 5264735", } @Proceedings{DBLP:conf/icaris/2008, editor = "Peter J. Bentley and Doheon Lee and Sungwon Jung", title = "7th International Conference on Artificial Immune Systems, ICARIS 2008", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "5132", year = "2008", address = "Phuket, Thailand", month = aug # " 10-13", isbn13 = "978-3-540-85071-7", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "This book constitutes the refereed proceedings of the 7th International Conference on Artificial Immune Systems, ICARIS 2008, held in Phuket, Thailand, in ...", ISBN = "3-540-85071-6", isbn13 = "9783540850717", keywords = "Computers", } @InProceedings{Bentley:2017:ieeeSSCI, author = "P. J. Bentley and S. L. Lim", booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Fault tolerant fusion of office sensor data using cartesian genetic programming", year = "2017", abstract = "The Smart Grid of the future will enable a cleaner, more efficient and fault tolerant system of power distribution. Sensing power use and predicting demand is an important component in the Smart Grid. In this work, we describe a Cartesian Genetic Programming (CGP) system applied to a smart office. In the building, power usage is directly proportional to the number of people present. CGP is used to perform data fusion on the data collected from smart sensors embedded in the building in order to predict the number of people over a two-month period. This is a challenging task, as the sensors are unreliable, resulting in incomplete data. It is also challenging because in addition to normal staff, the building underwent renovation during the test period, resulting the presence of additional personnel who would not normally be present. Despite these difficult real-world issues, CGP was able to learn human-readable rules that when used in combination, provide a method for data fusion that is tolerant to the observed faults in the sensors.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/SSCI.2017.8280827", month = nov, notes = "Also known as \cite{8280827}", } @Book{bentley:2024:AIchunks, author = "Peter J. Bentley", title = "Artificial Intelligence in Byte-sized Chunks", publisher = "Michael O'Mara", year = "2024", month = "20 " # jun, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1789296563", URL = "https://www.amazon.co.uk/Artificial-Intelligence-Byte-sized-Chunks-Bite-Sized/dp/1789296560", size = "224 pages", abstract = "Artificial intelligence is headline news with the launch of the latest ChatGPT and Google Gemini. But when did we start making computers mimic the human mind? And what is the reality of the capabilities of AI now, and in the future?", } @InProceedings{benhahia:1997:GPvd, author = "Ilham Benyahia and J. Yves Potvin", title = "Genetic Programming for Vehicle Dispatch", booktitle = "Proceedings of the 1997 {IEEE} International Conference on Evolutionary Computation", year = "1997", pages = "547--552", address = "Indianapolis, USA", publisher_address = "Piscataway, NJ, USA", month = "13-16 " # apr, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICEC.1997.592371", abstract = "Vehicle dispatching is aimed at allocating real time service requests to a fleet of vehicles in movement. This task is modeled as a multiattribute choice problem. Namely, different attribute values are associated with each vehicle to describe its situation with respect to the current service request. Based on this attribute description, a utility function that approximates the decision process of a professional dispatcher is computed. This utility function evolves through genetic programming. Computational results are reported on requests collected from a courier service company", notes = "ICEC-97", } @Article{Benyahia:1998:SMC, author = "Ilham Benyahia and Jean-Yves Potvin", title = "Decision Support for Vehicle Dispatching Using Genetic Programming", journal = "IEEE Transactions on Systems, Man, and Cybernetics part A: systems and humans", year = "1998", volume = "28", number = "3", pages = "306--314", month = may, keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/iel4/3468/14669/00668962.pdf", size = "9 pages", abstract = "Vehicle dispatching consists of allocating real-time service requests to a fleet of moving vehicles. In this paper, each vehicle is associated with a vector of attribute values that describes its current situation with respect to new incoming service requests. Using this attribute description, a utility function aimed at approximating the decision process of a professional dispatcher is constructed through genetic programming. Computational results are reported on requests collected from a courier service company and a comparison is provided with a neural network model and a simple dispatching policy.", } @InProceedings{conf/dms/BenyahiaT08, title = "Optimizing the Architecture of Adaptive Complex Applications Using Genetic Programming", author = "Ilham Benyahia and Vincent Talbot", publisher = "Knowledge Systems Institute", year = "2008", bibdate = "2009-06-06", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/dms/dms2008.html#BenyahiaT08", pages = "27--31", booktitle = "The 14th International Conference on Distributed Multimedia Systems, DMS'2008", address = "Hyatt Harborside at Logan Int'l Airport, Boston, USA", month = "4-6 " # sep, organisation = "Knowledge Systems Institute", keywords = "genetic algorithms, genetic programming", notes = "http://www.ksi.edu/seke/dms08.html See also \cite{Talbot:2010:IJSEA} http://citeseerx.ist.psu.edu/showciting?cid=11109136", } @PhdThesis{Benzeroual:thesis, author = "Karim Benzeroual", title = "Acquisition d'images steteoscopiques et calibration de cameras par algorithmes genetiques : application dans le domaine biomedical", title2 = "Stereoscopic image acquisition and camera calibration with genetic algorithm", school = "Ecole doctorale Sante, sciences, technologies, Tours", year = "2010", address = "France", month = "20 " # jul, keywords = "genetic algorithms", URL = "http://www.applis.univ-tours.fr/theses/2010/karim.benzeroual_3432.pdf", URL = "http://www.theses.fr/?q=benzeroual", URL = "http://www.theses.fr/2010TOUR4011", size = "215 pages", abstract = "This thesis Acquisition of stereoscopic images and camera calibration with genetic algorithms: application in the biomedical domain‖ consists to define a complete tool for acquiring the topography and texture of a 3D surface in order to apply it in the dermatological and more generally in the biomedical field. First, the objective is to study and to specify or design hardware devices such as professional cameras, assembly systems for stereo equipments, lighting system and trigger system for devices. Then, the thesis focuses on algorithmic and software aspects which relate to all mathematic and computational treatments needed to obtain a 3D surface. One of major issues addressed is the geometric calibration of stereo cameras. The developed approach pushes the limits of conventional methods in this field by proposing the use of more efficient and easier to implement optimization methods. We have shown that the algorithms using the principles of genetic algorithms can obtain more reliable results than their competitors and they can deal more easily the variable conditions of experiments. The real applications of our genetic algorithms for camera calibration cover many acquisition devices (industrial cameras, SLR cameras, stereo microscopes, beam splitters, pinhole and telecentric objectives), each acquisition device is adapted to a specific use following the requested study (microscopic areas, face or body parts). This thesis acquisition/calibration is a part of a global system called VirtualSkinLAB.", notes = "In French. Not GP? http://karim.benzeroual.com Supervisor: Gilles Venturini", } @Article{Berardi:2008:JH, author = "L. Berardi and Z. Kapelan and O. Giustolisi and D. A. Savic", title = "Development of pipe deterioration models for water distribution systems using {EPR}", journal = "Journal of Hydroinformatics", year = "2008", volume = "10", number = "2", pages = "113--126", keywords = "genetic algorithms, genetic programming, data-driven modelling, evolutionary polynomial regression, failure analysis, performance indicators, water systems", ISSN = "1464-7141", URL = "http://www.iwaponline.com/jh/010/0113/0100113.pdf", DOI = "doi:10.2166/hydro.2008.012", size = "14 pages", abstract = "The economic and social costs of pipe failures in water and wastewater systems are increasing, putting pressure on utility managers to develop annual replacement plans for critical pipes that balance investment with expected benefits in a risk-based management context. In addition to the need for a strategy for solving such a multi-objective problem, analysts and water system managers need reliable and robust failure models for assessing network performance. In particular, they are interested in assessing a conduit's propensity to fail and how to assign criticality to an individual pipe segment. pipe deterioration is modelled using Evolutionary Polynomial Regression. This data-driven technique yields symbolic formulae that are intuitive and easily understandable by practitioners. The case study involves a water quality zone within a distribution system and entails the collection of historical data to develop network performance indicators. Finally, an approach for incorporating such indicators into a decision support system for pipe rehabilitation/replacement planning is introduced and articulated.", notes = "Fig 5 bathtub curve Hydroinformatics Group, Technical University of Bari, via Orabona 4, I-70125, Bari, Italy", } @Article{Berardi:2009:WST, author = "L. Berardi and O. Giustolisi and D. A. Savic and Z. Kapelan", title = "An effective multi-objective approach to prioritisation of sewer pipe inspection", journal = "Water Science \& Technology", year = "2009", volume = "60", number = "4", pages = "841--850", keywords = "genetic algorithms, genetic programming, EPR, decision support, multi-objective optimisation, pipe inspection, prioritisation, sewer, CCTV", DOI = "doi:10.2166/wst.2009.432", size = "10 pages", abstract = "The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimisation problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK.", notes = "Papers using GP related results WST, IWA Publishing Civil and Environmental Engineering Department, Technical University of Bari, via Orabona 4, 70125, Bari, Italy E-mail: l.berardi@poliba.it; o.giustolisi@poliba.it Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, North Park Road, Exeter EX4 4QF, UK E-mail: d.savic@exeter.ac.uk; z.kapelan@exeter.ac.uk", } @InProceedings{DBLP:conf/maics/BerarducciJMS04, author = "Patrick Berarducci and Demetrius Jordan and David Martin and Jennifer Seitzer", title = "{GEVOSH}: Using Grammatical Evolution to Generate Hashing Functions", booktitle = "Proceedings of the Fifteenth Midwest Artificial Intelligence and Cognitive Sciences Conference, MAICS 2004", year = "2004", editor = "Eric G. Berkowitz", pages = "31--39", address = "Chicago, USA", month = apr # " 16-18", organisation = "Roosevelt University, Computer Science and Telecommunications", publisher = "Omnipress", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, genetic improvement", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.4612.pdf", size = "8.5 pages", abstract = "In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash functions along with comparisons to standard, human-generated functions.", notes = "Compares with six existing hash functions. Large variation? See \cite{berarducci:2004:ugw:pber}", bibsource = "DBLP, http://dblp.uni-trier.de", } @InProceedings{berarducci:2004:ugw:pber, author = "Patrick Berarducci and Demetrius Jordan and David Martin and Jennifer Seitzer", title = "{GEVOSH}: Using Grammatical Evolution to Generate Hashing Functions", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WUGW001.pdf", size = "4 pages", abstract = "In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash functions along with comparisons to standard, human-generated functions.", notes = "see \cite{DBLP:conf/maics/BerarducciJMS04} GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{Beretta:2016:PDP, author = "S. Beretta and M. Castelli and Yuliana Martinez and Luis Munoz and Sara Silva and Leonardo Trujillo and Luciano Milanesi and Ivan Merelli", booktitle = "2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)", title = "A Machine Learning Approach for the Integration of {miRNA}-Target Predictions", year = "2016", pages = "528--534", abstract = "Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelisable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/PDP.2016.125", month = feb, notes = "Also known as \cite{7445385}", } @Article{Beretta:2018:complexity, author = "Stefano Beretta and Mauro Castelli and Luis Munoz and Leonardo Trujillo and Yuliana Martinez and Ales Popovic and Luciano Milanesi and Ivan Merelli", title = "A Scalable Genetic Programming Approach to Integrate {miRNA}-Target Predictions: Comparing Different Parallel Implementations of {M3GP}", journal = "Complexity", year = "2018", volume = "2018", pages = "Article ID 4963139", keywords = "genetic algorithms, genetic programming", URL = "http://downloads.hindawi.com/journals/complexity/2018/4963139.pdf", DOI = "doi:10.1155/2018/4963139", size = "13 pages", abstract = "There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelisable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.", } @InCollection{beretz:2002:EAMEABGP, author = "John P. Beretz", title = "Evolution of Algorithms for Multi-Species Emergent Assembly Behavior using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "21--30", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{Berge:2021:SBST, author = "Alexandre Bergel and Ignacio {Slater Munoz}", title = "Beacon: Automated Test Generation for Stack-Trace Reproduction using Genetic Algorithms", booktitle = "2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)", year = "2021", editor = "Jie M. Zhang and Erik Fredericks", pages = "1--7", address = "internet", month = "31 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, SBSE, Search-Based Software Testing, Automated Crash Reproduction, Dynamically Typed Languages, Python", isbn13 = "978-1-6654-4571-9/21/", URL = "https://drive.google.com/file/d/1fcL-M3GmBus2fnixe8zGNyS00crxV4a-/view", slide_url = "https://uchile-my.sharepoint.com/:p:/g/personal/ignacio_slater_uchile_cl/EUntuVTvl_1EiFVA3ubyrQwByC_FmNBs8r_8kyA-K97nKw?e=ZhU9UW", video_url = "https://drive.google.com/file/d/1FcavXIPaPcfZY4pu_y_DmgprHSZJfSzQ/view?usp=sharing", DOI = "doi:10.1109/SBST52555.2021.00007", size = "7 pages", abstract = "Software crashes are a problem all developers face eventually. Manually reproducing crashes can be very expensive and require a lot of effort. Recent studies have proposed techniques to automatically generate tests to detect and reproduce errors. But even if this topic has been widely studied, there has been little to no progress done for dynamically typed languages. This becomes important because current approaches take advantage of the type information inherent to statically typed languages to generate the sequence of instructions needed to reproduce a crash, thus making it unclear to judge if type information is necessary to reproduce errors. The lack of explicit type declarations in dynamic languages difficult the task of generating the instructions to replicate an error because the type checking can only be done during runtime, making algorithms less knowledgeable about the program and, therefore, making it more difficult to use search-based approaches because the algorithms have less information to work with.This paper presents a Genetic Algorithm approach to produce crash reproductions on Python based only on the information contained in the error stack-trace. An empirical study analysing three different experiments were evaluated giving mostly positive results, achieving a high precision while reproducing the desired crashes (over 70 percent). The study shows that the presented approach is independent of the kind of typing of the language, and provides a solid base to further develop the topic.", notes = "https://sbst21.github.io/program/", } @InCollection{Bergen:2010:GPTP, author = "Steven Bergen and Brian J. Ross", title = "Evolutionary Art Using Summed Multi-Objective Ranks", booktitle = "Genetic Programming Theory and Practice VIII", year = "2010", editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva", series = "Genetic and Evolutionary Computation", volume = "8", address = "Ann Arbor, USA", month = "20-22 " # may, publisher = "Springer", chapter = "14", pages = "227--244", keywords = "genetic algorithms, genetic programming, evolutionary art, multi-objective optimization", isbn13 = "978-1-4419-7746-5", URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5", DOI = "doi:10.1007/978-1-4419-7747-2_14", abstract = "This paper shows how a sum of ranks approach to multi-objective evaluation is effective for some low-order search problems, as it discourages the generation of outlier solutions. Outliers, which often arise with the traditional Pareto ranking strategy, tend to exhibit good scores on a minority of feature tests, while having mediocre or poor scores on the rest. They arise from the definition of Pareto dominance, in which an individual can be superlative in as little as a single objective in order to be considered undominated. The application considered in this research is evolutionary art, inwhich images are synthesized that adhere to an aesthetic model based on color gradient distribution. The genetic programming system uses 4 different fitness measurements, that perform aesthetic and color palette analyses. Outliers are usually undesirable in this application, because the color gradient distribution measurements requires 3 features to be satisfactory simultaneously. Sum of ranks scoring typically results in images that score better on the majority of features, and are therefore arguably more visually pleasing. Although the ranked sum strategy was originally inspired by highly dimensional problems having perhaps 20 objectives or more, this research shows that it is likewise practical for low-dimensional problems.", notes = "part of \cite{Riolo:2010:GPTP}", } @MastersThesis{Bergen:mastersthesis, author = "Steve Bergen", title = "Automatic Structure Generation using Genetic Programming and Fractal Geometry", school = "Brock University", year = "2011", keywords = "genetic algorithms, genetic programming", URL = "https://dr.library.brocku.ca/bitstream/handle/10464/3916/Brock_Bergen_Raphael_2011.pdf", URL = "http://hdl.handle.net/10464/3916", size = "127 pages", abstract = "Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages offered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and effectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the field of computational aesthetics and automated structure design.", } @InProceedings{Bergen:2012:EvoMUSART, author = "Steve Bergen and Brian Ross", title = "Aesthetic {3D} Model Evolution", booktitle = "Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012", year = "2012", month = "11-13 " # apr, editor = "Penousal Machado and Juan Romero and Adrian Carballal", series = "LNCS", volume = "7247", publisher = "Springer Verlag", address = "Malaga, Spain", pages = "11--22", organisation = "EvoStar", isbn13 = "978-3-642-29141-8", DOI = "doi:10.1007/978-3-642-29142-5_2", keywords = "genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, multi-objective evaluation", abstract = "Recently, evolutionary art has been exploring the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a clear preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary art.", notes = "Part of \cite{Machado:2012:EvoMusArt_proc} EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012", } @Article{Bergen:2013:GPEM, author = "Steve Bergen and Brian J. Ross", title = "Aesthetic {3D} model evolution", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "3", pages = "339--367", month = sep, note = "Special issue on biologically inspired music, sound, art and design", keywords = "genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, Multi-objective evaluation", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-013-9187-8", size = "29 pages", abstract = "A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design.", } @InProceedings{berger:1999:AHGAVRPTWIC, author = "Jean Berger and Mourad Sassi and Martin Salois", title = "A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "44--51", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{berger:2002:DMILFAGP, author = "Eric Berger", title = "Development of a Minimal Information Line Following Algorithm using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "31--35", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", URL = "http://www.genetic-programming.org/sp2002/Berger.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{bergstrom:2000:eiraatrGP, author = "Agneta Bergstrom and Patricija Jaksetic and Peter Nordin", title = "Enhancing Information Retrieval by Automatic Acquisition of Textual Relations using Genetic Programming", booktitle = "IUI 2000", year = "2000", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, machine learning, natural language processing, semantic networks, information retrieval", URL = "http://web.media.mit.edu/~lieber/IUI/Bergstrom/Bergstrom.pdf", size = "4 pages", abstract = "We have explored a novel method to find textual relations in electronic documents using genetic programming and semantic networks. This can be used for enhancing information retrieval and simplifying user interfaces. The automatic extraction of relations from text enables easier updating of electronic dictionaries and may reduce interface area both for search input and hit output on small screens such as cell phones and PDAs (Personal Digital Assistants).", notes = "www", } @InProceedings{bergstrom:2000:atrawGP, author = "Agneta Bergstrom and Patricija Jaksetic and Peter Nordin", title = "Acquiring Textual Relations Automatically on the Web using Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "237--246", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_17", abstract = "The flood of electronic information is pouring over us, while the technology maintaining the information and making it available to us has not yet been able to catch up. One of the paradigms within information retrieval focuses on the use of thesauruses to analyse contextual/structural information. We have explored a method that automatically finds textual relations in electronic documents using genetic programming and semantic networks. Such textual relations can be used to extend and update thesauruses as well as semantic networks. The program is written in PROLOG and communicates with software for natural language parsing. The system is also an example of computationally expensive fitness function using a large database. The results from the experiment show feasibility for this type of automatic relation extraction.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{beriro:2012:WRM, author = "Darren J. Beriro and Robert J. Abrahart and Nick J. Mount and C. Paul Nathanail", title = "Letter to the Editor on ``Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models'' by Ozgur Kisi \& Jalal Shiri [Water Resources Management 25 (2011) 3135-3152]", journal = "Water Resources Management", year = "2012", volume = "26", number = "12", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s11269-012-0049-6", DOI = "doi:10.1007/s11269-012-0049-6", notes = "See \cite{Kisi:2011:WRM} and \cite{kisi:2012:WRM}", } @Article{Beriro2012, author = "Darren J. Beriro and Robert J. Abrahart and C. Paul Nathanail", title = "'Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations' by Jalal Shir and Ozgur Kisi [Computers and Geosciences (2011) 1692-1701]", journal = "Computer \& Geosciences", volume = "56", pages = "216--220", year = "2013", note = "Letter to the Editor", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2012.04.014", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001379?v=s5", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001379", keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Groundwater depth fluctuation, Neuro-fuzzy, Data-driven modelling, Time series analyse", abstract = "This letter is not a request for the original authors to fill gaps or clarify discrepancies noted in their research although such additional material would of course be welcomed. Rather, it highlights that GEP and perhaps data-driven modelling more generally would benefit greatly from the development of a sound application protocol incorporating checks and balances, informed by consensus, for training, testing and reporting of complex procedures and solutions. The original authors' thoughts on our general as well as specific issues would be warmly welcomed.", notes = "Shir article is \cite{Shiri2010}", } @Article{Beriro:2013:CG, author = "Darren J. Beriro and Robert J. Abrahart and C. Paul Nathanail", title = "'Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations' by Jalal Shir \& Ozgur Kisi [Computers and Geosciences (2011) 1692-1701]", journal = "Computer \& Geosciences", volume = "56", pages = "216--220", year = "2013", keywords = "genetic algorithms, genetic programming", ISSN = "0098-3004", DOI = "doi:10.1016/j.cageo.2012.04.014", URL = "http://www.sciencedirect.com/science/article/pii/S0098300412001379", } @InProceedings{Berlanga:2005:GFS, title = "Learning fuzzy rules using Genetic Programming: Context-free grammar definition for high-dimensionality problems", author = "Francisco Jose Berlanga and Maria Jose {Del Jesus} and Francisco Herrera", booktitle = "International workshop on Genetic Fuzzy System, GFS 2005", year = "2005", editor = "Oscar Cordon and Francisco Herrera", keywords = "genetic algorithms, genetic programming", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.415.2932", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.415.2932", URL = "http://sci2s.ugr.es/keel/pdf/specific/congreso/gfs2005.pdf", broken = "http://sci2s.ugr.es/publications/ficheros/berlanga_deljesus_herrera_GFS05.pdf", size = "5 pages", abstract = "The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this work we tackle this problem, the FRBCS learning with high interpretability for high-dimensionality problems. We propose a genetic-programming-based method, where the evolved disjunctive normal form fuzzy rules compete in order to obtain an FRBCS with high interpretability (few rules and few antecedent conditions per rule) while maintaining a good performance.", } @InProceedings{Berlanga:2006:ICAISC, author = "F. J. Berlanga and M. J. {del Jesus} and M. J. Gacto and F. Herrera", title = "A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems", booktitle = "Proceedings 8th International Conference on Artificial Intelligence and Soft Computing {ICAISC}", year = "2006", pages = "182--191", series = "Lecture Notes on Artificial Intelligence (LNAI)", volume = "4029", publisher = "Springer-Verlag", editor = "Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada", address = "Zakopane, Poland", month = jun # " 25-29", bibdate = "2006-07-05", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2006.html#BerlangaJGH06", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-35748-3", DOI = "doi:10.1007/11785231_20", size = "10 pages", abstract = "In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.", } @InProceedings{Berlanga:2008:GEFS, author = "Francisco Jose Berlanga and Maria Jose {del Jesus} and Francisco Herrera", title = "A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems", booktitle = "3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008", year = "2008", month = "4-7 " # mar, address = "Witten-Boommerholz, Germany", pages = "101--106", keywords = "genetic algorithms, genetic programming, genetic cooperative-competitive fuzzy rule based learning method, high dimensional classification problems, high dimensional problems, token competition mechanism, fuzzy set theory, knowledge based systems, learning (artificial intelligence)", DOI = "doi:10.1109/GEFS.2008.4484575", abstract = "In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good performance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.", notes = "Also known as \cite{4484575}", } @Article{Berlanga20101183, author = "F. J. Berlanga and A. J. Rivera and M. J. {del Jesus} and F. Herrera", title = "GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems", journal = "Information Sciences", volume = "180", number = "8", pages = "1183--1200", year = "2010", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2009.12.020", URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34R0J-1/2/82039ab1549f5a0d0fc4d73b2a30bfa6", keywords = "genetic algorithms, genetic programming, Classification, Fuzzy rule-based systems, Genetic fuzzy systems, High-dimensional problems, Interpretability-accuracy trade-off", abstract = "In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.", } @InProceedings{bermejo:2024:CEC, author = "Enrique Bermejo and Oscar Cordon and Javier Irurita and Inmaculada Aleman and Angel Rubio Salvador", title = "Age-at-Death Estimation based on Symbolic Regression Ensemble Learning from Multiple Annotations", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Uncertainty, Accuracy, Annotations, Forensics, Decision making, Predictive models, Mathematical models, Age-at-death estimation, Ensemble learning, Symbolic regression", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10611921", abstract = "The present study addresses the problem of semiautomatic age-at-death estimation from pubic symphysis, a crucial yet complex task in forensic anthropology. Its accuracy directly depends on the quality of the pubic bone trait labeling developed by the forensic practitioners, affected by an inherent uncertainty in their definition. As interpretability is a mandatory requirement, we propose an approach where the model design is based on evolutionary learning, considering genetic programming to frame the problem as a symbolic regression task. Additionally, ensemble learning is considered to address the challenges posed by noise, uncertainty, and conflicting annotations inherent in data collected from multiple subjects. Ensemble learning provides an effective approach to navigate these challenges by facilitating consensus-building through decision making and information fusion. Hence, observer committees are formed, comprising multiple forensic specialists with different skills and expertise which provide alternative annotations. Several ensemble configurations combining different weak learners and aggregation operators are tested to assess their effectiveness in improving accuracy and reliability in age-at-death predictions. Their performance is compared against models trained on single annotations, revealing an improvement in predictive accuracy. The obtained results also highlight the benefits of incorporating diverse perspectives to address the complexities associated with human variability and anatomical assessments.", notes = "also known as \cite{10611921} WCCI 2024", } @InProceedings{Bernabe-Rodriguez:2020:PPSN, author = "Amin V. {Bernabe Rodriguez} and Carlos A. {Coello Coello}", title = "Generation of New Scalarizing Functions Using Genetic Programming", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12270", series = "LNCS", pages = "3--17", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Multi-objective optimization, Scalarizing functions", isbn13 = "978-3-030-58114-5", DOI = "doi:10.1007/978-3-030-58115-2_1", abstract = "In recent years, there has been a growing interest in multiobjective evolutionary algorithms (MOEAs) with a selection mechanism different from Pareto dominance. This interest has been mainly motivated by the poor performance of Pareto-based selection mechanisms when dealing with problems having more than three objectives (the so-called many-objective optimization problems). Two viable alternatives for solving many-objective optimization problems are decomposition-based and indicator-based MOEAs. However, it is well-known that the performance of decomposition-based MOEAs (and also of indicator-based MOEAs designed around R2) heavily relies on the scalarising function adopted. In this paper, we propose an approach for generating novel scalarizing functions using genetic programming. Using our proposed approach, we were able to generate two new scalarizing functions (called AGSF1 and AGSF2), which were validated using an indicator-based MOEA designed around R2 (MOMBI-II). This validation was conducted using a set of standard test problems and two performance indicators (hypervolume and s-energy). Our results indicate that AGSF1 has a similar performance to that obtained when using the well-known Achievement Scalarizing Function (ASF). However, AGSF2 provided a better performance than ASF in most of the test problems adopted. Nevertheless, our most remarkable finding is that genetic programming can indeed generate novel (and possible more competitive) scalarizing functions.", notes = "PPSN2020", } @InProceedings{bernabe-rodriguez:2023:GEWS2023, author = "Amin V. {Bernabe Rodriguez} and Carlos A. {Coello Coello}", title = "Designing Scalarizing Functions Using Grammatical Evolution", booktitle = "Grammatical Evolution Workshop - 25 years of GE", year = "2023", editor = "Conor Ryan and Mahsa Mahdinejad and Aidan Murphy", pages = "2004--2012", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, multi-objective optimization, scalarizing functions", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596354", size = "9 pages", abstract = "In this paper, we present a grammatical evolution-based framework to produce new scalarizing functions, which are known to have a significant impact on the performance of both decomposition-based multi-objective evolutionary algorithms (MOEAs) and indicator-based MOEAs which use R2. We perform two series of experiments using this framework. First, we produce many scalarizing functions using different benchmark problems to explore the behavior of the resulting functions according to the geometry of the problem adopted to generate them. Then, we perform a second round of experiments adopting two combinations of problems which yield better results in some test instances. We present the experimental validation of these new functions compared against the Achievement Scalarizing Function (ASF), which is known to provide a very good performance. For this comparative study, we adopt several benchmark problems and we are able to show that our proposal is able to generate new scalarizing functions that can outperform ASF in different problems.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @Article{BERNABERODRIGUEZ:2024:swevo, author = "Amin V. {Bernabe Rodriguez} and Braulio I. Alejo-Cerezo and Carlos A. {Coello Coello}", title = "Improving multi-objective evolutionary algorithms using Grammatical Evolution", journal = "Swarm and Evolutionary Computation", year = "2024", volume = "84", pages = "Article Number: 101434", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Evolutionary algorithms, Multi-objective optimization", ISSN = "2210-6502", URL = "http://delta.cs.cinvestav.mx/~ccoello/journals/amin-swevo-final.pdf.gz", URL = "https://www.sciencedirect.com/science/article/pii/S2210650223002067", DOI = "doi:10.1016/j.swevo.2023.101434", size = "54 pages", abstract = "Multi-objective evolutionary algorithms (MOEAs) have become an effective choice to solve multi-objective optimization problems (MOPs). However, it is well known that Pareto dominance-based MOEAs struggle in MOPs with four or more objective functions due to a lack of selection pressure in high dimensional spaces. The main choices for dealing with such problems are decomposition-based and indicator-based MOEAs. In this work, we propose the use of Grammatical Evolution (an evolutionary computation search technique) to generate functions that can improve decomposition-based and indicator-based MOEAs. Namely, we propose a methodology to generate new scalarizing functions, which are known to have a great impact in the performance of decomposition-based MOEAs and in some indicator-based MOEAs. Additionally, we propose another methodology to generate hypervolume approximations, since the hypervolume is a popular performance indicator used not only in indicator-based MOEAs but also to assess performance of MOEAs. Using our first methodology, we generate two new scalarizing functions and provide their corresponding experimental validation to show that they exhibit a competitive behavior when compared against some well-known scalarizing functions such as ASF, PBI and the Tchebycheff scalarizing function. Using our second methodology, we produce 4 different hypervolume approximations and compare their performance against the Monte Carlo method and against two other state-of-the-art hypervolume approximations. The experimental results show that our functions exhibit a good compromise in terms of quality and execution time", } @InProceedings{Bernal-Urbina:2008:ijcnn, author = "M. Bernal-Urbina and A. Flores-Mendez", title = "Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = "2008", editor = "Jun Wang", pages = "3325--3330", address = "Hong Kong", month = "1-6 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-1821-3", file = "NN0903.pdf", DOI = "doi:10.1109/IJCNN.2008.4634270", ISSN = "1098-7576", abstract = "The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm.", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{4634270}. WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.", } @InProceedings{Bernard:2020:ICTAI, author = "Jason Bernard and Ian McQuillan", title = "Inferring Temporal Parametric L-systems Using Cartesian Genetic Programming", booktitle = "2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)", year = "2020", pages = "580--588", abstract = "Lindenmayer Systems (L-systems) are formal grammars that use rewriting rules to replace, in parallel, every symbol in a string with a replacement string. By iterating, a sequence of strings is produced whose symbols can model temporal processes by interpreting them as simulation instructions. Among the types of L-systems, parametric L-systems are considered useful for simulating mechanisms that change based on different influences as the parameters change. Typically, L-systems are found by taking precise measurements and using existing knowledge, which can be addressed by automatic inference. This paper presents the Plant Model Inference Tool for Parametric L-systems (PMIT-PARAM) that can automatically learn parametric L-systems from a sequence of strings generated, where at least one parameter represents time. PMIT-PARAM is evaluated on a test suite of 20 known parametric L-systems, and is found to be able to infer the correct rewriting rules for the 18 L-systems containing only non-erasing productions; however, it can find appropriate parametric equations for all 20 of the L-systems. Inferring L-systems algorithmically not only can automatically learn models and simulations of a process with potentially less effort than doing so by hand, but it may also help reveal the scientific principles governing how the process' mechanisms change over time.", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/ICTAI50040.2020.00095", ISSN = "2375-0197", month = nov, notes = "Also known as \cite{9288168}", } @InProceedings{Bernard:2006:ECML, author = "Marc Bernard and Amaury Habrard and Marc Sebban", title = "Learning Stochastic Tree Edit Distance", booktitle = "Machine Learning: ECML 2006", year = "2006", series = "Lecture Notes in Computer Science", editor = "Johannes Furnkranz and Tobias Scheffer and Myra Spiliopoulou", publisher = "Springer", pages = "42--53", volume = "4212", DOI = "doi:10.1007/11871842_9", abstract = "Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity have been achieved during the last decade. However, this classic ED usually uses a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree ED. We use an adaptation of the Expectation-Maximization algorithm for learning the primitive edit costs. We carried out series of experiments that confirm the interest to learn a tree ED rather than a priori imposing edit costs.", notes = "not GP but cited by \cite{mcdermott:2011:EuroGP}", affiliation = "EURISE, Universite Jean Monnet de Saint-Etienne, 23, rue Paul Michelon, 42023 cedex 2 Saint-Etienne, France", } @InProceedings{Bernardi:2006:CEC, author = "P. Bernardi and E. Sanchez and M. Schillaci and G. Squillero and M. {Sonza Reorda}", title = "An Evolutionary Methodology to Enhance Processor Software-Based Diagnosis", booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation", year = "2006", editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas", pages = "3201--3206", address = "Vancouver", month = "16-21 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, microGP", ISBN = "0-7803-9487-9", DOI = "doi:10.1109/CEC.2006.1688401", size = "6 pages", abstract = "The widespread use of cheap processor cores requires the ability to quickly point out the manufacturing process criticalities in an effort to enhance the production yield. Fault diagnosis is an integral part of the industrial effort towards these goals. This paper describes an innovative application of evolutionary algorithms: iterative refinement of a diagnostic test set. Several enhancements in the used evolutionary core are additionally outlined, highlighting their relevance for the specific problem. Experimental results are reported in the paper showing the effectiveness of the approach for a widely-known microcontroller core.", notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = {"}859--864{"},", } @InProceedings{Bernardino:2009:ICARIS, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Grammar-Based Immune Programming for Symbolic Regression", booktitle = "Proceedings of the 8th International Conference on Artificial Immune Systems (ICARIS)", year = "2009", editor = "Paul S. Andrews and Jon Timmis and Nick D. L. Owens and Uwe Aickelin and Emma Hart and Andrew Hone and Andy M. Tyrrell", volume = "5666", series = "Lecture Notes in Computer Science", pages = "274--287", address = "York, UK", month = aug # " 9-12", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution, Artificial immune system, immune programming, symbolic regression", isbn13 = "978-3-642-03245-5", language = "English", URL = "http://dx.doi.org/10.1007/978-3-642-03246-2_26", DOI = "doi:10.1007/978-3-642-03246-2_26", abstract = "This paper presents a Grammar-based Immune Programming (GIP) that can evolve programs in an arbitrary language using a clonal selection algorithm. A context-free grammar that defines this language is used to decode candidate programs (antibodies) to a valid representation. The programs are represented by tree data structures as the majority of the program evolution algorithms do. The GIP is applied to symbolic regression problems and the results found show that it is competitive when compared with other algorithms from the literature.", } @InProceedings{Bernardino:2010:CILAMCE, author = "Heder Soares Bernardino and Helio Jose Correa Barbosa", title = "Comparing two ways of inferring a differential equation model via Grammar-based Immune Programming", booktitle = "Proceedings of the Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE)", year = "2010", editor = "Eduardo Dvorkin and Marcela Goldschmit and Mario Storti", pages = "9107--9124", address = "Buenos Aires", month = nov # " 15-18", organisation = "University of Buernos Aires", publisher = "Asociacion Argentina de Mecanica Computacional http://www.amcaonline.org.ar", keywords = "genetic algorithms, genetic programming, grammatical evolution, Artificial immune systems, grammar-based immune programming, symbolic regression, model inference", URL = "http://www.cimec.org.ar/ojs/index.php/mc/article/view/3656/3569", size = "18 pages", abstract = "An ordinary differential equation (ODE) is a mathematical form to describe physical or biological systems composed by time-derivatives of physical positions or chemical concentrations as a function of its current state. Given observed pairs, a relevant modelling problem is to find the symbolic expression of a differential equation which mathematically describes the concerned phenomenon. The Grammar-based Immune Programming (GIP) is a method for evolving programs in an arbitrary language by immunological inspiration. A program can be a computer program, a numerical function in symbolic form, or a candidate design, such as an analogue circuit. GIP can be used to solve symbolic regression problems in which the objective is to find an analytical expression of a function that better fits a given data set. At least two ways are available to solve model inference problems in the case of ordinary differential equations by means of symbolic regression techniques. The first one consists in taking numerical derivatives from the given data obtaining a set of approximations. Then a symbolic regression technique can be applied to these approximations. Another way is to numerically integrate the ODE corresponding to the candidate solution and compare the results with the observed data. Here, by means of numerical experiments, we compare the relative performance of these two ways to infer models using the GIP method.", notes = "paper ID-1364 https://sites.google.com/a/mecom2010.net/mecom-2010/home/Final.pdf?attredirects=0 mecom2010.net CILAMCE / ABMEC https://sites.google.com/a/mecom2010.net/mecom-2010/silamce-abmec", } @Article{Bernardino:2011:NC, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Grammar-based immune programming", journal = "Natural Computing", year = "2011", volume = "10", number = "1", pages = "209--241", month = mar, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Artificial immune system, AIS, Immune programming, Symbolic regression, Model inference", ISSN = "1567-7818", DOI = "doi:10.1007/s11047-010-9217-x", abstract = "This paper describes Grammar-based Immune Programming (GIP) for evolving programs in an arbitrary language by immunological inspiration. GIP is based on Grammatical Evolution (GE) in which a grammar is used to define a language and decode candidate solutions to a valid representation (program). However, by default, GE uses a Genetic Algorithm in the search process while GIP uses an artificial immune system. Some modifications are needed of an immune algorithm to use a grammar in order to efficiently decode antibodies into programs. Experiments are performed to analyse algorithm behaviour over different aspects and compare it with GEVA, a well known GE implementation. The methods are applied to identify a causal model (an ordinary differential equation) from an observed data set, to symbolically regress an iterated function f(f(x)) = g(x), and to find a symbolic representation of a discontinuous function", language = "English", } @InProceedings{Bernardino:2011:ICARIS, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Inferring Systems of Ordinary Differential Equations via Grammar-Based Immune Programming", booktitle = "Proceedings of the International Conference on Artificial Immune Systems (ICARIS)", year = "2011", editor = "Pietro Lio and Giuseppe Nicosia and Thomas Stibor", volume = "6825", series = "Lecture Notes in Computer Science", pages = "198--211", address = "Cambridge, UK", month = jul # " 18-21", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-22370-9", URL = "http://dx.doi.org/10.1007/978-3-642-22371-6_19", DOI = "doi:10.1007/978-3-642-22371-6_19", abstract = "Grammar-based Immune Programming (GIP) is a method for evolving programs in an arbitrary language using an immunological inspiration. GIP is applied here to solve the relevant modelling problem of finding a system of differential equations, in analytical form, which better explains a given set of data obtained from a certain phenomenon. Computational experiments are performed to evaluate the approach, showing that GIP is an efficient technique for symbolic modelling.", language = "English", } @InProceedings{Bernardino:2011:CILAMCE, author = "Heder S. Bernardino and Eduardo S. Castro and Joao N. C. Guerreiro and Helio J. C. Barbosa", title = "Inferring strains on a locally deformed pipe via grammar-based immune programming", booktitle = "Proceedings of the Iberian Latin American Congress on Computational Methods in Engineering (CILAMCE)", year = "2011", editor = "Andrea R. D. Silva", address = "Ouro Preto, MG, Brazil", month = nov # " 13-16", keywords = "genetic algorithms, genetic programming", notes = "CILAMCE XXXII CILAMCE 2011, the 32th CILAMCE Conference, is being organized by the Graduate Programs in Civil (PROPEC) and Mineral Engineering (PPGEM), of the Federal University of Ouro Preto - Brazil (UFOP) http://www.acquacon.com.br/cilamce2011/en/index.php PAP007449 http://www.acquacon.com.br/cilamce2011/en/authorsinfo.php", } @InProceedings{Bernardino:2012:WCCM, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Simultaneous topology, shape, and sizing optimization of Truss structures via grammatical evolution", booktitle = "Proceedings of the 10th World Congress on Computational Mechanics (WCCM)", year = "2012", editor = "Paulo M. Pimenta", address = "Sao Paulo, Brazil", month = "8-13 " # jul, organisation = "IACM", keywords = "genetic algorithms, genetic programming", notes = "http://docslide.us/documents/wccm2012.html http://www.iacm.info/vpage/1/0/Events/WCCM", } @InCollection{bernardinobarbosa2014, author = "Heder S. Bernardino and Helio J. C. Barbosa", title = "Infer\^{e}ncia de Modelos Utilizando a Programa\c{c}\~{a}o Imunol\'{o}gica Gramatical", booktitle = "T\'{e}cnicas de Intelig\^{e}ncia Computacional com Aplica\c{c}\~{o}es em Problemas Inversos de Engenharia", publisher = "Omnipax", year = "2014", editor = "Fran S\'{e}rgio Lobato and Valder {Steffen Jr.} and Antonio Jos\'{e} da {Silva Neto}", chapter = "4", pages = "37--50", address = "Curitiba, PR", edition = "1", keywords = "genetic algorithms, genetic programming, grammatical evolution, Inverse problems, Model identification, Grammar-based immune programming", isbn13 = "978-85-64619-15-9", URL = "http://omnipax.com.br/site/?page_id=549", DOI = "doi:10.7436/2014.tica.04", size = "14 pages", abstract = "In this chapter we present the grammar-based immune programming, a technique for evolving programs by combining a search mechanism, inspired by the clonal selection theory, with the grammatical evolution representation which makes a clear distinction between the search and the solution spaces, thus offering more flexibility. The technique is applied to the inverse problem of model identification - in symbolic form - from data. Examples of the inference of systems of ordinary differential equations are presented.", abstract = "Apresenta-se neste capitulo a programacao imunologica gramatical, uma tecnica para evolucao de programas que combina um mecanismo de busca inspirado pela teoria da selecao clonal com a representacao via evolucao gramatical, que faz uma distincao clara entre o espaco de busca e o espaco de solucoes, oferecendo portanto mais flexibilidade. A tecnica e aplicada ao problema inverso de identificacao de modelos - em forma simbolica - a partir de dados. Exemplos de inferencia de sistemas de equacoes diferenciais ordinarias sao apresentados.", notes = "In Portuguese", } @InProceedings{Bernardino:2015:CEC, author = "Heder Bernardino and Helio Barbosa", title = "Grammar-based Immune Programming to Assist in the Solution of Functional Equations", booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)", year = "2015", editor = "Yadahiko Murata", pages = "1167--1174", address = "Sendai, Japan", month = "25-28 " # may, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-7491-7", DOI = "doi:10.1109/CEC.2015.7257021", abstract = "Grammar-based immune programming is proposed here as a tool to assist the search for a general solution to a functional equation. A external archive is incorporated to the algorithm in order to store good solutions found during the search. By inspecting such particular solutions the user is able to generalize and construct a general solution to the functional equation considered. The main objective here is to provide the user with a large diverse set of particular solutions to the problem at hand. Preliminary computational experiments are performed where some functional equations from the literature are tackled.", notes = "1335 hrs 15529 CEC2015", } @InProceedings{Bernardo:2012:UKCI, author = "Dario Bernardo and Hani Hagras and Edward Tsang", title = "An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets", booktitle = "12th UK Workshop on Computational Intelligence (UKCI 2012)", year = "2012", DOI = "doi:10.1109/UKCI.2012.6335765", abstract = "Today stock market exchange and finance are centres of attention all over the world. In finance, arbitrage is the practice of taking advantage of a price misalignment between two or more stock markets where profit can be earned by striking a combination of matching deals that capitalise upon the misalignment. If one strikes when misalignment has been observed, such deals are practically risk-free. However, when risk-free profit is around, everyone would compete to take advantage of it. Therefore, the question is whether arbitrage opportunities can be predicted; after all, misalignment does not happen instantaneously. Furthermore, financial operators do not like black boxes in forecasting. In this paper, we will present a type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarised models from pre-specified number of linguistic rules, which enables the user to understand the generated models for arbitrage opportunities prediction. The system is able to use this summarised model for the prediction of arbitrage opportunities in stock markets. We have performed several experiments based on the arbitrage data which is used in stock markets to spot ahead of time arbitrage opportunities. The proposed type-2 FLS has outperformed the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees). Like GP, the type-2 FLS is capable of providing a white box model which could be easily understood and analysed by the lay user.", keywords = "genetic algorithms, genetic programming, fuzzy logic, pricing, profitability, risk management, stock markets, financial application modelling, financial application prediction, financial operators, interval type-2 fuzzy logic based system, linguistic rules, model generation, model summarisation, price misalignment, risk-free profit, stock market exchange, type-2 FLS, Finance, Firing, Fuzzy logic, Fuzzy sets, Predictive models, Stock markets, Uncertainty, Financial Applications, Type-2 Fuzzy logic Systems, arbitrage, prediction", notes = "'We have compared the proposed approach with one of the most powerful white box modeling and prediction systems for spotting arbitrage opportunities which is EDR procedure [31]. The EDR method evolves a set of decision rules by using Genetic Programming (GP)' 'type-2 FLS' ' much better' Also known as \cite{6335765}", } @InProceedings{Bernardo:2013:ieeeFUZZ, author = "Dario Bernardo and Hani Hagras and Edward Tsang", title = "A Genetic Type-2 fuzzy logic based system for financial applications modelling and prediction", booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ 2013)", year = "2013", month = jul, keywords = "genetic algorithms, genetic programming, type-2 fuzzy logic", DOI = "doi:10.1109/FUZZ-IEEE.2013.6622310", ISSN = "1098-7584", abstract = "Following the global economic crisis, many financial organisations around the World are seeking efficient frameworks for predicting and assessing financial risks. However, in the current economic situation, transparency became an important factor where there is a need to fully understand and analyse a given financial model. In this paper, we will present a Genetic Type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarised optimised type-2 FLSs based financial models which are easy to read and analyse by the lay user. The system is able to use the summarised model for prediction within financial applications. We have performed several evaluations in two distinctive financial domains one for the prediction of good/bad customers in a credit card approval application and the other domain was in the prediction of arbitrage opportunities in the stock markets. The proposed Genetic type-2 FLS has outperformed white box financial models like the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees) and gave a comparable performance to black box models like neural networks while the proposed system provided a white box model which is easy to understand and analyse by the lay user.", notes = "Also known as \cite{6622310}", } @InProceedings{Berndt:2021:SBCCI, author = "Augusto Berndt and Isac S. Campos and Bryan Lima and Mateus Grellert and Jonata T. Carvalho and Cristina Meinhardt and Brunno A. {De Abreu}", title = "Accuracy and Size Trade-off of a Cartesian Genetic Programming Flow for Logic Optimization", booktitle = "2021 34th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI)", year = "2021", month = aug, keywords = "genetic algorithms, genetic programming, cartesian genetic programming", DOI = "doi:10.1109/SBCCI53441.2021.9529968", abstract = "Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Traditional approaches for logic synthesis have been in the spotlight so far. However, due to advances in machine learning and their high performance in solving specific problems, such algorithms appear as an attractive option to improve electronic design tools. In our work, we explore Cartesian Genetic Programming for logic optimization of exact or approximate combinational circuits. The proposed CGP flow receives input from the circuit description in the format of AND-Inverter Graphs and its expected behavior as a truth-table. The CGP may improve solutions found by other techniques used for bootstrapping the evolutionary process or initialize the search from random (unbiased) individuals seeking optimal circuits. We propose two different evaluation methods for the CGP: to minimize the number of AIG nodes or optimize the circuit accuracy. We obtain at least 22.percent superior results when considering the ratio between accuracy and size for the benchmarks used, compared with the teams from the IWLS 2020 contest that obtained the best accuracy and size results. It is noteworthy that any logic synthesis approach based on AIGs can easily incorporate the proposed flow. The results obtained show that their usage may achieve improved logic circuits.", notes = "Also known as \cite{9529968} See \cite{Berndt:2022:JICS}", } @Article{Berndt:2022:JICS, author = "Augusto Andre {Souza Berndt} and Brunno Abreu and Isac S. Campos and Bryan Lima and Mateus Grellert and Jonata T. Carvalho and Cristina Meinhardt", title = "A {CGP}-based Logic Flow: Optimizing Accuracy and Size of Approximate Circuits", journal = "Journal of Integrated Circuits and Systems", year = "2022", volume = "17", number = "1", note = "Selected Papers from Symposium on Integrated Circuits and Systems Design, 2021", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Logic Minimization, Machine Learning, Multi-variable Optimization, Evolutionary Computing", URL = "https://jics.org.br/ojs/index.php/JICS/article/view/546/380", DOI = "doi:10.29292/jics.v17i1.546", size = "12 pages", abstract = "Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Machine learning techniques show high performance in solving specific problems, being an attractive option to improve electronic design tools. We explore Cartesian Genetic Programming (CGP) for logic optimization of exact or approximate Boolean functions in our work. The proposed CGP-based flow receives the expected circuit behavior as a truth-table and either performs the synthesis starting from random circuits or optimizes a circuit description provided in the format of an AND-Inverter Graph. The optimization flow improves solutions found by other techniques, using them for bootstrapping the evolutionary process. We use two metrics to evaluate our CGP-based flow: (i) the number of AIG nodes or (ii) the circuit accuracy. The results obtained showed that the CGP-based flow provided at least 22.6percent superior results when considering the trade-off between accuracy and size compared with two other methods that brought the best accuracy and size outcomes, respectively.", notes = "previous \cite{Berndt:2021:SBCCI} Federal University of Santa Catarina (UFSC), Brazil", } @Article{Bernhardt:2008:EC, author = "Knut Bernhardt", title = "Finding Alternatives and Reduced Formulations for Process-Based Models", journal = "Evolutionary Computation", year = "2008", volume = "16", number = "1", pages = "63--88", month = "Spring", keywords = "genetic algorithms, genetic programming, Model reduction, complexity, dimension reduction", ISSN = "1063-6560", DOI = "doi:10.1162/evco.2008.16.1.63", size = "26 pages", abstract = "This paper addresses the problem of model complexity commonly arising in constructing and using process-based models with intricate interactions. Apart from complex process details the dynamic behaviour of such systems is often limited to a discrete number of typical states. Thus, models reproducing the system's processes in all details are often too complex and over-parameterised. In order to reduce simulation times and to get a better impression of the important mechanisms, simplified formulations are desirable. In this work a data adaptive model reduction scheme that automatically builds simple models from complex ones is proposed. The method can be applied to the transformation and reduction of systems of ordinary differential equations. It consists of a multistep approach using a low dimensional projection of the model data followed by a Genetic Programming/Genetic Algorithm hybrid to evolve new model systems. As the resulting models again consist of differential equations, their process-based interpretation in terms of new state variables becomes possible. Transformations of two simple models with oscillatory dynamics, simulating a mathematical pendulum and predator-prey interactions respectively, serve as introductory examples of the method's application. The resulting equations of force indicate the predator-prey system's equivalence to a nonlinear oscillator. In contrast to the simple pendulum it contains driving and damping forces that produce a stable limit cycle.", notes = "CVODE, SUNDIALS", } @InProceedings{bersano-begey:1996:pici, author = "Tommaso F. Bersano-Begey and Jason M. Daida and John F. Vesecky and Frank L. Ludwig", title = "A Platform-Independent Collaborative Interface for Genetic Programming Applications: Image Analysis for Scientific Inquiry", booktitle = "Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996", year = "1996", editor = "John R. Koza", pages = "1--8", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California 94305-3079, USA", month = "28--31 " # jul, publisher = "Stanford Bookstore", ISBN = "0-18-201031-7", keywords = "genetic algorithms, genetic programming", notes = "GP-96LB java The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{bersano-begey:1997:jcifGPa, author = "Tommaso F. Bersano-Begey and Jason M. Daida and John F. Vesecky and Frank L. Ludwig", title = "A {Java} Collaborative Interface for Genetic Programming Applications: Image Analysis and Scientific Inquiry", booktitle = "Proceedings of the 1997 {IEEE} International Conference on Evolutionary Computation", year = "1997", address = "Indianapolis", publisher_address = "Piscataway, NJ, USA", month = "13-16 " # apr, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/ICEC97image.pdf", notes = "ICEC-97 Collaborative Interface Demonstration http://www.sprl.umich.edu/acers/gaia/collab.html GAIA (Genetic programming Assistant for Image Analysis) slides http://www-personal.umich.edu/~tombb/gaia74/", } @InProceedings{Bersano-Begey:1997:cedslo, author = "Tommaso F. Bersano-Begey", title = "Controlling Exploration, Diversity and Escaping Local Optima in GP: Adapting Weights of Training Sets to Model Resource Consumption", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "7--10", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670", } @InProceedings{Bersano-Begey:1997:grffc, author = "Tommaso F. Bersano-Begey and Jason M. Daida", title = "A Discussion on Generality and Robustness and a Framework for Fitness Set Construction in Genetic Programming to Promote Robustness", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "11--18", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 Fri, 05 Sep 1997 06:14:54 EDT I did some follow-up work in trying to improve generality of code in the wall-following problem, and started to look at how to gain more information about generality by recording the distribution of hits (rather than just their total), an iterative algorithm to check for and correct ambiguous training sets (one which can be solved by other solutions besides the correct one), and an account of the relationship between size and generality of solutions. The following was a very preliminary work, but I am now working on expanding each topic and writing them in a more formal way. slides http://www-personal.umich.edu/~tombb/gp973/", } @InProceedings{bersano-begey:1997:, author = "Tommaso F. Bersano-Begey and Patrick G. Kenny and Edmund H. Durfee", title = "Multi-Agent Teamwork, Adaptive Learning and Adversarial Planning in Robocup Using a PRS Architecture", booktitle = "IJCAI97", year = "1997", note = "accepted", keywords = "genetic algorithms, genetic programming", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.1962", URL = "http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=BC06E9197308E7FDF6E8347CECE81DC1?doi=10.1.1.53.1962&rep=rep1&type=pdf", size = "7 pages", abstract = "Our approach for the Robocup97 competition is to emphasise teamwork among agents by augmenting reactions (based on awareness of the current situation) with predictions (based on predefined multiagent manoeuvres). These predictions are accomplished by allowing agents to cooperatively accomplish predefined plans, which are elaborated reactively and hierarchically to ensure responsiveness to changing circumstances. By supporting the runtime construction of plans, our approach simplifies the introduction of new plans, strategies, and actions, and produces a framework for dynamic adaptation and plan recognition through automatically generating belief networks. Our implementation is built on top of UM-PRS, a procedural reasoning system architecture for real-time environments, which allows specifying, executing, and integrating plans based on subgoaling and preconditions", notes = "um-prs.pdf broken 5-sep-97 http://www.sonycsl.co.jp/person/kitano/RoboCup/ws97.html", } @InProceedings{Bersini:2000:GECCO, author = "Hugues Bersini", title = "Chemical Crossover", pages = "825--832", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/AA140.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/AA140.ps", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{berstein:2004:mpefsgp, title = "Multiobjective Parsimony Enforcement for Superior Generalisation Performance", author = "Yaniv Bernstein and Xiaodong Li and Vic Ciesielski and Andy Song", pages = "83--89", booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation", year = "2004", publisher = "IEEE Press", month = "20-23 " # jun, address = "Portland, Oregon", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Multiobjective evolutionary algorithms, Combinatorial \& numerical optimization", URL = "http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf", DOI = "doi:10.1109/CEC.2004.1330841", size = "7 pages", abstract = "Program Bloat - the phenomenon of ever-increasing program size during a GP run - is a recognised and widespread problem. Traditional techniques to combat program bloat are program size limitations or parsimony pressure (penalty functions). These techniques suffer from a number of problems, in particular their reliance on parameters whose optimal values it is difficult to a priori determine. In this paper we introduce POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an alternative, parameter-free technique for eliminating program bloat. We test it on a classification problem and find that while vastly reducing program size, it does improve generalisation performance.", notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.", } @InProceedings{Berthier:2010:LION, title = "Consistency Modifications for Automatically Tuned {Monte-Carlo} Tree Search", author = "Vincent Berthier and Hassen Doghmen and Olivier Teytaud", booktitle = "Learning and Intelligent OptimizatioN, LION 4", year = "2010", editor = "Roberto Battiti", address = "Venice", month = jan # " 18-22", keywords = "genetic algorithms, genetic programming, Game Go, Mathematics/Optimization and Control, Monte-Carlo Tree Search Consistency Ko-fights", URL = "http://hal.archives-ouvertes.fr/docs/00/43/71/46/PDF/consistency.pdf", URL = "HAL:http://hal.archives-ouvertes.fr/inria-00437146/en/", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", identifier = "HAL:inria-00437146, version 1", language = "EN", oai = "oai:hal.archives-ouvertes.fr:inria-00437146_v1", abstract = "Monte-Carlo Tree Search algorithms (MCTS [4, 6]), including upper confidence trees (UCT [9]), are known for their impressive ability in high dimensional control problems. Whilst the main test bed is the game of Go, there are increasingly many applications [13, 12, 7]; these algorithms are now widely accepted as strong candidates for high-dimensional control applications. Unfortunately, it is known that for optimal performance on a given problem, MCTS requires some tuning; this tuning is often handcrafted or automated, with in some cases a loss of consistency, i.e. a bad behavior asymptotically in the computational power. This highly undesirable property led to a stupid behavior of our main MCTS program MoGo in a real-world situation described in section 3. This is a big trouble for our several works on automatic parameter tuning [3] and the genetic programming of new features in MoGo. We will see in this paper: -- A theoretical analysis of MCTS consistency; -- Detailed examples of consistent and inconsistent known algorithms; -- How to modify a MCTS implementation in order to ensure consistency, independently of the modifications to the scoring module (the module which is automatically tuned and genetically programmed in MoGo); -- As a by product of this work, we'll see the interesting property that some heavily tuned MCTS implementations are better than UCT in the sense that they do not visit the complete tree (whereas UCT asymptotically does), whilst preserving the consistency at least if consistency modifications above have been made.", notes = "LION4 http://lion.disi.unitn.it/intelligent-optimization//LION4/program.php", } @Article{Bertolini:2018:SP, author = "Vittorio Bertolini and Carlos Rey Barra and Mauricio Sepulveda and Victor Parada", title = "Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem", journal = "Scientific Programming", year = "2018", volume = "2018", pages = "6971827:1--6971827:13", keywords = "genetic algorithms, genetic programming", publisher = "Hindawi", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/sp/sp2018.html#BertoliniRSP18", URL = "http://downloads.hindawi.com/journals/sp/2018/6971827.pdf", DOI = "doi:10.1155/2018/6971827", size = "14 pages", abstract = "New constructive algorithms for the two-dimensional guillotine-cutting problem are presented. The algorithms were produced from elemental algorithmic components using evolutionary computation. A subset of the components was selected from a previously existing constructive algorithm. The algorithms' evolution and testing process used a set of 46 instances from the literature. The structure of three new algorithms is described, and the results are compared with those of an existing constructive algorithm for the problem. Several of the new algorithms are competitive with respect to a state-of-the-art constructive algorithm. A subset of novel instructions, which are responsible for the majority of the new algorithms' good performances, has also been found.", notes = "Informatics Engineering Department, University of Santiago of Chile, Santiago, Chile Also known as \cite{journals/sp/BertoliniRSP18}", } @InProceedings{Bertram:1997:ris, author = "Robert R. Bertram and Jason M. Daida and John F. Vesecky and Guy A. Meadows and Christian Wolf", title = "Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms", booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference", year = "1997", editor = "John R. Koza", pages = "19--27", address = "Stanford University, CA, USA", publisher_address = "Stanford University, Stanford, California, 94305-3079, USA", month = "13--16 " # jul, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-206995-8", notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see also \cite{bertram:1998:risiGA}", } @InProceedings{bertram:1998:risiGA, author = "Robert R. Bertram and Jason M. Daida and John F. Vesecky and Guy A. Meadows and Christian Wolf", title = "Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "447--454", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms", URL = "http://citeseer.ist.psu.edu/244792.html", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/10392/ftp:zSzzSzftp.eecs.umich.eduzSzpeoplezSzdaidazSzpaperszSzsga98reconstruct.pdf/reconstructing-incomplete-signals-using.pdf", size = "8 pages", abstract = "This paper describes a general, nonanalytical method for deriving Fourier series coefficients using a genetic algorithm. Non-analytical methods are often needed in problems where lost portions of a complex signal require restoration. We discuss some of the difficulties involved in working with the associated trigonometric polynomials and propose an alternative solution for adapting genetic algorithms for this class of problems. We demonstrate the efficacy of our approach with a case study. Our particular case study features the processing of data that has been collected by a novel optical waveslope instrument, which measures the topography of water surfaces.", notes = "SGA-98 see also \cite{Bertram:1997:ris}", } @InProceedings{bertschinger:2024:GECCO, author = "Amanda Bertschinger and James Bagrow and Joshua Bongard", title = "Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Jean-Baptiste Mouret and Kai Qin and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "277--285", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, symbolic regression, neuroevolution, multi-objective optimization, Evolutionary Machine Learning", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654030", size = "9 pages", abstract = "Data increasingly abounds, but distilling their underlying relationships down to something interpretable remains challenging. One approach is genetic programming, which 'symbolically regresses' a data set down into an equation. However, symbolic regression (SR) faces the issue of requiring training from scratch for each new dataset. To generalize across all datasets, deep learning techniques have been applied to SR. These networks, however, are only able to be trained using a symbolic objective: NN-generated and target equations are symbolically compared. But this does not consider the predictive power of these equations, which could be measured by a behavioral objective that compares the generated equation's predictions to actual data. Here we introduce a method that combines gradient descent and evolutionary computation to yield neural networks that minimize the symbolic and behavioral errors of the equations they generate from data. As a result, these evolved networks are shown to generate more symbolically and behaviorally accurate equations than those generated by networks trained by state-of-the-art gradient based neural symbolic regression methods. We hope this method suggests that evolutionary algorithms, combined with gradient descent, can improve SR results by yielding equations with more accurate form and function.", notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Berutich:2016:ESA, author = "Jose Manuel Berutich and Francisco Lopez and Francisco Luna and David Quintana", title = "Robust technical trading strategies using {GP} for algorithmic portfolio selection", journal = "Expert Systems with Applications", year = "2016", volume = "46", pages = "307--315", keywords = "genetic algorithms, genetic programming, Algorithmic trading, Portfolio management, Trading rule, Finance, buy and hold", ISSN = "0957-4174", URL = "https://e-archivo.uc3m.es/rest/api/core/bitstreams/169bcfb0-d6cc-4f0a-870b-dd9915924000/content", URL = "http://www.sciencedirect.com/science/article/pii/S0957417415007447", DOI = "doi:10.1016/j.eswa.2015.10.040", size = "12 pages", abstract = "This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy and Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81percent for the testing period 2009-2013 in the Spanish market, having the IBEX35 index returned 2.67percent.", } @PhdThesis{Berutich:thesis, author = "Jose Manuel {Berutich Lindquist}", title = "Robust Optimization of Algorithmic Trading Systems", school = "Lenguajes y Ciencias de la Computacion, Universidad de Malaga", year = "2017", address = "Malaga, Andalucia, Spain", month = "22 " # may, keywords = "genetic algorithms, genetic programming", URL = "https://hdl.handle.net/10630/15353", URL = "https://riuma.uma.es/xmlui/bitstream/handle/10630/15353/TD_BERUTICH_%20LINDQUIST_Jose_Manuel.pdf", size = "157 pages", abstract = "GAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions.", notes = "In English AUTOR: Jose Manuel Berutich Lindquist http://orcid.org/0000-0002-0918-9634 Supervisors: Francisco Luna Valero and Francico Lopez Valverde https://core.ac.uk/download/pdf/214843381.pdf", } @InProceedings{1068303, author = "Sireesha Besetti and Terence Soule", title = "Function choice, resiliency and growth in genetic programming", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1771--1772", address = "Washington DC, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1771.pdf", DOI = "doi:10.1145/1068009.1068303", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, function choice, growth, resilience", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @InProceedings{Beshah:2012:INCoS, author = "Tibebe Beshah and Dejene Ejigu and Pavel Kromer and Vaclav Snasel and Jan Platos and Ajith Abraham", booktitle = "4th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2012", title = "Learning the Classification of Traffic Accident Types", year = "2012", pages = "463--468", DOI = "doi:10.1109/iNCoS.2012.75", abstract = "This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labelling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management authorities that can exploit the knowledge to improve road safety and mitigate the severity of traffic accidents.", keywords = "genetic algorithms, genetic programming, data analysis, fuzzy set theory, learning (artificial intelligence), pattern classification, road accidents, road traffic, traffic engineering computing, evolutionary fuzzy classifier design, feature selection, machine learning, real world road accident data set, road accident data analysis, road safety improvement, symbolic classifier, traffic accident severity mitigation, traffic accident type classification, traffic management authorities, Accidents, Biological cells, Indexes, Injuries, Labeling, Vehicles, fuzzy rules, machine learning, traffic accidents", notes = "Also known as \cite{6337959}", } @InProceedings{best:1999:CMGSE, author = "Michael L. Best", title = "Coevolving Mutualists Guide Simulated Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "941", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming, poster papers", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{bettenhausen:1995:biox, author = "K. D. Bettenhausen and S. Gehlen and P. Marenbach and H. Tolle", title = "BioX++ -- {N}ew results and conceptions concerning the intelligent control of biotechnological processes", booktitle = "6th International Conference on Computer Applications in Biotechnology", year = "1995", editor = "A. Munack and K. Sch{\"u}gerl", pages = "324--327", organisation = "IFAC Publications", publisher = "Elsevier Science", email = "mali@rt.e-technik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming, Expert systems, neural networks, fuzzy systems, learning control, fermentation, biotechnology", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_03.pdf", size = "4 pages", abstract = "BioX++ facilities the transparent generation of process control stratgies and sequences based on automatically self-organized structured process models. Experimental results showing the increased product yeild and the discussion of approach-specific problems are part of this paper as well as the new approaches actually examined.", notes = "14--17 May, Garmisch-Partenkirchen, Germany", } @InProceedings{bettenhausen:1995:sombbff, author = "Kurt Dirk Bettenhausen and Peter Marenbach", title = "Self-organizing modeling of biotechnological batch and fed-batch fermentations", booktitle = "EUROSIM'95", year = "1995", editor = "F. Breitenecker and I. Husinsky", address = "Vienna, Austria", publisher = "Elsevier", email = "kurt.dirk.bettenhausen@rt.e-technik.tu-darmstadt.de (Kurt Dirk Bettenhausen), mali@rt.e-technik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming, fermentation, biotechnology", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_23.ps.gz", size = "5 pages", abstract = "An approach for the automatic generation of dynamic nonlinear process models obtained from experimantal process data and theoretical biological and chemical reflections using genetic programming for the supervision and coordination of the symbolic model structure during automatic development BioX++ includes (amongs fuzzy rule learning, expert system, NN also refered to) GP to produce process models, constants adapted using standard algorithmic techniques.", notes = "11--15 September, Vienna, Austria", } @InProceedings{bettenhausen:1995:sombbffGP, author = "K. D. Bettenhausen and P. Marenbach and Stephan Freyer and Hans Rettenmaier and Ulrich Nieken", title = "Self-organizing Structured modeling of a Biotechnological Fed-batch fermentation by Means of Genetic Programming", booktitle = "First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA", year = "1995", editor = "A. M. S. Zalzala", volume = "414", pages = "481--486", address = "Sheffield, UK", publisher_address = "London, UK", month = "12-14 " # sep, publisher = "IEE", email = "mali@rt.e-technik.tu-darmstadt.de", keywords = "genetic algorithms, genetic programming, symbolic modelling, system identification, biotechnology, predictive control", ISBN = "0-85296-650-4", URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_24.pdf", DOI = "doi:10.1049/cp:19951095", size = "6 pages", abstract = "12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm The article describes an approach for the self-organizing generation of models of complex and unknown processes by means of GP and its application on a biotechnological fed-batch production. First experiments of the symbolic generation of structured models within an industrial cooperation with BASF are presented.", notes = "Deals much more than bettenhausen:1995:ssmbff and \cite{bettenhausen:1995:biox} with the idea of Genetic Programming. First results from an application of our approach for finding model of an industrial fed-batch fermentation process are presented which. This work was part of an cooperation of our Institute and the BASF AG, Ludwigshafen, Germany. This paper includes a more detailed description of how our GP system works. ", } @Article{beura:2018:AJSE, author = "Sambit Kumar Beura and Prasanta Kumar Bhuyan", title = "Operational Analysis of Signalized Street Segments Using Multi-gene Genetic Programming and Functional Network Techniques", journal = "Arabian Journal for Science and Engineering", year = "2018", volume = "43", number = "10", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/article/10.1007/s13369-018-3176-4", DOI = "doi:10.1007/s13369-018-3176-4", } @Article{BEURA:2020:JTH, author = "Sambit Kumar Beura and Kondamudi Vinod Kumar and Shakti Suman and Prasanta Kumar Bhuyan", title = "Service quality analysis of signalized intersections from the perspective of bicycling", journal = "Journal of Transport \& Health", volume = "16", pages = "100827", year = "2020", ISSN = "2214-1405", DOI = "doi:10.1016/j.jth.2020.100827", URL = "http://www.sciencedirect.com/science/article/pii/S2214140519300866", keywords = "genetic algorithms, genetic programming, Developing country, Signalized intersection, Bicycle level of service, Prediction modelling, Functional network", abstract = "Bicycling reduces the risk of various health problems associated with sedentary lifestyles. Hence, it is important to encourage bicycle commuting by enhancing the bikeability of transportation facilities. To support this process, this article proposes efficient bicycle level of service (BLOS) models for the assessment of signalized intersections under heterogeneous traffic conditions. Here, BLOS denotes bicyclists' perceived level of satisfaction. Method Extensive data sets (geometrical, traffic operational and built-environmental) are collected from 70 well-diversified intersection approaches of India. All approaches are also rated by 200 on-site bicyclists based on their perceived satisfaction levels on a Likert scale of 1-6 (excellent-worst). The attributes having significant influences on these ratings are then identified through Spearman's correlation analysis. Subsequently, three highly efficient techniques namely, associativity functional network (FN), genetic programming (GP) and step-wise regression are used to develop reliable BLOS models. Results As observed, the intersection BLOS is significantly (p < 0.001) influenced by total eight attributes of which crossing pedestrian volume, parking turn-over and average bicycle delay are the most dominating ones. Using these variables, the FN tool has produced the most efficient BLOS model with a coefficient of determination (R2) value of 0.92 with averaged observations. Further, the classification of BLOS ratings into six symmetrical levels A-F (excellent-worst) has reported that around 8percent intersection approaches in India are offering BLOS C-F. Conclusion The important measures of BLOS improvement at signalized intersections include the efficient management of crossing pedestrians, restrictions on nearby parking activities, and minimization of bicycle delay. The deficiencies in these aspects have perhaps made the intersection approaches in India to offer BLOS C-F. The BLOS models and transportation engineering solutions proposed in this study for the improvement of public health through bicycling are highly efficient for developing countries.", } @Article{BEURA:2021:JTH, author = "Sambit Kumar Beura and Haritha Chellapilla and Mahabir Panda and Prasanta Kumar Bhuyan", title = "Bicycle Comfort Level Rating ({BCLR)} model for urban street segments in mid-sized cities of India", journal = "Journal of Transpor \& Health", volume = "20", pages = "100971", year = "2021", ISSN = "2214-1405", DOI = "doi:10.1016/j.jth.2020.100971", URL = "https://www.sciencedirect.com/science/article/pii/S2214140520301754", keywords = "genetic algorithms, genetic programming, Bicycling comfort, Road segment, Environmental healthiness, Heterogeneous traffic, Genetic programming clustering", abstract = "Introduction The perceived comfort levels of on-street bicyclists are affected by both road characteristics and environmental healthiness. A thorough knowledge of these factors helps to encourage bicycle use and improve human health. This study thus aims to incorporate the parameters describing environmental healthiness in the evaluation of urban street performance. Methods For analysis purpose, extensive data are collected from sixty street segments of three Indian mid-sized cities. Variables having significant influences on bicycling comfort are identified using Spearman's correlation technique and a {"}Bicycle Comfort Level Rating{"} (BCLR) model is developed using the step-wise regression technique. A service scale is also defined using the Genetic Programming (GP) cluster technique to convert model outputs to letter-graded bicycling comfort levels A-F (excellent-worst). Results As observed, the bicycling comfort is influenced by total eight attributes. Of all, air quality index (AQI) is the most significant one (Spearman's correlation coefficient = 0.645). The BCLR model developed using all identified parameters has produced a high coefficient of determination (R2) value of 0.87 with overall observations. Results have also shown that around 97percent segments are offering average-worst levels of bicycling comfort (C-F) at their present scenario. Conclusion An unhealthy environment largely discourages the use of bicycles as a choice mode of transport (as the users are more likely to be exposed to environmental hazards). Hence, the improvement in factors like air quality is essential to encourage the bicycling activity. The roadway parameters like traffic volume, road width and roadside commercial activities, etc. should also be prioritized in the planning process to provide better bicycling comfort. The developed BCLR model is highly reliable for its applications in mid-sized cities of India and other developing countries. This model along with other outcomes of this study would be helpful to enhance the quality of bicycling and public health.", } @InProceedings{conf/icic/BevilacquaNMI16, title = "Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling", author = "Vitoantonio Bevilacqua and Nicola Nuzzolese and Ernesto Mininno and Giovanni Iacca", bibdate = "2017-05-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icic/icic2016-3.html#BevilacquaNMI16", booktitle = "Intelligent Computing Methodologies - 12th International Conference, {ICIC} 2016, Lanzhou, China, August 2-5, 2016, Proceedings, Part {III}", publisher = "Springer", year = "2016", volume = "9773", editor = "De-Shuang Huang and Kyungsook Han and Abir Hussain", isbn13 = "978-3-319-42296-1", pages = "248--259", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming, multi-objective evolutionary algorithms, adaptive genetic programming, machine learning, home automation, energy efficiency", URL = "https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_24", DOI = "doi:10.1007/978-3-319-42297-8_24", abstract = "We propose in this paper a modification of one of the modern state-of-the-art genetic programming algorithms used for data-driven modelling, namely the Bi-objective Genetic Programming (BioGP). The original method is based on a concurrent minimization of both the training error and complexity of multiple candidate models encoded as Genetic Programming trees. Also, BioGP is empowered by a predator-prey co-evolutionary model where virtual predators are used to suppress solutions (preys) characterised by a poor trade-off error vs complexity. In this work, we incorporate in the original BioGP an adaptive mechanism that automatically tunes the mutation rate, based on a characterisation of the current population (in terms of entropy) and on the information that can be extracted from it. We show through numerical experiments on two different datasets from the energy domain that the proposed method, named BioAGP (where A stands for Adaptive), performs better than the original BioGP, allowing the search to maintain a good diversity level in the population, without affecting the convergence rate.", } @InProceedings{beyer:1999:FNLEOGQFM, author = "Hans-Georg Beyer and Dirk V. Arnold", title = "Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "817--824", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/beyer_GECCO99.ps.gz", URL = "http://www.cs.dal.ca/~dirk/docs/GECCO99.ps.gz", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{beyer_et_al:DSP:2006:498, author = "Hans-Georg Beyer and Thomas Jansen and Colin Reeves and Michael D. Vose", title = "04081 Abstracts Collection -- Theory of Evolutionary Algorithms", booktitle = "Theory of Evolutionary Algorithms", year = "2004", editor = "Hans-Georg Beyer and Thomas Jansen and Colin Reeves and Michael D. Vose", number = "04081", series = "Dagstuhl Seminar Proceedings", ISSN = "1862-4405", publisher = "Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany", address = "Dagstuhl, Germany", URL = "http://drops.dagstuhl.de/opus/volltexte/2006/498", note = "$<$http://drops.dagstuhl.de/opus/volltexte/2006/498$>$ [date of citation: 2006-01-01]", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, co-evolution, run time analysis, landscape analysis, Markov chains", abstract = "From 15.02.04 to 20.02.04, the Dagstuhl Seminar 04081 ``Theory of Evolutionary Algorithms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.", notes = "See also \cite{langdon:2003:normal} and \cite{mcphee:ots:gecco2004}", } @Article{beyer:2004:GPEM, author = "Hans-Georg Beyer and Markus Olhofer and Bernhard Sendhoff", title = "On the Impact of Systematic Noise on the Evolutionary Optimization Performance -- A Sphere Model Analysis", journal = "Genetic Programming and Evolvable Machines", year = "2004", volume = "5", number = "4", pages = "327--360", month = dec, keywords = "ES, evolution strategies, noisy optimisation, performance analysis, robust optimization", ISSN = "1389-2576", DOI = "doi:10.1023/B:GENP.0000036020.79188.a0", abstract = "Quality evaluations in optimisation processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective parameters or to environmental parameters inside the fitness function. We thoroughly analyse the sphere function with systematic noise for the evolution strategy with global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between [mu] and [lambda]. Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalised mutation strength. A residual localisation error R[infin] can be quantified and a second [mu] to [lambda] ratio is derived by minimising R[infin].", notes = "Article ID: 5272968", } @Article{beyer:2005:GPEM, author = "Hans-Georg Beyer and Dirk V. Arnold and Silja Meyer-Nieberg", title = "A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "7--24", month = mar, keywords = "ES, evolution strategies, final fitness error, noisy optimization, optimization quality, robust optimization", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7617-y", abstract = "Differential-geometric methods are applied to derive steady state conditions for the (mgr/mgrI,lambda)-ES on the general quadratic test function disturbed by fitness noise of constant strength. A new approach for estimating the expected final fitness deviation observed under such conditions is presented. The theoretical results obtained are compared with real ES runs, showing a surprisingly excellent agreement.", } @Proceedings{GECCO2005, title = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", address = "Washington DC, USA", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, A-Life, Evolutionary Robotics and Adaptive Behaviour, Ant Colony Optimisation and Swarm Intelligence, Artificial Immune Systems, Biological Applications, Coevolution, Estimation of Distribution Algorithms, Evolutionary Combinatorial Optimisation, Evolutionary Multi-objective Optimization, Evolutionary Strategies, Evolutionary Programming, Evolvable Hardware, Meta-heuristics and Local Search, Real World Applications, Search-based Software Engineering", ISBN = "1-59593-010-8", URL = "http://portal.acm.org/citation.cfm?id=1068009&jmp=cit&coll=GUIDE&dl=GUIDE&CFID=48779769&CFTOKEN=55479664#supp", abstract = "The papers in this two volume proceedings are presented at the 7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005), held in Washington, D.C., June 25-29, 2005.This year is an exceptional one for the GECCO conference series. First, the International Society for Genetic and Evolutionary Computation (ISGEC) which has always been GECCO's sponsor has changed to become a Special Interest Group of the ACM named SIGEVO. Being part of ACM reflects the evolution and integration of our very successful discipline into the main stream of computer science. As a consequence, the GECCO-2005 proceedings are an ACM publication and they are incorporated into the ACM Digital Library. This guarantees an even broader dissemination of Darwinian and other nature-inspired computation methods.Second, we had 549 regular paper submissions representing the absolute record of all conferences emphasising the field of evolutionary computation. Paper reviewing has been done by double blind assignment. On average each paper was evaluated by five independent reviewers. Finally, 253 paper (46.1%) have been accepted as full (max. 8 pages) papers. Additionally, 120 submissions were accepted as posters.A goal of GECCO is to encourage new areas and paradigms of evolutionary computation to gather momentum and flourish. This is accomplished by the establishment of new independent tracks each year. This year, as a result of a recombinative and creative process, GECCO-2005 comprises 16 tracks consisting of core tracks ({"}C{"}), tracks previously in GECCOs ({"}P{"}), not yet belonging to the core track family), {"}recombined{"} tracks from GECCO 2004 ({"}R{"}), and newly created tracks ({"}N{"}):.", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052", } @Article{Beyer:2006:GPEM, author = "Hans-Georg Beyer", title = "Special Issue: Best of GECCO 2005", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "2", pages = "129--130", month = aug, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9002-x", size = "2 pages", notes = "Introduction to special issue", } @Article{Beyer:2007:GPEM, author = "Hans-Georg Beyer and Silja Meyer-Nieberg", title = "Self-adaptation of evolution strategies under noisy fitness evaluations", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "4", pages = "295--328", month = dec, keywords = "Evolution strategies, Self-adaptation, Noisy optimisation, Noisy sphere model", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9017-3", size = "34 pages", abstract = "This paper investigates the self-adaptation behaviour of (1,L)-evolution strategies (ES) on the noisy sphere model. To this end, the stochastic system dynamics is approximated on the level of the mean value dynamics. Being based on this microscopic analysis, the steady state behavior of the ES for the scaled noise scenario and the constant noise strength scenario will be theoretically analysed and compared with real ES runs. An explanation will be given for the random walk like behaviour of the mutation strength in the vicinity of the steady state. It will be shown that this is a peculiarity of the (1,L)-ES and that intermediate recombination strategies do not suffer from such behaviour.", } @InCollection{bezdek:1999:EADC, author = "Trevor Bezdek", title = "Evolution and Analysis of DNA Classifiers", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "21--30", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @Article{Bhalla:2009:GPEM, author = "Navneet Bhalla", title = "Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots Elsevier, 2008, 310 pp, 41 colour plates, hard cover, \$160 USD list price, ISBN 978-0-444-52865-0", journal = "Genetic Programming and Evolvable Machines", year = "2009", volume = "10", number = "4", pages = "473--475", month = dec, note = "Book Review", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-009-9088-z", size = "3 pages", } @InProceedings{bhanu:2002:GECCO:workshop, title = "Coevolutionary Construction of Features for Transformation of Representation in Machine Learning", author = "Bir Bhanu and Krzysztof Krawiec", pages = "249--254", booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming", broken = "http://www-idss.cs.put.poznan.pl/~krawiec/./pubs/gecco2002.pdf", broken = "http://citeseer.ist.psu.edu/509773.html", URL = "https://www.researchgate.net/publication/2496301", size = "6 pages", abstract = "The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature.", notes = "also known as \cite{BhanuKrawiec02} Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{bhanu:2002:gecco, author = "Bir Bhanu and Yingqiang Lin", title = "Learning Composite Operators For Object Detection", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1003--1010", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, real world applications, composite operators, genetic image segmentation, object detection", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA165_v2.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @Article{bhanu:2004:ASC, author = "Bir Bhanu and Yingqiang Lin", title = "Object detection in multi-modal images using genetic programming", journal = "Applied Soft Computing", year = "2004", volume = "4", number = "2", pages = "175--201", month = may, keywords = "genetic algorithms, genetic programming", broken = "http://www.sciencedirect.com/science/article/B6W86-4BV444R-1/2/7540dd938c0b2f3059b1afb5382bd28a", DOI = "doi:10.1016/j.asoc.2004.01.004", abstract = "In this paper, we learn to discover composite operators and features that are synthesized from combinations of primitive image processing operations for object detection. Our approach is based on genetic programming (GP). The motivation for using GP-based learning is that we hope to automate the design of object detection system by automatically synthesizing object detection procedures from primitive operations and primitive features. There are many basic operations that can operate on images and the ways of combining these primitive operations to perform meaningful processing for object detection are almost infinite. The human expert, limited by experience, knowledge and time, can only try a very small number of conventional combinations. Genetic programming, on the other hand, attempts many unconventional combinations that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. To improve the efficiency of GP, we propose soft composite operator size limit to control the code-bloat problem while at the same time avoid severe restriction on the GP search. Our experiments, which are performed on selected regions of images to improve training efficiency, show that GP can synthesize effective composite operators consisting of pre-designed primitive operators and primitive features to effectively detect objects in images and the learned composite operators can be applied to the whole training image and other similar testing images.", } @InProceedings{bhanu:2003:gecco, author = "Krzysztof Krawiec and Bir Bhanu", title = "Coevolution and Linear Genetic Programming for Visual Learning", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "332--343", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2723", series = "LNCS", ISBN = "3-540-40602-6", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Coevolution", DOI = "doi:10.1007/3-540-45105-6_39", abstract = "a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition system, by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery, shows the competitiveness of the proposed approach with human-designed recognition systems.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{bhanu:fsu:gecco2004, author = "Bir Bhanu and Jiangang Yu and Xuejun Tan and Yingqiang Lin", title = "Feature Synthesis Using Genetic Programming for Face Expression Recognition", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "896--907", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", size = "12", keywords = "genetic algorithms, genetic programming", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", } @Article{Bhanu:2004:PRL, author = "Bir Bhanu and Yingqiang Lin", title = "Synthesizing feature agents using evolutionary computation", journal = "Pattern Recognition Letters", year = "2004", volume = "25", pages = "1519--1531", number = "13", abstract = "genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called {"}agents{"} and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V15-4CRY8J6-2/2/d245bfcfeee2d509066321e19d84a0fd", month = "1 " # oct, note = "Pattern Recognition for Remote Sensing (PRRS 2002)", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1016/j.patrec.2004.06.005", size = "13 pages", notes = "SAR", } @Book{Bhanu:book, author = "Bir Bhanu and Yingqiang Lin and Krzysztof Krawiec", title = "Evolutionary Synthesis of Pattern Recognition Systems", year = "2005", publisher = "Springer-Verlag", address = "New York", series = "Monographs in Computer Science", keywords = "genetic algorithms, genetic programming, visual learning, feature synthesis, Computer vision, Image processing, Object detection, Pattern recognition", ISBN = "0-387-21295-7", URL = "http://www.springer.com/west/home/computer/imaging?SGWID=4-149-22-39144807-detailsPage=ppmmedia|aboutThisBook", size = "296 pages", } @Article{Bharadwaj:2007:waset, author = "Kamal K. Bharadwaj and Basheer M. Al-Maqaleh", title = "Evolutionary Approach for Automated Discovery of Censored Production Rules", journal = "International Journal of Computer, Information Science and Engineering", volume = "1", number = "10", year = "2007", pages = "11--16", keywords = "genetic algorithms, genetic programming, data mining, machine learning, evolutionary algorithms", bibsource = "http://waset.org/Publications", ISSN = "1307-6892", publisher = "World Academy of Science, Engineering and Technology", index = "International Science Index 10, 2007", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.308.7101", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.7101", URL = "http://waset.org/publications/14169", URL = "http://waset.org/Publications?p=10", size = "6 pages", abstract = "In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski & Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations, in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the 'If P Then D' part of the CPR expresses important information, while the Unless C part acts only as a switch and changes the polarity of D to ~D. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of CPRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a CPR. Appropriate genetic operators are suggested and a fitness function is proposed that incorporates the basic constraints on CPRs. Experimental results are presented to demonstrate the performance of the proposed algorithm.", notes = "oai:CiteSeerX.psu:10.1.1.308.7101 http://waset.org/publications/14169", } @Article{Bharambe:2013:ijetae, author = "Dewendra Bharambe and Susheel Jain and Anurag Jain", title = "A Detection of Duplicate Records from Multiple Web Databases using pattern matching in UDD", journal = "International Journal of Emerging Technology and Advanced Engineering", year = "2013", volume = "3", number = "5", pages = "412--417", month = may, keywords = "genetic algorithms, genetic programming, data deduplication, UDD, SVM, WCSS, genetic algorithm, pattern matching", ISSN = "2250--2459", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.413.7928", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.7928", URL = "http://www.ijetae.com/files/Volume3Issue5/IJETAE_0513_68.pdf", URL = "http://www.ijetae.com/Volume3Issue5.html", abstract = "Record matching refers to the task of finding entries that refer to the same entity in two or more files, is a vital process in data integration. Most of the supervised record matching methods require training data provided by users. Such methods can not apply for web database scenario, where query results dynamically generated. In existing system, an unsupervised record matching method effectively identifies the duplicates from query result records of multiple web databases by identifying the duplicate and non duplicate set in the source and from that non duplicate set again searches for the existence of duplication. Then use two co-operative classifiers from the non duplicate set, they are Weighted Component Similarity Summing (WCSS) Classifier and Support Vector Machine (SVM) classifier. These two classifiers can be used to identify the query results iteratively from multiple web databases. In this paper we modify record matching algorithm with genetic algorithm. The genetic programming is time consuming so we proposed UDD with genetic programming. A performance evaluation for accuracy is done for the dataset with duplicates using UDD and UDD with Genetic algorithm.", notes = "Article 68.", } @Article{Bhardwaj:2011:ACIJ, author = "Arpit Bhardwaj and Aditi Sakalle and Harshita Chouhan and Harshit Bhardwaj", title = "Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique", year = "2011", journal = "Advanced Computing : an International Journal", volume = "2", number = "6", pages = "59--68", month = nov, publisher = "Academy \& Industry Research Collaboration Center (AIRCC)", keywords = "genetic algorithms, genetic programming, bloat, stepwise crossover, double mutation, elitism, fitness, Java, Oracle 10g", ISSN = "2229726X", URL = "http://airccse.org/journal/acij/papers/1111acij06.pdf", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=2229726X\&date=2011\&volume=2\&issue=6\&spage=59", DOI = "doi:10.5121/acij.2011.2606", size = "10 pages", abstract = "During the evolution of solutions using genetic programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness---a phenomenon commonly referred to as bloat. The conception of bloat in Genetic Programming is a well naturalised phenomenon characterised by variable-length genomes gradually maturating in size during evolution. 'In a very real sense, bloating makes genetic programming a race against time, to find the best solution possible before bloat puts an effective stop to the search.' In this paper we are proposing a Stepwise crossover and double mutation operation in order to reduce the bloat. In this especial crossover operation we are using local elitism replacement in combination with depth limit and size of the trees to reduce the problem of bloat substantially without compromising the performance. The use of local elitism in crossover and mutation increases the accuracy of the operation and also reduces the problem of bloat and further improves the performance. To shew our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets.", notes = "IRIS, WBC, BUPA, WINE, ABALONE, SPOKEN ARABIC DIGIT, HILL-VALLEY", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:4e07bf6e3c343d42b02de6aed48a4d17", } @InProceedings{Bhardwaj:2013:GECCOcomp, author = "Arpit Bhardwaj and Aruna Tiwari", title = "Performance improvement in genetic programming using modified crossover and node mutation", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1721--1722", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2480787", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "During the evolution of solutions using Genetic Programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness'a phenomenon commonly referred to as bloat. Bloating increases time to find the best solution. Sometimes, best solution can never be obtained. In this paper we are proposing a modified crossover and point mutation operation in GP algorithm in order to reduce the problem of bloat. To demonstrate our approach, we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying modified crossover together with modified node mutation reduces the problem of bloat substantially without compromising the performance.", notes = "Also known as \cite{2480787} Distributed at GECCO-2013.", } @InProceedings{Bhardwaj:2013:ICIC, author = "Arpit Bhardwaj and Aruna Tiwari", title = "A Novel Genetic Programming Based Classifier Design Using a New Constructive Crossover Operator with a Local Search Technique", booktitle = "International Conference on Intelligent Computing (ICIC 2013)", year = "2013", editor = "De-Shuang Huang and Vitoantonio Bevilacqua and Juan Carlos Figueroa and Prashan Premaratne", volume = "7995", series = "Lecture Notes in Computer Science", pages = "86--95", address = "Nanning, China", month = jul # " 28-31", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Crossover, Local Search Technique", isbn13 = "978-3-642-39478-2", DOI = "doi:10.1007/978-3-642-39479-9_11", size = "10 pages", abstract = "A common problem in genetic programming search algorithms is the destructive nature of the crossover operator in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. In this paper, we proposed the integration of variants of local search techniques in the breeding process, done by allowing parents to produce many off springs and applying a selection procedure to choose high performing off springs. Our approach has removed the randomness of crossover operator. To demonstrate our approach, we designed a Multiclass classifier and tested it on various benchmark datasets. Our method has shown the tremendous improvement over the other state of the art methods.", } @InProceedings{Bhardwaj:2014:GECCOcomp, author = "Arpit Bhardwaj and Aruna Tiwari and M. Vishaal Varma and M. Ramesh Krishna", title = "Classification of EEG signals using a novel genetic programming approach", booktitle = "GECCO 2014 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC)", year = "2014", editor = "Stephen L. Smith and Stefano Cagnoni and Robert M. Patton", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming", pages = "1297--1304", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2609851", DOI = "doi:10.1145/2598394.2609851", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper, we present a new method for classification of electroencephalogram (EEG) signals using Genetic Programming (GP). The Empirical Mode Decomposition (EMD) is used to extract the features of EEG signals which served as an input for the GP. In this paper, new constructive crossover and mutation operations are also produced to improve GP. In these constructive crossover and mutation operators hill climbing search is integrated to remove the destructive nature of these operators. To improve GP, we apply constructive crossover on all the individuals which remain after reproduction. A new concept of selecting the global prime off-springs of the generation is also proposed. The constructive mutation approach is applied to poor individuals who are left after selecting globally prime off-springs. Improvement of the method is measured against classification accuracy, training time and the number of generations for EEG signal classification. As we show in the results section, the classification accuracy can be estimated to be 98.69percent on the test cases, which is better than classification accuracy of Liang and coworkers method which was published in 2010.", notes = "Also known as \cite{2609851} Distributed at GECCO-2014.", } @InProceedings{Bhardwaj:2014:BMEI, author = "Arpit Bhardwaj and Aruna Tiwari and Dharmil Chandarana and Darshil Babel", title = "A Genetically Optimized Neural Network for Classification of Breast Cancer Disease", booktitle = "7th International Conference on Biomedical Engineering and Informatics (BMEI 2014)", year = "2014", month = oct, pages = "693--698", keywords = "genetic algorithms, genetic programming, ANN, SVN", DOI = "doi:10.1109/BMEI.2014.7002862", size = "6 pages", abstract = "In this paper, we propose a new, Genetically Optimised Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimise its structure for classification. We introduce new crossover and mutation operations which differ from a normal Genetic programming life-cycle to reduce the destructive nature of these operations. We use the GONN algorithm to classify breast cancer tumours as benign or malignant. Accurate classification of a breast cancer tumour is an important task in medical diagnosis. Our algorithm gives better classification accuracy of almost 4percent and 2percent more than a Back Propagation neural network and a Support Vector Machine respectively.", notes = "Discipline of Comput. Sci. & Eng., Indian Inst. of Technol. Indore, Indore, India Also known as \cite{7002862}", } @Article{Bhardwaj:2015:ESA, author = "Arpit Bhardwaj and Aruna Tiwari", title = "Breast cancer diagnosis using Genetically Optimized Neural Network model", journal = "Expert Systems with Applications", volume = "42", number = "10", pages = "4611--4620", year = "2015", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2015.01.065", URL = "http://www.sciencedirect.com/science/article/pii/S0957417415000883", abstract = "One in every eight women is susceptible to breast cancer, at some point of time in her life. Early detection and effective treatment is the only rescue to reduce breast cancer mortality. Accurate classification of a breast cancer tumour is an important task in medical diagnosis. Machine learning techniques are gaining importance in medical diagnosis because of their classification capability. In this paper, we propose a new, Genetically Optimised Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimize its architecture (structure and weight) for classification. We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators. We use the GONN algorithm to classify breast cancer tumors as benign or malignant. To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model. Our algorithm gives classification accuracy of 98.24percent, 99.63percent and 100percent for 50-50, 60-40, 70-30 training-testing partition respectively and 100percent for 10 fold cross validation. The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods.", keywords = "genetic algorithms, genetic programming, Genetically Optimised Neural Network, Artificial Neural Network, Modified Crossover Operator", } @InProceedings{Bhardwaj:2015:GECCO, author = "Arpit Bhardwaj and Aruna Tiwari and M. Vishaal Varma and M. Ramesh Krishna", title = "An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "209--216", keywords = "genetic algorithms, genetic programming, Biological and Biomedical Applications", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754710", DOI = "doi:10.1145/2739480.2754710", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analysed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system.", notes = "Also known as \cite{2754710} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @Article{Bhardwaj:2016:CMPB, author = "Arpit Bhardwaj and Aruna Tiwari and Ramesh Krishna and Vishaal Varma", title = "A novel genetic programming approach for epileptic seizure detection", journal = "Computer Methods and Programs in Biomedicine", volume = "124", pages = "2--18", year = "2016", ISSN = "0169-2607", DOI = "doi:10.1016/j.cmpb.2015.10.001", URL = "http://www.sciencedirect.com/science/article/pii/S016926071500262X", abstract = "The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of our method for accurately detecting epileptic seizures in an EEG signal.", keywords = "genetic algorithms, genetic programming, Constructive crossover, Dynamic fitness value computation, Epilepsy", } @InProceedings{Bhardwaj:2015:IC4, author = "Harshit Bhardwaj and Pankaj Dashore", booktitle = "2015 International Conference on Computer, Communication and Control (IC4)", title = "A novel genetic programming approach to control bloat using crossover and mutation with intelligence technique", year = "2015", abstract = "Bloat is a problem that occurs when there is no advancement in fitness measure, but the size of the tree grows exponentially. Bloat eventually increases the time required to reach the optimal solution. To overcome this defect, Crossover and Mutation with Intelligence technique is proposed. We also used double tournament, in which we apply two tournaments on the basis of size and fitness respectively to select the individuals to perform Crossover with Intelligence. Our approach of overcoming bloat is tested experimentally on some benchmark datasets picked up from UCI repository and by some observations. The results verified that our Crossover and Mutation with Intelligence degrades the bloat phenomena.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IC4.2015.7375619", month = sep, notes = "Comput. Sci. Dept., Medicaps Inst. of Sci. & Technol., Indore, India Also known as \cite{7375619}", } @InProceedings{Bhardwaj:2018:ieeeCompIntl, author = "Harshit Bhardwaj and Aditi Sakalle and Arpit Bhardwaj and Aruna Tiwari and Madhushi Verma", booktitle = "2018 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach", year = "2018", pages = "2186--2192", abstract = "Breast cancer is the most prevalent type of cancer found in women worldwide. It is becoming a leading cause of death among women in the whole world. Early detection and effective treatment of this disease is the only rescue to reduce breast cancer mortality. Because of the effective classification and high diagnostic capability expert systems are gaining popularity in this field. But the problem with machine learning algorithms is that if redundant and irrelevant features are available in the dataset then they are not being able to achieve desired performance. Therefore, in this paper, a simultaneous feature selection and classification technique using Genetic Programming (GPsfsc) is proposed for breast cancer diagnosis. To demonstrate our results, we had taken the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) databases from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, and Mann Whitney test results of GONN with classical multi-tree GP algorithm for feature selection (GPmtfs). The experimental results on WBC and WDBC datasets show that the proposed method produces better classification accuracy with reduced features. Therefore, our proposed method is of great significance and can serve as first-rate clinical tool for the detection of breast cancer.", keywords = "genetic algorithms, genetic programming, Computational intelligence, Feature Selection, Breast Cancer Diagnosis, Classification", DOI = "doi:10.1109/SSCI.2018.8628935", month = nov, notes = "Also known as \cite{8628935}", } @Article{Bhardwaj:2019:ES, author = "Harshit Bhardwaj and Aditi Sakalle and Arpit Bhardwaj and Aruna Tiwari", title = "Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming", journal = "Expert Systems", year = "2019", volume = "36", number = "1", pages = "e12338", month = feb, keywords = "genetic algorithms, genetic programming", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/es/es36.html#BhardwajSBT19", DOI = "doi:10.1111/exsy.12338", abstract = "Epilepsy, sometimes called seizure disorder, is a neurological condition that justifies itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of electrical activity in the brain that causes an alteration in behaviour, sensation, or consciousness. It is essential to have a method for automatic detection of seizures, as these seizures are arbitrary and unpredictable. A profound study of the electroencephalogram (EEG) recordings is required for the accurate detection of these epileptic seizures. In this study, an Innovative Genetic Programming framework is proposed for classification of EEG signals into seizure and non-seizure. An empirical mode decomposition technique is used for the feature extraction followed by genetic programming for the classification. Moreover, a method for intron deletion, hybrid crossover, and mutation operation is proposed, which are responsible for the increase in classification accuracy and a decrease in time complexity. This suggests that the Innovative Genetic Programming classifier has a potential for accurately predicting the seizures in an EEG signal and hints on the possibility of building a real-time seizure detection system.", notes = "journals/es/BhardwajSBT19,", } @Book{bhardwaj:atgp1, author = "Arpit Bhardwaj and Aruna Tiwari and Jasjit S. Suri", title = "Advances and Trends in Genetic Programming: Volume 1: Classification Techniques and Life Cycles Paperback", publisher = "Academic Press", year = "2022", keywords = "genetic algorithms, genetic programming", isbn13 = "978-0128180204", URL = "https://www.amazon.co.uk/s?k=advances+and+trends+in+genetic+programming+%3A+volume+1%3A+classification+techniques+and+life+cycles", size = "220 pages", notes = " Sep 2023 Currently unavailable", } @InProceedings{Bhargava:2023:CSET, author = "Krishna Bhargava A and Deepak Kumar Sinha and Garima Sinha", booktitle = "2023 International Conference on Computer Science and Emerging Technologies (CSET)", title = "Network Optimization Using Genetic Programming", year = "2023", abstract = "Evolutionary Algorithms form the base for creating Artificial Intelligence applications and systems. Evolutionary Computation forms the basis of those algorithms that are used in day to day and future applications. Computer networks as we know, form a major contributor to those data sources. Big Data and all types of computer related data are found on the internet and the internet forms sources of data all over the world. Therefore, data modulation techniques form a basis of data transfer over the internet all over the world. Therefore, Evolutionary Networks are the type of computer networks that evolve over time due to the evolutionary nature of the algorithms involved. Our intention in the paper is to create a fault free data modulation technique used in computer networking. In the network, the timing is based on the evolutionary data of the customer. The internet history of the customer is taken into consideration and the data modulation is based on the customer history. How this is done is based on the pheromone concentration of ant colony optimisation and the probable path of the bee algorithm. These evolutionary algorithms are fed to the firewall and routing tables of the network to form the evolutionary network generation and user behaviour. When the algorithms working behind firewall and routing tables are replaced by Evolutionary Algorithms, then they start to showcase evolutionary behaviour. This is the foundation principle on which this paper is based. Once the firewall and the routing table are evolved, the computer network to which they are connected becomes Evolutionary Networks.", keywords = "genetic algorithms, genetic programming, Firewalls (computing), Soft sensors, Modulation, Evolutionary computation, Routing, Behavioural sciences, Evolutionary Computation, Evolutionary Algorithms, Evolutionary Networking, Network Fitness, Data Fitness, Evolutionary based Data Modulation", DOI = "doi:10.1109/CSET58993.2023.10346779", month = oct, notes = "Also known as \cite{10346779}", } @Article{Bhargavi:2010:IJCSIT, title = "Soil Classification Using {GATREE}", author = "P. Bhargavi and S. Jyothi", journal = "International Journal of Computer Science \& Information Technology", year = "2010", volume = "2", number = "5", pages = "184--191", keywords = "genetic algorithms, genetic programming, data mining, soil profile, soil database, classification", ISSN = "09754660", URL = "http://airccse.org/journal/jcsit/1010ijcsit14.pdf", DOI = "doi:10.5121/ijcsit.2010.2514", publisher = "Academy \& Industry Research Collaboration Centre (AIRCC)", bibsource = "OAI-PMH server at www.doaj.org", oai = "oai:doaj-articles:62c4c972981e7958ba9ff79981358355", size = "8 pages", abstract = "This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic Algorithms(GAs). The idea behind it is rather simple but powerful. Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global metric of tree quality that try to optimise accuracy and size. GATree offers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many difficult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil texture for soil data set.", } @InProceedings{Bhatt:2015:ieeeCGVIS, author = "M. S. Bhatt and T. P. Patalia", booktitle = "2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS)", title = "Genetic programming evolved spatial descriptor for Indian monuments classification", year = "2015", pages = "131--136", abstract = "Travel and tourism are the largest service industries in India. Every year people visit tourist places. and upload pictures of their visit on social networking sites or share via mobile device with friends and relatives. Millions of such photographs are uploaded and it is almost impossible to manually classify these pictures as per the monuments they have visited. Classification is helpful to hoteliers for development of new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security etc. The proposed system extracts Genetic programming evolved spatial descriptor and classifies the Indian monuments visited by tourists based on linear Support Vector Machine(SVM). The proposed system is divided into 3 main phases: preprocessing, genetic programming evolution and classification. The Preprocessing phase converts images into a form suitable for processing by genetic programming system using Generalized Co-Occurrence Matrix. The second phase generates best so far spatial descriptor in the form of program based on the fitness. The Fitness is calculated using SVM. Once program is obtained as output it can be used for classification. The proposed system is implemented in MATLAB and achieves high accuracy.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CGVIS.2015.7449908", month = nov, notes = "Also known as \cite{7449908}", } @InProceedings{Bhattacharya:2001:GPR, author = "Maumita Bhattacharya and Baikunth Nath", title = "Genetic Programming: {A} Review of Some Concerns", volume = "2074", pages = "1031--1040", year = "2001", booktitle = "Proceedings of International Conference Computational Science Part~II - ICCS 2001", editor = "V. N. Alexandrov and J. J. Dongarra and B. A. Juliano and R. S. Renner and C. J. Kenneth Tan", series = "Lecture Notes in Computer Science", address = "San Francisco, CA, USA", month = may # " 28-30", publisher = "Springer", note = "Late Submissions", keywords = "genetic algorithms, genetic programming, bloat", CODEN = "LNCSD9", ISSN = "0302-9743", isbn13 = "978-3-540-42233-4", bibdate = "Sat Feb 2 13:04:30 MST 2002", DOI = "doi:10.1007/3-540-45718-6_109", acknowledgement = ack-nhfb, size = "10 pages", abstract = "Genetic Programming (GP) is gradually being accepted as a promising variant of Genetic Algorithm (GA) that evolves dynamic hierarchical structures, often described as programs. In other words GP seemingly holds the key to attain the goal of 'automated program generation'. However one of the serious problems of GP lies in the 'code growth' or 'size problem' that occurs as the structures evolve, leading to excessive pressure on system resources and unsatisfying convergence. Several researchers have addressed the problem. However, absence of a general framework and physical constraints, viz, infinitely large resource requirements have made it difficult to find any generic explanation and hence solution to the problem. This paper surveys the major research works in this direction from a critical angle. Overview of a few other major GP concerns is covered in brief. We conclude with a general discussion on code growth and other critical aspects of GP techniques, while attempting to highlight on future research directions to tackle such problems.", } @InProceedings{bhattacharya:2001:HIS, title = "A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria", author = "Maumita Bhattacharya and Ajith Abraham and Baikunth Nath", editor = "Ajith Abraham and Mario Koppen", booktitle = "2001 International Workshop on Hybrid Intelligent Systems", series = "LNCS", pages = "379--394", publisher = "Springer-Verlag", address = "Adelaide, Australia", publisher_address = "Berlin", month = "11-12 " # dec, year = "2001", email = "maumita.bhattacharya@infotech.monash.edu.au, ajith.abraham@infotech.monash.edu.au, b.nath@infotech.monash.edu.au", keywords = "genetic algorithms, genetic programming, Linear genetic programming, neuro-fuzzy, neural networks, forecasting, electricity demand", broken = "http://www-mugc.cc.monash.edu.au/~abrahamp/172.pdf", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6", URL = "http://citeseer.ist.psu.edu/510872.html", ISBN = "3-7908-1480-6", abstract = "Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, Australia, while comparing its performance with two other popular soft computing techniques. The forecast accuracy is compared with the actual energy demand. To evaluate, we considered load demand patterns for ten consecutive months taken every 30 minutes for training the different prediction models. Test results show that while the linear genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and computation time, as compared to LGP and neural networks.", notes = "HIS01 Possibly also of interest Applied Soft Computing Volume 1, Issue 2 , August 2001, Pages 127-138 doi:10.1016/S1568-4946(01)00013-8", } @InProceedings{bhattacharyya:1998:rsGPlhf, author = "Siddhartha Bhattacharyya and Olivier Pictet and Gilles Zumbach", title = "Representational Semantics for Genetic Programming Based Learning in High-Frequency Financial Data", booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference", year = "1998", editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo", pages = "11--16", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "San Francisco, CA, USA", month = "22-25 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-548-7", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/bhattacharyya_1998_rsGPlhf.pdf", notes = "GP-98", } @Article{bhattacharyya:1998:DS, author = "Siddhartha Bhattacharyya and Parag C. Pendharkar", title = "Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study", journal = "Decision Sciences", year = "1998", volume = "29", number = "4", pages = "871--899", month = "Fall", keywords = "genetic algorithms, genetic programming, Discriminant Analysis, Inductive Learning, Machine Learning, and Neural Networks", ISSN = "00117315", URL = "http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf", size = "45 pages", abstract = "This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data is used to examine how the different learning techniques perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data has been previously used in the comparative evaluation of a number of techniques and helps relate our findings across a range of discrimination techniques.", notes = "http://www.decisionsciences.org/dsj/ (USPS 884860) http://www.decisionsciences.org/dsj/Vol29_4/29_4_871.htm", } @InProceedings{347186, author = "Siddhartha Bhattacharyya", title = "Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing", booktitle = "KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining", year = "2000", pages = "465--473", address = "Boston, Massachusetts, United States", publisher_address = "New York, NY, USA", organisation = "SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data AAAI : Am Assoc for Artifical Intelligence SIGART: ACM Special Interest Group on Artificial Intelligence SIGMOD: ACM Special Interest Group on Management of Data", publisher = "ACM Press", keywords = "genetic algorithms, genetic programming, Algorithms, Design, Experimentation, Human Factors, Management, Measurement, Performance, Theory, Pareto-optimal models, data mining, database marketing, evolutionary computation, multiple objectives", ISBN = "1-58113-233-6", URL = "http://tigger.uic.edu/~sidb/papers/MultiObj_KDD2000.pdf", URL = "http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530", DOI = "doi:10.1145/347090.347186", size = "9 pages", abstract = "Predictive models in direct marketing seek to identify individuals most likely to respond to promotional solicitations or other intervention programs. While standard modelling approaches embody single objectives, real-world decision problems often seek multiple performance measures. Decision-makers here desire solutions that simultaneously optimise on multiple objectives, or obtain an acceptable tradeoff amongst objectives. Multi-criteria problems often characterise a range of solutions, none of which dominate the others with respect to the multiple objectives, these specify the Pareto-frontier of nondominated solutions, each offering a different level of tradeoff. This paper proposes the use of evolutionary computation based procedures for obtaining a set of nondominated models with respect to multiple stated objectives. The targeting depth-of-file presents a crucial real-world criterion in direct marketing, and models here are tailored for specified file-depths. Decision-makers are thus able to obtain a set of models along the Pareto-frontier, for a specific file-depth. The choice of a model to implement can be thus based on observed tradeoffs in the different objectives, based on possibly subjective and problem specific judgements. Given distinct models tailored for different file-depths, the implementation decision can also consider performance tradeoffs at the different depths-offile. Empirical results from a real-world problem illustrate the benefits of the proposed approach. Both linear and nonlinear models obtained by genetic search are examined.", notes = "p470 'For the non-linear GP, results were found to be similar to those observed for the linear GA.' 'Elitism always provides improved performance'.", } @InCollection{bhattacharyya:2002:ECEF, author = "Siddhartha Bhattacharyya and Kumar Mehta", title = "Evolutionary Induction of Trading Models", booktitle = "Evolutionary Computation in Economics and Finance", publisher = "Physica Verlag", year = "2002", editor = "Shu-Heng Chen", volume = "100", series = "Studies in Fuzziness and Soft Computing", chapter = "17", pages = "311--332", month = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-7908-1476-8", URL = "http://tigger.uic.edu/~sidb/papers/EvolInductionOfTradingModels.pdf", DOI = "doi:10.1007/978-3-7908-1784-3_17", abstract = "Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterising rules obtained with alternate fitness criteria.", size = "22 pages", } @Article{bhattacharyya:2002:trEC, author = "Siddhartha Bhattacharyya and Olivier V. Pictet and Gilles Zumbach", title = "Knowledge-intensive genetic discovery in foreign exchange markets", journal = "IEEE Transactions on Evolutionary Computation", year = "2002", volume = "6", number = "2", pages = "169--181", month = apr, keywords = "genetic algorithms, genetic programming, Data mining, financial markets, foreign exchange markets, machine learning, semantic restrictions, trading models", ISSN = "1089-778X", URL = "http://tigger.uic.edu/~sidb/papers/KnowIntenGPForex__IEEE_EC.pdf", DOI = "doi:10.1109/4235.996016", size = "13 pages", abstract = "This paper considers the discovery of trading decision models from high-frequency foreign exchange (FX) markets data using genetic programming (GP). It presents a domain-related structuring of the representation and incorporation of semantic restrictions for GP-based searching of trading decision models. A defined symmetry property provides a basis for the semantics of FX trading models. The symmetry properties of basic indicator types useful in formulating trading models are defined, together with semantic restrictions governing their use in trading model specification. The semantics for trading model specification have been defined with respect to regular arithmetic, comparison and logical operators. This study also explores the use of two fitness criteria for optimization, showing more robust performance with a risk-adjusted measure of returns", notes = "CODEN: ITEVF5 INSPEC Accession Number:7256658", } @InProceedings{bhavita:2019:WREE, author = "K. Bhavita and D. Swathi and J. Manideep and D. Sree Sandeep and Maheswaran Rathinasamy", title = "{Regime-Wise} Genetic Programming Model for Improved Streamflow Forecasting", booktitle = "Water Resources and Environmental Engineering I", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-981-13-2044-6_17", DOI = "doi:10.1007/978-981-13-2044-6_17", } @InProceedings{Bhowan:2009:cec, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Differentiating Between Individual Class Performance in Genetic Programming Fitness for Classification with Unbalanced Data", booktitle = "2009 IEEE Congress on Evolutionary Computation", year = "2009", editor = "Andy Tyrrell", pages = "2802--2809", address = "Trondheim, Norway", month = "18-21 " # may, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-2959-2", file = "P289.pdf", DOI = "doi:10.1109/CEC.2009.4983294", abstract = "This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness function evolves classifiers with a performance bias toward the majority class at the expense of minority class performance. We develop four new fitness functions which consider the accuracy of majority and minority class separately to address this learning bias. Results using these fitness functions show that good accuracy for both the minority and majority classes can be achieved from evolved classifiers while keeping overall performance high and balanced across the two classes.", keywords = "genetic algorithms, genetic programming", notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR", } @InProceedings{Bhowan:2009:IVCNZ, title = "Genetic Programming for Image Classification with Unbalanced Data", author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", booktitle = "Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09", year = "2009", month = "23-25 " # nov, pages = "316--321", ISSN = "2151-2205", address = "Wellington", publisher = "IEEE", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-4697-1", DOI = "doi:10.1109/IVCNZ.2009.5378388", abstract = "Image classification methods using unbalanced data can produce results with a performance bias. If the class representing important objects-of-interest is in the minority class, learning methods can produce the deceptive appearance of good looking results while recognition ability on the important minority class can be poor. This paper develops and compares two Genetic Programming (GP) methods for image classification problems with class imbalance. The first focuses on adapting the fitness function in GP to evolve classifiers with good individual class accuracy. The second uses a multi-objective approach to simultaneously evolve a set of classifiers along the trade-off surface representing minority and majority class accuracies. Evaluating our GP methods on two benchmark binary image classification problems with class imbalance, our results show that good solutions were evolved using both GP methods.", notes = "Also known as \cite{5378388}", } @InProceedings{DBLP:conf/ausai/BhowanZJ09, author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", title = "Multi-Objective Genetic Programming for Classification with Unbalanced Data", booktitle = "Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09)", year = "2009", editor = "Ann E. Nicholson and Xiaodong Li", volume = "5866", series = "Lecture Notes in Computer Science", pages = "370--380", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Melbourne, Australia", month = dec # " 1-4", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-10438-1", DOI = "doi:10.1007/978-3-642-10439-8_38", abstract = "Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.", } @InProceedings{Bhowan:2010:EuroGP, author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", title = "Genetic Programming for Classification with Unbalanced Data", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "1--13", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_1", abstract = "Learning algorithms can suffer a performance bias when data sets only have a small number of training examples for one or more classes. In this scenario learning methods can produce the deceptive appearance of good looking results even when classification performance on the important minority class can be poor. This paper compares two Genetic Programming (GP) approaches for classification with unbalanced data. The first focuses on adapting the fitness function to evolve classifiers with good classification ability across both minority and majority classes. The second uses a multi-objective approach to simultaneously evolve a Pareto front (or set) of classifiers along the minority and majority class trade-off surface. Our results show that solutions with good classification ability were evolved across a range of binary classification tasks with unbalanced data.", notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @InProceedings{Bhowan:2010:gecco, author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", title = "AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "845--852", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830639", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach using the accuracy of the minority and majority class as learning objectives. We focus our analysis on the classification ability of evolved Pareto-front solutions using the Area Under the ROC Curve (AUC) and investigate which regions of the objective trade-off surface favour high-scoring AUC solutions. We show that a diverse set of well-performing classifiers is simultaneously evolved along the Pareto-front using the MOGP approach compared to canonical GP where only one solution is found along the objective trade-off surface, and that in some problems the MOGP solutions had better AUC than solutions evolved with canonical GP using hand-crafted fitness functions.", notes = "Also known as \cite{1830639} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{conf/ausai/BhowanZJ10, title = "A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data", author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston", booktitle = "Australasian Conference on Artificial Intelligence", editor = "Jiuyong Li", year = "2010", volume = "6464", series = "Lecture Notes in Computer Science", pages = "243--252", address = "Adelaide", month = dec, publisher = "Springer", bibdate = "2010-11-30", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#BhowanZJ10", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-17431-5", DOI = "doi:10.1007/978-3-642-17432-2_25", size = "10 pages", abstract = "Machine learning algorithms like Genetic Programming (GP) can evolve biased classifiers when data sets are unbalanced. In this paper we compare the effectiveness of two GP classification strategies. The first uses the standard (zero) class-threshold, while the second uses the best class-threshold determined dynamically on a solution-by-solution basis during evolution. These two strategies are evaluated using five different GP fitness across across a range of binary class imbalance problems, and the GP approaches are compared to other popular learning algorithms, namely, Naive Bayes and Support Vector Machines. Our results suggest that there is no overall difference between the two strategies, and that both strategies can evolve good solutions in binary classification when used in combination with an effective fitness function.", affiliation = "School of Engineering and Computer Science, Victoria University of Wellington, New Zealand", } @InProceedings{Bhowan:2011:GECCO, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Evolving ensembles in multi-objective genetic programming for classification with unbalanced data", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1331--1338", keywords = "genetic algorithms, genetic programming", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001756", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-objective Genetic Programming approach using negative correlation learning to evolve accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We also compare two popular Pareto-based fitness schemes on the classification tasks. We show that the evolved ensembles achieve high accuracy on both classes using six unbalanced binary data sets, and that this performance is usually better than many of its individual members.", notes = "Also known as \cite{2001756} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @InProceedings{conf/ausai/BhowanJZ11, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data", booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)", year = "2011", editor = "Dianhui Wang and Mark Reynolds", volume = "7106", series = "Lecture Notes in Computer Science", pages = "192--202", address = "Perth, Australia", month = dec # " 5-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-25832-9_20", size = "11 pages", abstract = "Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper develops a multi-objective genetic programming approach to evolving accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We explore why the ensembles can also be vulnerable to the learning bias using a range of unbalanced data sets. Based on the notion that smaller ensembles can be better than larger ensembles, we develop a new evolutionary-based pruning method to find groups of highly-cooperative individuals that can improve accuracy on the important minority class.", affiliation = "Evolutionary Computation Research Group, Victoria University of Wellington, New Zealand", bibdate = "2011-12-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#BhowanJZ11", } @Article{Bhowan:2012:ieeeTEC, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang and Xin Yao", title = "Evolving Diverse Ensembles using Genetic Programming for Classification with Unbalanced Data", journal = "IEEE Transactions on Evolutionary Computation", year = "2013", volume = "17", number = "3", pages = "368--386", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6198882", DOI = "doi:10.1109/TEVC.2012.2199119", size = "19 pages", abstract = "In classification, machine learning algorithms can suffer a performance bias when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while the other class(es) make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es). This paper proposes a Multi-objective Genetic Programming (MOGP) approach to evolving accurate and diverse ensembles of genetic program classifiers with good performance on both the minority and majority classes. The evolved ensembles comprise of nondominated solutions in the population where individual members vote on class membership. This paper evaluates the effectiveness of two popular Pareto-based fitness strategies in the MOGP algorithm (SPEA2 and NSGAII), and investigates techniques to encourage diversity between solutions in the evolved ensembles. Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, Naive Bayes and Support Vector Machines, on highly unbalanced tasks. This highlights the importance of developing an effective fitness evaluation strategy in the underlying MOGP algorithm to evolve good ensemble members.", ISSN = "1089-778X", notes = "NSGA-II, SPEA2 Also known as \cite{6198882}", } @Article{Bhowan:2012:SMC, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data", journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics", year = "2012", month = apr, volume = "42", number = "2", pages = "406--421", size = "16 pages", abstract = "Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class has on traditional training criteria in the fitness function. This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data. Using a range of real-world classification problems with class imbalance, we empirically show that these new fitness functions evolve classifiers with good performance on both the minority and majority classes. Our approaches use the original unbalanced training data in the GP learning process, without the need to artificially balance the training examples from the two classes (e.g., via sampling).", keywords = "genetic algorithms, genetic programming, GP learning process, biased classifiers, binary classification, class imbalance, data sets, fitness functions, machine learning algorithms, majority class, minority class, training criteria, unbalanced data, unbalanced training data, data handling, learning (artificial intelligence), pattern classification", DOI = "doi:10.1109/TSMCB.2011.2167144", ISSN = "1083-4419", notes = "Also known as \cite{6029340}", } @InProceedings{Bhowan:2013:GECCOcomp, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang", title = "Comparing ensemble learning approaches in genetic programming for classification with unbalanced data", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "135--136", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2464643", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "This paper compares three approaches to evolving ensembles in Genetic Programming (GP) for binary classification with unbalanced data. The first uses bagging with sampling, while the other two use Pareto-based multi-objective GP (MOGP) for the trade-off between the two (unequal) classes. In MOGP, two ways are compared to build the ensembles: using the evolved Pareto front alone, and using the whole evolved population of dominated and non-dominated individuals alike. Experiments on several benchmark (binary) unbalanced tasks find that smaller, more diverse ensembles chosen during ensemble selection perform best due to better generalisation, particularly when the combined knowledge of the whole evolved MOGP population forms the ensemble.", notes = "Also known as \cite{2464643} Distributed at GECCO-2013.", } @Article{Bhowan:2014:ieeeTEC, author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang and Xin Yao", title = "Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data", journal = "IEEE Transactions on Evolutionary Computation", year = "2014", volume = "18", number = "6", pages = "893--908", month = dec, keywords = "genetic algorithms, genetic programming", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2013.2293393", size = "16 pages", abstract = "Classification algorithms can suffer from performance degradation when the class distribution is unbalanced. This paper develops a two-step approach to evolving ensembles using genetic programming (GP) for unbalanced data. The first step uses multi-objective (MO) GP to evolve a Pareto approximated front of GP classifiers to form the ensemble by trading-off the minority and the majority class against each other during learning. The MO component alleviates the reliance on sampling to artificially re-balance the data. The second step, which is the focus this paper, proposes a novel ensemble selection approach using GP to automatically find/choose the best individuals for the ensemble. This new GP approach combines multiple Pareto-approximated front members into a single composite genetic program solution to represent the (optimised) ensemble. This ensemble representation has two main advantages/novelties over traditional genetic algorithm (GA) approaches. Firstly, by limiting the depth of the composite solution trees, we use selection pressure during evolution to find small highly-cooperative groups of individuals for the ensemble. This means that ensemble sizes are not fixed a priori (as in GA), but vary depending on the strength of the base learners. Secondly, we compare different function set operators in the composite solution trees to explore new ways to aggregate the member outputs and thus, control how the ensemble computes its output. We show that the proposed GP approach evolves smaller, more diverse ensembles compared to an established ensemble selection algorithm, while still performing as well as, or better than the established approach. The evolved GP ensembles also perform well compared to other bagging and boosting approaches, particularly on tasks with high levels of class imbalance.", notes = "Also known as \cite{6677603}", } @InProceedings{Bhowan:2015:EuroGP, author = "Urvesh Bhowan and D. J. McCloskey", title = "Genetic Programming for Feature Selection and Question-Answer Ranking in {IBM Watson}", booktitle = "18th European Conference on Genetic Programming", year = "2015", editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim", series = "LNCS", volume = "9025", publisher = "Springer", pages = "153--166", address = "Copenhagen", month = "8-10 " # apr, organisation = "EvoStar", keywords = "genetic algorithms, genetic programming, IBM Watson, Question answer ranking, Feature selection: Poster", isbn13 = "978-3-319-16500-4", DOI = "doi:10.1007/978-3-319-16501-1_13", abstract = "IBM Watson is an intelligent open-domain question answering system capable of finding correct answers to natural language questions in real-time. Watson uses machine learning over a large heterogeneous feature set derived from many distinct natural language processing algorithms to identify correct answers. This paper develops a Genetic Programming (GP) approach for feature selection in Watson by evolving ranking functions to order candidate answers generated in Watson. We leverage GP automatic feature selection mechanisms to identify Watson key features through the learning process. Our experiments show that GP can evolve relatively simple ranking functions that use much fewer features from the original Watson feature set to achieve comparable performances to Watson. This methodology can aid Watson implementers to better identify key components in an otherwise large and complex system for development, troubleshooting, and/or customer or domain-specific enhancements.", notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015", } @InProceedings{Bi:2017:IVCNZ, author = "Ying Bi and Mengjie Zhang and Bing Xue", booktitle = "2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)", title = "An automatic region detection and processing approach in genetic programming for binary image classification", year = "2017", abstract = "In image classification, region detection is an effective approach to reducing the dimensionality of the image data but requires human intervention. Genetic Programming (GP) as an evolutionary computation technique can automatically identify important regions, and conduct feature extraction, feature construction and classification simultaneously. In this paper, an automatic region detection and processing approach in GP (GP-RDP) method is proposed for image classification. This approach is able to evolve important image operators to deal with detected regions for facilitating feature extraction and construction. To evaluate the performance of the proposed method, five recent GP methods and seven non-GP methods based on three types of image features are used for comparison on four image data sets. The results reveal that the proposed method can achieve comparable performance on easy data sets and significantly better performance on difficult data sets than the other comparable methods. To further demonstrate the interpretability and understandability of the proposed method, two evolved programs are analysed. The analysis shows the good interpretability of the GP-RDP method and proves that the GP-RDP method is able to identify prominent regions, evolve effective image operators to process these regions, extract and construct good features for efficient image classification.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IVCNZ.2017.8402469", ISSN = "2151-2205", month = dec, notes = "Also known as \cite{8402469}", } @InProceedings{Bi:2018:evoApplications, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming", booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoIASP 2018", year = "2018", editor = "Stefano Cagnoni and Mengjie Zhang", series = "LNCS", volume = "10784", publisher = "Springer", pages = "421--438", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Image classification, Feature extraction, Image analysis", isbn13 = "978-3-319-77537-1", DOI = "doi:10.1007/978-3-319-77538-8_29", abstract = "Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.", notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php", } @InProceedings{Bi:2018:CEC, author = "Ying Bi and Mengjie Zhang and Bing Xue", title = "Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification", booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)", year = "2018", editor = "Marley Vellasco", address = "Rio de Janeiro, Brazil", month = "8-13 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", URL = "https://openaccess.wgtn.ac.nz/articles/conference_contribution/Genetic_Programming_for_Automatic_Global_and_Local_Feature_Extraction_to_Image_Classification/13884998", DOI = "doi:10.1109/CEC.2018.8477911", size = "8 pages", abstract = "Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.", notes = "WCCI2018", } @InProceedings{bi:2018:AJCAI, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "A Gaussian {Filter-Based} Feature Learning Approach Using Genetic Programming to Image Classification", booktitle = "Australasian Joint Conference on Artificial Intelligence", year = "2018", editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li", volume = "11320", series = "LNCS", pages = "251--257", address = "Wellington, New Zealand", month = dec # " 11-14", publisher = "Springer", keywords = "genetic algorithms, genetic programming, ANN, Feature learning, Image classification, Gaussian filter, Evolutionary computation, Feature extraction", isbn13 = "978-3-030-03990-5", URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_25", DOI = "doi:10.1007/978-3-030-03991-2_25", abstract = "To learn image features automatically from the problems being tackled is more effective for classification. However, it is very difficult due to image variations and the high dimensionality of image data. This paper proposes a new feature learning approach based on Gaussian filters and genetic programming (GauGP) for image classification. Genetic programming (GP) is a well-known evolutionary learning technique and has been applied to many visual tasks, showing good learning ability and interpretability. In the proposed GauGP method, a new program structure, a new function set and a new terminal set are developed, which allow it to detect small regions from the input image and to learn discriminative features using Gaussian filters for image classification. The performance of GauGP is examined on six different data sets of varying difficulty and compared with four GP methods, eight traditional approaches and convolutional neural networks. The experimental results show GauGP achieves significantly better or similar performance in most cases.", } @Article{Bi:2018:JZU, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "A Survey on Genetic Programming to Image Analysis", journal = "Journal of Zhengzhou University (Engineering Science)", year = "2018", volume = "39", number = "6", pages = "3--13", note = "In Chinese", keywords = "genetic algorithms, genetic programming, image analysis, evolutionary computation, feature extraction, image classification", URL = "https://yingbi92.github.io/homepage/2020/%E9%81%97%E4%BC%A0%E8%A7%84%E5%88%92%E5%9C%A8%E5%9B%BE%E5%83%8F%E5%88%86%E6%9E%90%E4%B8%8A%E7%9A%84%E5%BA%94%E7%94%A8%E7%BB%BC%E8%BF%B0%E2%80%94v4.pdf", URL = "http://gxb.zzu.edu.cn/oa/darticle.aspx?type=view&id=201802014", broken_doi = "doi:10.13705/j.issn.1671-6833.2013.06.01", size = "11 pages", abstract = "As an evolutionary computation (EC) technique, Genetic programming (GP) has been widely applied to image analysis in recent decades. However, there was no comprehensive and systematic literature review in this area. To provide guidelines for the state-of-the-art research, this paper presented a survey of the literature in recent years on GP for image analysis, including feature extraction, image classification, edge detection, and image segmentation. In addition, this paper summarised the current issues and challenges, such as computationally expensive, generalisation ability and transfer learning, on GP for image analysis, and pointd out promising research directions for future work", } @InProceedings{Bi:2019:CEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification", booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019", year = "2019", editor = "Carlos A. Coello Coello", pages = "3197--3204", address = "Wellington, New Zealand", month = "10-13 " # jun, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, ANN", isbn13 = "978-1-7281-2152-6", DOI = "doi:10.1109/CEC.2019.8790151", size = "8 pages", abstract = "Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (ANN). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP ac", notes = "also known as \cite{8790151} IEEE Catalog Number: CFP19ICE-ART", } @InProceedings{Bi:2019:GECCO, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "An automated ensemble learning framework using genetic programming for image classification", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "365--373", address = "Prague, Czech Republic", DOI = "doi:10.1145/3321707.3321750", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ensemble Learning, Image Classification, Feature Learning, Machine Learning, Computer Vision", size = "9 pages", abstract = "An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner. Genetic programming (GP) has been applied to construct ensembles using different strategies such as bagging and boosting. However, no GP-based ensemble methods focus on dealing with image classification, which is a challenging task in computer vision and machine learning. This paper proposes an automated ensemble learning framework using GP (EGP) for image classification. The new method integrates feature learning, classification function selection, classifier training, and combination into a single program tree. To achieve this, a novel program structure, a new function set and a new terminal set are developed in EGP. The performance of EGP is examined on nine different image classification data sets of varying difficulty and compared with a large number of commonly used methods including recently published methods. The results demonstrate that EGP achieves better performance than most competitive methods. Further analysis reveals that EGP evolves good ensembles simultaneously balancing diversity and accuracy. To the best of our knowledge, this study is the first work using GP to automatically generate ensembles for image classification.", notes = "Also known as \cite{3321750} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Bi:2020:CIM, author = "Ying Bi and Bing Xue and Mengjie Zhang", journal = "IEEE Computational Intelligence Magazine", title = "An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier]", year = "2020", volume = "15", number = "2", pages = "65--77", abstract = "Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/MCI.2020.2976186", ISSN = "1556-6048", month = may, notes = "Also known as \cite{9067779}", } @InProceedings{Bi:2020:GECCOcomp, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Automatically Extracting Features for Face Classification Using Multi-Objective Genetic Programming", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", isbn13 = "9781450371278", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://doi.org/10.1145/3377929.3389989", DOI = "doi:10.1145/3377929.3389989", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "117--118", size = "2 pages", keywords = "genetic algorithms, genetic programming, feature extraction, evolutionary multi-objective, face classification", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets.", notes = "Also known as \cite{10.1145/3377929.3389989} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Bi:2020:CEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming-Based Feature Learning for Facial Expression Classification", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24102", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185491", abstract = "Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.", notes = "https://wcci2020.org/ Victoria University of Wellington, New Zealand. Also known as \cite{9185491}", } @InProceedings{Bi:2020:PPSN, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming", booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part I", year = "2020", editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann", volume = "12269", series = "LNCS", pages = "3--18", address = "Leiden, Holland", month = "7-9 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, EvoDF, Evolutionary deep learning, Deep forest, Image classification, Feature extraction", isbn13 = "978-3-030-58111-4", URL = "https://openaccess.wgtn.ac.nz/articles/chapter/Evolving_deep_forest_with_automatic_feature_extraction_for_image_classification_using_genetic_programming/13158329", DOI = "doi:10.1007/978-3-030-58112-1_1", size = "14 pages", abstract = "Deep forest is an alternative to deep neural networks to use multiple layers of random forests without back-propagation for solving various problems. In this study, we propose a genetic programming-based approach to automatically and simultaneously evolving effective structures of deep forest connections and extracting informative features for image classification. First, in the new approach we define two types of modules: forest modules and feature extraction modules. Second, an encoding strategy is developed to integrate forest modules and feature extraction modules into a tree and the search strategy is introduced to search for the best solution. With these designs, the proposed approach can automatically extract image features and find forests with effective structures simultaneously for image classification. The parameters in the forest can be dynamically determined during the learning process of the new approach. The results show that the new approach can achieve better performance on the datasets having a small number of training instances and competitive performance on the datasets having a large number of training instances. The analysis of evolved solutions shows that the proposed approach uses a smaller number of random forests over the deep forest method.", notes = "tried ResNet, AlexNet, EvoDF, CNN, gcForest (8 forests), Random Forest, SVM (linear kernel), kNN on ORL, Extend Yale B, SCENE, KTH, MNIST and CIFAR-10. 'Fig 6 The solution found by EvoDF on the MNIST dataset' depth 8. PPSN2020", } @PhdThesis{YingBi_thesis, author = "Ying Bi", title = "Genetic Programming for Feature Learning in Image Classification", school = "Computer Science, Victoria University of Wellington", year = "2020", address = "New Zealand", keywords = "genetic algorithms, genetic programming, ANN, CNN, FLGP, IEGP, ensembles, STGP", URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/34714144", URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_for_Feature_Learning_in_Image_Classification/19529515", DOI = "doi:10.26686/wgtn.19529515", size = "292 pages", abstract = "Image classification is an important and fundamental task in computer vision and machine learning. The task is to classify images into one of some predefined groups based on the content in the images. However, image classification is a challenging task due to high variations across images, such as illumination, viewpoint, scale variations, deformation, and occlusion. To effectively solve image classification, it is necessary to extract or learn a set of meaningful features from raw pixels or images. The effectiveness of these features significantly affects classification performance. Feature learning aims to automatically learn effective features from images for classification. However, feature learning is difficult due to the high variations of images and the large search space. Genetic Programming (GP) as an Evolutionary Computation (EC) technique is known for its powerful global search ability and high interpretability of the evolved solutions. Compared with other EC methods, GP has a flexible representation of variable length and can search the solution space without any assumptions on the solution structure. The potential of GP in feature learning for image classification has not been comprehensively investigated due to the use of simple representations, e.g., functions and program structures. The overall goal of this thesis is to further investigate and explore the potential of GP for image classification by developing a new GP-based approach with a new representation to automatically learning effective features for different types of image classification tasks. Firstly, this thesis proposes a new GP based approach with image descriptors to learning global and/or local features for image classification by developing a new program structure, a new function set, a new terminal set, and a new fitness function. These new designs allow GP to detect small regions from the relatively large input image, extract features using image descriptors from the detected regions or the input image, and combine the extracted features for classification. The results show that the new approach significantly out performs five GP-based methods, eight traditional methods, and three convolutional neural network methods in almost all the comparisons on eight different datasets. Secondly, this thesis proposes a new GP-based approach with a flexible program structure and image-related operators for feature learning in image classification. The new approach learns effective features transformed by multiple layers, i.e., a filtering layer, a pooling layer, a feature extraction layer, and a feature concatenation layer, in a flexible way. The results show that the new approach achieves better performance than a large number of effective methods on 12 benchmark datasets. The solutions and features learned by the new approach provide high interpretability. Thirdly, this thesis proposes the first GP-based approach to automatically and simultaneously learning features and evolving ensembles for image classification. The new approach can learn high-level features through multiple transformations, select effective classification algorithms and optimise the parameters for these classification algorithms to build effective ensembles. The new approach outperforms a large number of benchmark methods on 12 different image classification datasets. Finally, this thesis proposes a multi-population GP-based approach with knowledge transfer and ensembles to improving both the generalisation performance and computational efficiency of GP-based feature learning algorithms for image classification. The new approach can achieve better generalisation performance and computational efficiency than baseline GP-based feature learning method. The new approach can achieve better performance on 11 datasets than a large number of benchmark methods, including many neural network-based methods.", notes = "Also known as \cite{Bi2022} supervisors: Mengjie Zhang and Bing Xue", } @Article{Bi:2020:TEVC, author = "Ying Bi and Bing Xue and Mengjie Zhang", journal = "IEEE Transactions on Evolutionary Computation", title = "Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification", year = "2021", volume = "25", number = "1", pages = "87--101", month = feb, keywords = "genetic algorithms, genetic programming, Feature Learning, Image Classification, Representation, Evolutionary Computation", ISSN = "1941-0026", DOI = "doi:10.1109/TEVC.2020.3002229", size = "14 pages", abstract = "Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.", notes = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand Also known as \cite{9117044}", } @Article{Bi:2020:CYB, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2021", volume = "51", number = "4", pages = "1769--1783", month = apr, keywords = "genetic algorithms, genetic programming, Ensemble learning, feature learning, image classification, representation", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2020.2964566", size = "15 pages", abstract = "Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity.", notes = "Also known as \cite{8976239}", } @Book{bi2021gpimage, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming for Image Classification", publisher = "Springer Nature", year = "2021", volume = "24", series = "Adaptation, Learning, and Optimization book series", month = feb # " 9", note = "An Automated Approach to Feature Learning", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Feature Learning, Image Classification, Computer Vision, Machine Learning, Feature Extraction, Feature Selection, Feature Construction, Model Interpretability", isbn13 = "978-3030659264", ISSN = "1867-4534", URL = "https://link.springer.com/book/10.1007/978-3-030-65927-1", URL = "https://www.amazon.com/Genetic-Programming-Image-Classification-Optimization-dp-3030659267/dp/3030659267", code_url = "https://github.com/YingBi92/BookCode", DOI = "doi:10.1007/978-3-030-65927-1", size = "286 pages", abstract = "This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation. Front Matter Pages i-xxviii. Introduction Pages 1-10. Computer Vision and Machine Learning Pages 11-48. Evolutionary Computation and Genetic Programming Pages 49-74. Multi-layer Representation for Binary Image Classification Pages 75-95. Evolutionary Deep Learning Using GP with Convolution Operators Pages 97-115. GP with Image Descriptors for Learning Global and Local Features Pages 117-143. GP with Image-Related Operators for Feature Learning Pages 145-177. GP for Simultaneous Feature Learning and Ensemble Learning Pages 179-205. Random Forest-Assisted GP for Feature Learning Pages 207-226. Conclusions and Future Directions Pages 227-237. Back Matter Pages 239-258. Evolutionary Computation Research Group, School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand", notes = "Reviewed in \cite{Zafra:2022:GPEM}", } @Article{Bi:2021:ASC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Multi-objective genetic programming for feature learning in face recognition", journal = "Applied Soft Computing", year = "2021", volume = "103", pages = "107152", month = may, keywords = "genetic algorithms, genetic programming, Multi-objective optimisation, Evolutionary computation, Feature learning, Face recognition", ISSN = "1568-4946", URL = "https://yingbi92.github.io/homepage/2021/MOGP.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S1568494621000752", DOI = "doi:10.1016/j.asoc.2021.107152", size = "14 pages", abstract = "Face recognition is a challenging task due to high variations of pose, expression, ageing, and illumination. As an effective approach to face recognition, feature learning can be formulated as a multi-objective optimisation task of maximising classification accuracy and minimising the number of learned features. However, most of the existing algorithms focus on improving classification accuracy without considering the number of learned features. In this paper, we propose new multi-objective genetic programming (GP) algorithms for feature learning in face recognition. To achieve effective face feature learning, a new individual representation is developed to allow GP to select informative regions from the input image, extract features using various descriptors, and combine the extracted features for classification. Then two new multi-objective genetic programming (GP) algorithms, one with the idea of non-dominated sorting (NSGPFL) and the other with the idea of Strength Pareto (SPGPFL), are proposed to simultaneously optimise these two objectives. NSGPFL and SPGPFL are compared with a single-objective GP for feature learning (GPFL), a single-objective GP for weighting two objectives (GPFLW), and a large number of baseline methods. The experimental results show the effectiveness of the NSGPFL and SPGPFL algorithms by achieving better or comparable classification performance and learning a small number of features.", notes = "MOGP.pdf is a 40 page preprint", } @Article{Bi:TEVC2, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "A Divide-and-Conquer Genetic Programming Algorithm with Ensembles for Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "6", pages = "1148--1162", month = dec, keywords = "genetic algorithms, genetic programming, Feature Learning, Knowledge Transfer, Ensemble Learning, Divide-and-Conquer, Image Classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3082112", size = "15 pages", abstract = "Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this paper develops a divide-and-conquer GP algorithm with knowledge transfer and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divideand-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new knowledge transfer method is proposed to improve GP learning performance. A new fitness function based on log-loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-theart algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and knowledge transfer in the proposed approach.", notes = "also known as \cite{9437306}", } @Article{Bi:2022:InformationSciences, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Using a Small Number of Training Instances in Genetic Programming for Face Image Classification", journal = "Information Sciences", year = "2022", volume = "593", pages = "488--504", month = may, keywords = "genetic algorithms, genetic programming, MOGP, Image classification, Fitness measure, Small data, Evolutionary computation", ISSN = "0020-0255", URL = "https://www.sciencedirect.com/science/article/abs/pii/S0020025522000871", DOI = "doi:10.1016/j.ins.2022.01.055", abstract = "Classifying faces is a difficult task due to image variations in illumination, occlusion, pose, expression, etc. Typically, it is challenging to build a generalised classifier when the training data is small, which can result in poor generalisation. This paper proposes a new approach for the classification of face images based on multi-objective genetic programming (MOGP). In MOGP, image descriptors that extract effective features are automatically evolved by optimising two different objectives at the same time: the accuracy and the distance measure. The distance measure is a new measure intended to enhance generalisation of learned features and/or classifiers. The performance of MOGP is evaluated on eight face datasets. The results show that MOGP significantly outperforms 17 competitive methods.", notes = "also known as \cite{BI2022488}", } @Article{Learning_and_Sharing_A_Multitask_Genetic_Programming_Approach_to_Image_Feature_Learning, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "2", pages = "218--232", month = apr, note = "Special Issue on Multitask Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Multitask Learning, Knowledge Sharing, Feature Learning, Image Classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3097043", size = "15 pages", abstract = "Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this paper develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. Experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.", notes = "also known as \cite{9484082}", } @Article{Dual-Tree_Genetic_Programming_for_Few-Shot_Image_Classification, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Dual-Tree Genetic Programming for Few-Shot Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2022", volume = "26", number = "3", pages = "555--569", month = jun, keywords = "genetic algorithms, genetic programming, Representation, Fitness Evaluation, Few-Shot Learning, Image Classification", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2021.3100576", size = "15 pages", abstract = "Few-shot image classification is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalisation performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This paper proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for few-shot image classification. The dual-tree representation allows the proposed approach to have better search ability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalisation performance. The proposed approach can deal with different types of few-shot image classification tasks with various numbers of classes and different", notes = "also known as \cite{9499117} School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand", } @Article{Ying_Bi:cybernetics1, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2023", volume = "53", number = "2", pages = "1118--1132", month = feb, keywords = "genetic algorithms, genetic programming, evolutionary computation, EC, feature learning, instance selection, surrogate", ISSN = "2168-2275", DOI = "doi:10.1109/TCYB.2021.3105696", size = "15 pages", abstract = "Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.", notes = "Also known as \cite{9526355}", } @Article{Ying_Bi:Cybernetics2, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2022", volume = "52", number = "8", pages = "8272--8285", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/TCYB.2021.3049778", ISSN = "2168-2275", abstract = "Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise.", notes = "Also known as \cite{9345467}", } @Article{Ying_Bi:Cybernetics3, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification", journal = "IEEE Transactions on Cybernetics", year = "2023", volume = "53", number = "5", pages = "3007--3020", month = may, keywords = "genetic algorithms, genetic programming, Evolutionary computation (EC), feature learning, GP, image classification, multiobjective optimisation, multitask learning", ISSN = "2168-2267", DOI = "doi:10.1109/TCYB.2022.3174519", size = "14 pages", abstract = "Feature learning is a promising approach to image classification. However, it is difficult due to high image variations. When the training data are small, it becomes even more challenging, due to the risk of overfitting. Multitask feature learning has shown the potential for improving generalization. However, existing methods are not effective for handling the case that multiple tasks are partially conflicting. Therefore, for the first time, this article proposes to solve a multitask feature learning problem as a multiobjective optimization problem by developing a genetic programming approach with a new representation to image classification. In the new approach, all the tasks share the same solution space and each solution is evaluated on multiple tasks so that the objectives of all the tasks can be optimized simultaneously using a single population. To learn effective features, a new and compact program representation is developed to allow the new approach to evolving solutions shared across tasks. The new approach can automatically find a diverse set of nondominated solutions that achieve good tradeoffs between different tasks. To further reduce the risk of overfitting, an ensemble is created by selecting non-dominated solutions to solve each image classification task. The results show that the new approach significantly outperforms a large number of benchmark methods on six problems consisting of 15 image classification datasets of varying difficulty. Further analysis shows that these new designs are effective for improving the performance. The detailed analysis clearly reveals the benefits of solving multitask feature learning as multi-objective optimisation in improving the generalisation.", notes = "also known as \cite{9781346}", } @Article{Bi:JRSNZ, author = "Ying Bi and Bing Xue and Dana Briscoe and Ross Vennell and Mengjie Zhang", title = "A new artificial intelligent approach to buoy detection for mussel farming", journal = "Journal of the Royal Society of New Zealand", year = "2023", volume = "53", number = "1", pages = "27--51", keywords = "genetic algorithms, genetic programming, Artificial intelligence, computer vision, aquaculture, evolutionary learning, object detection, deep learning, ANN", publisher = "Taylor \& Francis", ISSN = "0303-6758", DOI = "doi:10.1080/03036758.2022.2090966", size = "25 pages", abstract = "Aquaculture is an important industry in New Zealand (NZ). Mussel farmers often manually check the state of the buoys that are required to support the crop, which is labour-intensive. Artificial intelligence (AI) can provide automatic and intelligent solutions to many problems but has seldom been applied to mussel farming. In this paper, a new AI-based approach is developed to automatically detect buoys from mussel farm images taken from a farm in the South Island of NZ. The overall approach consists of four steps, i.e. data collection and preprocessing, image segmentation, keypoint detection and feature extraction, and classification. A convolutional neural network (CNN) method is applied to perform image segmentation. A new genetic programming (GP) method with a new representation, a new function set and a new terminal set is developed to automatically evolve descriptors for extracting features from key points. The new approach is applied to seven subsets and one full dataset containing images of buoys over different backgrounds and compared to three baseline methods. The new approach achieves better performance than the compared methods. Further analysis of the parameters and the evolved solutions provides more insights into the performance of the new approach to buoy detection.", notes = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand", } @Article{Ying_Bi:ieeeTEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "2", pages = "307--322", month = apr, keywords = "genetic algorithms, genetic programming, ANN, Evolutionary Deep Learning, Image Classification, Small Data, Evolutionary Computation, Deep Learning", ISSN = "1089-778X", URL = "https://ieeexplore.ieee.org/abstract/document/9919314/", DOI = "doi:10.1109/TEVC.2022.3214503", size = "15 pages", abstract = "Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffers from limitations such as poor interpretability. We propose a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colo", notes = "Also known as \cite{9919314}", } @Article{Ying_Bi:ieeeTEC2, author = "Ying Bi and Bing Xue and Pablo Mesejo and Stefano Cagnoni and Mengjie Zhang", title = "A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "1", pages = "5--25", month = feb, keywords = "genetic algorithms, genetic programming, evolutionary Computation, Image Analysis, Computer Vision, Pattern Recognition, Image Processing, Artificial Intelligence", ISSN = "1089-778X", URL = "https://arxiv.org/abs/2209.06399v1", URL = "https://ieeexplore.ieee.org/abstract/document/9943992/", DOI = "doi:10.1109/TEVC.2022.3220747", size = "21 pages", abstract = "Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research.", notes = "also known as \cite{9943992} School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China", } @InProceedings{Bi:2023:CEC, author = "Ying Bi and Bing Xue and Mengjie Zhang", title = "Evolutionary Deep-Learning for Image Classification: A Genetic Programming Approach", booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)", year = "2023", editor = "Gui DeSouza and Gary Yen", address = "Chicago, USA", month = "1-5 " # jul, note = "Tutorial", keywords = "genetic algorithms, genetic programming", URL = "https://2023.ieee-cec.org/program-html/", URL = "https://2023.ieee-cec.org/wp-content/uploads/sites/438/17EDL_proposal_YingBi.pdf", size = "5 pages", notes = "2 July 2023 2:00pm-3:50pm CEC2023 https://2023.ieee-cec.org/program-html/", } @Article{BiYing:ieeeTEC, author = "Ying Bi and Jing Liang and Bing Xue and Mengjie Zhang", title = "A Genetic Programming Approach with Building Block Evolving and Reusing to Image Classification", journal = "IEEE Transactions on Evolutionary Computation", year = "2024", volume = "28", number = "5", pages = "1366--1380", month = oct, keywords = "genetic algorithms, genetic programming, Image classification, Feature extraction, Task analysis, Training data, Training, Sociology, Random forests, Ensemble learning, evolutionary computation (EC), feature extraction", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2023.3284712", notes = "also known as \cite{10147342}", } @InProceedings{Bian:2016:ATS, author = "Song Bian and Michihiro Shintani and Zheng Wang and Masayuki Hiromoto and Anupam Chattopadhyay and Takashi Sato", booktitle = "2016 IEEE 25th Asian Test Symposium (ATS)", title = "Runtime NBTI Mitigation for Processor Lifespan Extension via Selective Node Control", year = "2016", pages = "234--239", abstract = "Negative bias temperature instability (NBTI) has become one of the major reliability concerns for nanoscale CMOS technology. The NBTI effect degrades pMOS transistors by stressing them with negatively biased voltage, while the transistors heal themselves as the negative bias is removed. In this paper, we propose a cross-layer mitigation technique for NBTI-induced timing degradation in processors. The NOP (No Operation) instruction is replaced by a custom NOP instruction for healing purpose. Cells that are likely to be stressed under negative bias are classified and their upstream cell will be replaced by the internal node control (INC) logics. Upon encountering a custom NOP instruction, the INC logics will force the NBTI-stressed cell to be in its healing mode. The optimal INC logic insertion through genetic programming approach achieves much greater delay mitigation of 44.3percent than prior works in a 10-year span with less than 4percent of power and negligible area overhead.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ATS.2016.31", month = nov, notes = "Also known as \cite{7796119}", } @InProceedings{bian:2021:apr_icse, author = "Zhiqiang Bian and Justyna Petke and Aymeric Blot", title = "Refining Fitness Functions for Search-Based Program Repair", booktitle = "APR @ ICSE 2021", year = "2021", month = "1 " # jun, editor = "Sergey Mechtaev and Shin Hwei Tan and Martin Monperrus and Lingming Zhang", publisher = "IEEE", address = "internet", pages = "1--8", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Software Engineering, SBSE, Program Repair, Fitness Function, GenProg, ARJAe, 2Phase, Gin, EvoSuite, QuixBugs", isbn13 = "978-1-6654-4473-6", code_url = "https://github.com/SOLAR-group/apr2021artefact", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/bian_apr-icse_2021.pdf", video_url = "https://www.youtube.com/watch?v=2CbHQMhkMTU", DOI = "doi:10.1109/APR52552.2021.00008", size = "8 pages", abstract = "Debugging is a time-consuming task for software engineers. Automated Program Repair (APR) has proved successful in automatically fixing bugs for many real-world applications. Search-based APR generates program variants that are then evaluated on the test suite of the original program, using a fitness function. In the vast majority of search based APR work only the Boolean test case result is taken into account when evaluating the fitness of a program variant. We pose that more fine-grained fitness functions could lead to a more diverse fitness landscape,and thus provide better guidance for the APR search algorithms. We thus present 2Phase, a fitness function that also incorporates the output of test case failures, and compare it with ARJAe,that shares the same principles, and the standard fitness, that only takes the Boolean test case result into consideration. We conduct the comparison on 16 buggy programs from the QuixBugs benchmark using the Gin genetic improvement framework. The results show no significant difference in the performance of all three fitness functions considered. However, Gin was able to find 8 correct fixes, more than any of the APR tools in the recent QuixBugs study.", notes = "'no significant difference between effectiveness and efficiency of the three fitness functions on our dataset' cf video 2CbHQMhkMTU 11:17 A: due to QuixBugs, may be different results on different benchmarks. A2 (red) Future work more benchmarks and especially bigger faulty software, with possibly larger search spaces. More formal analysis of search space, cf search landscape of SBSE testing. Q2: fitness diversity. A: different patches (of same example bug) should have different (numerical) fitness values. Low diversity means different patches have the same fitness. Without fitness diversity it is hard to guide the search for better patches. Q3: would other types of diversity be better? A1(off screen, voice only): tried new operators. a2: Future work may be need new measures of diversity. A3(grey): We concentrate upon fitness diversity between patches to guide search. a2: Smooth the landscape so EC algorithm is better guided (to acceptable bug repair) 'test-suite adequate patches for 11 programs, with 8 being correct fixes (more than any of the APR tools in the recent QuixBugs study)' checkpoints around numerical values in the source code as part of fitness distance calculation. Internal test oracles? But not used? patch over fitting. EvoSuite hold out tests. 'The mutation operator either appends a random new edit to a patch or removes one from a non-empty patch. The crossover operator combines two parent solutions by concatenating, in both orders, the two sequences of edits before each edit is removed with 0.5 probability to create the two children patches.' University College London, London, United Kingdom https://program-repair.org/workshop-2021/", } @Misc{oai:arXiv.org:1505.02921, author = "Simone Bianco and Gianluigi Ciocca and Raimondo Schettini", title = "How Far Can You Get By Combining Change Detection Algorithms?", year = "2015", month = may # "~12", note = "Comment: Submitted to IEEE Transactions on Image Processing", bibsource = "OAI-PMH server at export.arxiv.org", oai = "oai:arXiv.org:1505.02921", keywords = "genetic algorithms, genetic programming, computer science - computer vision and pattern recognition", URL = "http://arxiv.org/abs/1505.02921", abstract = "In this paper we investigate if simple change detection algorithms can be combined and used to create a more robust change detection algorithm by leveraging their individual peculiarities. We use Genetic Programming to combine the outputs (i.e. binary masks) of the detection algorithms with unary, binary and n-ary functions performing both masks' combination and post-processing. Genetic Programming allows us to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations. Using different experimental settings, we created two algorithms that we named IUTIS-1 and IUTIS-2 (In Unity There Is Strength). These algorithms are compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the ChandeDetection.net (CDNET 2014) challenge. Results demonstrate that starting from simple algorithms we can achieve comparable results of more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications. Moreover, when our framework is applied to more complex algorithms, the resulting IUTIS-3 outperforms all the 33 state-of-the-art algorithms considered.", notes = "see \cite{Bianco:ieeeTEC}", } @Article{Bianco:ieeeTEC, author = "Simone Bianco and Gianluigi Ciocca and Raimondo Schettini", title = "Combination of Video Change Detection Algorithms by Genetic Programming", journal = "IEEE Transactions on Evolutionary Computation", year = "2017", volume = "21", number = "6", pages = "914--928", month = dec, keywords = "genetic algorithms, genetic programming, Change detection, algorithm combining and selection, CDNET", ISSN = "1089-778X", URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898824", DOI = "doi:10.1109/TEVC.2017.2694160", size = "15 pages", abstract = "Within the field of Computer Vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analysing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited Genetic Programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms combination and post-processing operations are achieved with unary, binary and n-ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed IUTIS (In Unity There Is Strength). These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the Change Detection. net (CDNET 2014) challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies. The performance of our algorithm are significantly different from those of the other state-of-the-art algorithms. This fact is supported by the statistical significance analysis conducted with the Friedman Test and Wilcoxon Rank Sum post-hoc tests.", notes = "also known as \cite{7898824} See also \cite{oai:arXiv.org:1505.02921}", } @Article{BIANCO:2020:IF, author = "Simone Bianco and Marco Buzzelli and Gianluigi Ciocca and Raimondo Schettini", title = "Neural architecture search for image saliency fusion", journal = "Information Fusion", volume = "57", pages = "89--101", year = "2020", ISSN = "1566-2535", DOI = "doi:10.1016/j.inffus.2019.12.007", URL = "http://www.sciencedirect.com/science/article/pii/S1566253519302374", keywords = "genetic algorithms, genetic programming, Saliency fusion, Evolutionary algorithms, Neural architecture search", abstract = "Saliency detection methods proposed in the literature exploit different rationales, visual clues, and assumptions, but there is no single best saliency detection algorithm that is able to achieve good results on all the different benchmark datasets. In this paper we show that fusing different saliency detection algorithms together by exploiting neural network architectures makes it possible to obtain better results. Designing the best architecture for a given task is still an open problem since the existing techniques have some limits with respect to the problem formulation, to the search space, and require very high computational resources. To overcome these problems, in this paper we propose a three-step fusion approach. In the first step, genetic programming techniques are exploited to combine the outputs of existing saliency algorithms using a set of provided operations. Having a discrete search space allows us a fast generation of the candidate solutions. In the second step, the obtained solutions are converted into backbone Convolutional Neural Networks (CNNs) where operations are all implemented with differentiable functions, allowing an efficient optimization of the corresponding parameters (in a continuous space) by backpropagation. In the last step, to enrich the expressiveness of the initial architectures, the networks are further extended with additional operations on intermediate levels of the processing that are once again efficiently optimized through backpropagation. Extensive experimental evaluations show that the proposed saliency fusion approach outperforms the state-of-the-art on the MSRAB dataset and it is able to generalize to unseen data of different benchmark datasets", } @InProceedings{DBLP:conf/ijcci/BiauWCL21, author = "Julien Biau and Dennis Wilson and Sylvain Cussat-Blanc and Herve Luga", editor = "Thomas B{\"{a}}ck and Christian Wagner and Jonathan M. Garibaldi and H. K. Lam and Marie Cottrell and Juan Juli{\'{a}}n Merelo and Kevin Warwick", title = "Improving Image Filters with Cartesian Genetic Programming", booktitle = "Proceedings of the 13th International Joint Conference on Computational Intelligence, {IJCCI} 2021, Online Streaming, October 25-27, 2021", pages = "17--27", publisher = "{SCITEPRESS}", year = "2021", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "https://doi.org/10.5220/0010640000003063", DOI = "doi:10.5220/0010640000003063", timestamp = "Fri, 19 Nov 2021 13:53:58 +0100", biburl = "https://dblp.org/rec/conf/ijcci/BiauWCL21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @InProceedings{Biau:2024:evoapplications, author = "Julien Biau and Sylvain Cussat-Blanc and Herve Luga", title = "Improving Image Filter Efficiency: A Multi-objective Genetic Algorithm Approach to Optimize Computing Efficiency", booktitle = "27th International Conference, EvoApplications 2024", year = "2024", editor = "Stephen Smith and Joao Correia and Christian Cintrano", series = "LNCS", volume = "14634", publisher = "Springer", address = "Aberystwyth", month = "3-5 " # apr, pages = "19--34", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Cartesian Genetic Programming, CGP-IP-GI, MOGA, NSGA-II, Island model, Python, Multi-Objective, Image processing, OpenCV, Real Time Applications, Embedded Systems", isbn13 = "978-3-031-56851-0", URL = "https://rdcu.be/dDZHh", DOI = "doi:10.1007/978-3-031-56852-7_2", size = "16 pages", abstract = "For real-time applications in embedded systems, an efficient image filter is not defined solely by its accuracy but by the delicate balance it strikes between precision and computational cost. While one approach to manage an algorithm computing demands involves evaluating its complexity, an alternative strategy employs a multi-objective algorithm to optimize both precision and computational cost. we introduce a multi-objective adaptation of Cartesian Genetic Programming aimed at enhancing image filter performance. We refine the existing Cartesian Genetic Programming framework for image processing by integrating the elite Non-dominated Sorting Genetic Algorithm into the evolutionary process, thus enabling the generation of a set of Pareto front solutions that cater to multiple objectives. To assess the effectiveness of our framework, we conduct a study using a Urban Traffic dataset and compare our results with those obtained using the standard framework employing a mono-objective evolutionary strategy. Our findings reveal two key advantages of this adaptation. Firstly, it generates individuals with nearly identical precision in one objective while achieving a substantial enhancement in the other objective. Secondly, the use of the Pareto front during the evolution process expands the research space, yielding individuals with improved fitness.", notes = "INIA SAS, Toulouse, France http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024", } @InProceedings{Bickel:1989:tsrGA, author = "Authur S. Bickel and Riva Wenig Bickel", title = "Tree Structured Rules in Genetic Algorithms", booktitle = "Genetic Algorithms and their Applications: Proceedings of the second International Conference on Genetic Algorithms", year = "1987", editor = "John J. Grefenstette", pages = "77--81", address = "MIT, Cambridge, MA, USA", publisher_address = "Hillsdale, NJ, USA", month = "28-31 " # jul, publisher = "Lawrence Erlbaum Associates", keywords = "genetic algorithms, genetic programming", size = "5 pages", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga1987/Bickel_1989_tsrGA.pdf", URL = "http://gpbib.cs.ucl.ac.uk/icga/icga1987.pdf", bibtex_url = "http://dl.acm.org/citation.cfm?id=42523&CFID=53044018&CFTOKEN=44075976", abstract = "GA applied to variable length lists of tree structured production rules. Mutation applied within trees, eg > replaced by >=. Inversion applied by re-ordering rules, nb does change semantics of rules set because they are applied in order, not applied within trees. Crossover applied to lists NOT to contents of trees", } @InProceedings{Bidaud:2002:romansy, author = "Philippe Bidaud and Frederic Chapelle and G. Dumont", title = "Evolutionary optimization of mechanical and control design. Application to active endoscopes", booktitle = "Theory and Practice of Robots and Manipulators: Proceedings of the Fourteenth Cism-IFToMM Symposium", organization = "CISM - IFToMM", address = "Udine, Italy", month = jul, pages = "317--330", editor = "Giovanni Bianchi and Jean-Claude Guinot and Cezary Rzymkowski", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", year = "2002", number = "14", series = "RoManSy", publisher_address = "Wien/New York", isbn13 = "978-3-211-83691-0", URL = "http://www.springer.com/physics/classical+continuum+physics/book/978-3-211-83691-0", notes = "Also known as \cite{2002ACTI53}. http://www.isir.upmc.fr/?op=view_profil&id=6&pageid=publi&lang=fr See also http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.285&rep=rep1&type=pdf ROMANSY 14 Broken Dec 2012 http://www.meil.pw.edu.pl/romansy2002/html/romansy14.htm CISM International Centre for Mechanical Sciences, Number 438", } @InProceedings{Bidlo:2013:CEC, article_id = "1416", author = "Michal Bidlo and Zdenek Vasicek", title = "Evolution of Cellular Automata with Conditionally Matching Rules", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "1178--1185", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4799-0453-2", DOI = "doi:10.1109/CEC.2013.6557699", notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Bidlo:2021:SSCI, author = "Michal Bidlo", booktitle = "2021 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "Evolution of Approximate Functions for Image Thresholding", year = "2021", abstract = "This paper investigates the use of approximate addition and multiplication for designing image thresholding functions. Cartesian Genetic Programming is applied for the evolutionary design of circuits using various implementations of the approximate operations. The results are presented for various experimental setups and compared with the case when only exact addition and multiplication is considered. It will be shown that for some range of error metrics of the approximate operations the evolution provides solutions that are better than those provided by the exact operations. Moreover, approximate components allows reducing the implementation area of the resulting functions.", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming", DOI = "doi:10.1109/SSCI50451.2021.9659876", month = dec, notes = "Also known as \cite{9659876}", } @PhdThesis{MichelJanMarinusBieleveldCorr16, author = "Michel Jan Marinus Bieleveld", title = "Improving species distribution model quality with a parallel linear genetic programming-fuzzy algorithm", titletranslation = "Melhorar a qualidade de modelo de distribui{\c c}{\~a}o das esp{\'e}cies com um algoritmo paralelo de programa{\c c}{\~a}o linear gen{\'e}tico-fuzzy.", school = "Computer Engineering, Escola Politecnica", year = "2016", type = "Tese de Doutorado", address = "Brazil", month = "9 " # sep, keywords = "genetic algorithms, genetic programming, applied and specific algorithms, bioclimatologia, ecological niche models, fuzzy logic, species distribution modelling", bibsource = "OAI-PMH server at www.teses.usp.br", contributor = "Antonio Mauro Saraiva", language = "en", oai = "oai:teses.usp.br:tde-26012017-113329", rights = "Liberar o conte{\'u}do para acesso p{\'u}blico.", URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/", URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/en.php", URL = "http://www.teses.usp.br/teses/disponiveis/3/3141/tde-26012017-113329/publico/MichelJanMarinusBieleveldCorr16.pdf", publisher = "Biblioteca Digitais de Teses e Disserta{\c c}{\~o}es da USP", size = "142 pages", abstract = "Biodiversity, the variety of life on the planet, is declining due to climate change, population and species interactions and as the result f demographic and landscape dynamics. Integrated model-based assessments play a key role in understanding and exploring these complex dynamics and have proven use in conservation planning. Model-based assessments using Species Distribution Models constitute an efficient means of translating limited point data to distribution probability maps for current and future scenarios in support of conservation decision making. The aims of this doctoral study were to investigate; (1) the use of a hybrid genetic programming to build high quality models that handle noisy real-world presence and absence data, (2) the extension of this solution to exploit the parallelism inherent to genetic programming for fast scenario based decision making tasks, and (3) a conceptual framework to share models in the hope of enabling research synthesis. Subsequent to this, the quality of the method, evaluated with the true skill statistic, was examined with two case studies. The first with a dataset obtained by defining a virtual species, and the second with data extracted from the North American Breeding Bird Survey relating to mourning dove (Zenaida macroura). In these studies, the produced models effectively predicted the species distribution up to 30percent of error rate both presence and absence samples. The parallel implementation based on a twenty-node c3.xlarge Amazon EC2 StarCluster showed a linear speedup due to the multiple-deme coarse-grained design. The hybrid fuzzy genetic programming algorithm generated under certain consitions during the case studies significantly better transferable models.", abstract = "Biodiversidade, a variedade de vida no planeta, est{\'a} em decl{\'i}nio {\`a}s altera{\c c}{\~o}es clim{\'a}ticas, mudan{\c c}as nas intera{\c c}{\~o}es das popula{\c c}{\~o}es e esp{\'e}cies, bem como nas altera{\c c}{\~o}es demogr{\'a}ficas e na din{\^a}mica de paisagens. Avalia{\c c}{\~o}es integradas baseadas em modelo desempenham um papel fundamental na compreens{\~a}o e na explora{\c c}{\~a}o destas din{\^a}micas complexas e tem o seu uso comprovado no planejamento de conserva{\c c}{\~a}o da biodiversidade. Os objetivos deste estudo de doutorado foram investigar; (1) o uso de t{\'e}cnicas de programa{\c c}{\~a}o gen{\'e}tica e fuzzy para construir modelos de alta qualidade que lida com presen{\c c}a e aus{\^e}ncia de dados ruidosos do mundo real, (2) a extens{\~a}o desta solu{\c c}{\~a}o para explorar o paralelismo inerente {\`a} programa{\c c}{\~a}o gen{\'e}tica para acelerar tomadas de decis{\~a}o e (3) um framework conceitual para compartilhar modelos, na expectativa de permitir a s{\'i}ntese de pesquisa. Subsequentemente, a qualidade do m{\'e}todo, avaliada com a true skill statistic, foi examinado com dois estudos de caso. O primeiro utilizou um conjunto de dados fict{\'i}cios obtidos a partir da defini{\c c}{\~a}o de uma esp{\'e}cie virtual, e o segundo utilizou dados de uma esp{\'e}cie de pomba (Zenaida macroura) obtidos do North American Breeding Bird Survey. Nestes estudos, os modelos foram capazes de predizer a distribui{\c c}{\~a}o das esp{\'e}cies maneira correta mesmo utilizando bases de dados com at{\'e} 30percent de erros nas amostras de presen{\c c}a e de aus{\^e}ncia. A implementa{\c c}{\~a}o paralela utilizando um cluster de vinte n{\'o}s c3.xlarge Amazon EC2 StarCluster, mostrou uma acelera{\c c}{\~a}o linear devido ao arquitetura de m{\'u}ltiplos deme de granula{\c c}{\~a}o grossa. O algoritmo de programa{\c c}{\~a}o gen{\'e}tica e fuzzy gerada em determinadas condi{\c c}{\~o}es durante os estudos de caso, foram significativamente melhores na transfer{\^e}ncia do que os algoritmos do BIOMOD.", } @InProceedings{DBLP:conf/icml/BielikRV16, author = "Pavol Bielik and Veselin Raychev and Martin T. Vechev", title = "{PHOG:} Probabilistic Model for Code", booktitle = "Proceedings of the 33nd International Conference on Machine Learning, ICML 2016", year = "2016", editor = "Maria-Florina Balcan and Kilian Q. Weinberger", series = "JMLR Workshop and Conference Proceedings", volume = "48", pages = "2933--2942", address = "New York City, NY, USA", month = jun # " 19-24", publisher = "PMLR", keywords = "genetic algorithms, genetic programming, seeding", timestamp = "Wed, 29 May 2019 08:41:46 +0200", biburl = "https://dblp.org/rec/conf/icml/BielikRV16.bib", bibsource = "dblp computer science bibliography, https://dblp.org", URL = "http://proceedings.mlr.press/v48/bielik16.html", URL = "http://proceedings.mlr.press/v48/bielik16.pdf", size = "10 pages", abstract = "We introduce a new generative model for code called probabilistic higher order grammar (PHOG). PHOG generalises probabilistic context free grammars (PCFGs) by allowing conditioning of a production rule beyond the parent non-terminal, thus capturing rich contexts relevant to programs. Even though PHOG is more powerful than a PCFG, it can be learned from data just as efficiently. We trained a PHOG model on a large JavaScript code corpus and show that it is more precise than existing models, while similarly fast. As a result, PHOG can immediately benefit existing programming tools based on probabilistic models of code.", notes = "'Learning of TCOND Functions... Enumerative search is exponential we use it only on short functions with up to 5 instructions... The resulting functions serve as a starting population for a follow-up genetic programming search... We do not apply a cross-over operation in the genetic search procedure. Also known as \cite{pmlr-v48-bielik16} Cited by \cite{DBLP:conf/pldi/LeeHAN18}", } @Article{Biesheuvel:2004:JCE, author = "Cornelis J. Biesheuvel and Ivar Siccama and Diederick E. Grobbee and Karel G. M. Moons", title = "Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism", journal = "Journal of Clinical Epidemiology", year = "2004", volume = "57", pages = "551--560", number = "6", abstract = "Objective Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. Study design and setting We compared genetic programming and the commonly used logistic regression technique in the development of a prediction model using empirical data from a study on diagnosis of pulmonary embolism. Using part (67%) of the data, we developed and internally validated (using bootstrapping techniques) a diagnostic prediction model by genetic programming and by logistic regression, and compared both on their predictive ability in the remaining data (validation set). Results In the validation set, the area under the ROC curve of the genetic programming model was significantly larger (0.73; 95%CI: 0.64-0.82) than that of the logistic regression model (0.68; 0.59-0.77). The calibration of both models was similar, indicating a similar amount of overoptimism. Conclusion Although the interpretation of a genetic programming model is less intuitive and this is the first empirical study quantifying its value for medical prediction, genetic programming seems a promising technique to develop prediction rules for diagnostic and prognostic purposes.", owner = "wlangdon", URL = "http://igitur-archive.library.uu.nl/med/2006-0906-200235/grobbee_04_geneticprogrammingoutperformed.pdf", URL = "http://www.sciencedirect.com/science/article/B6T84-4CTB5RT-3/2/325f5e3699d990701839201564eff8d3", month = jun, keywords = "genetic algorithms, genetic programming, Logistic regression, Prediction, Diagnostic research, Discrimination, Reliability", DOI = "doi:10.1016/j.jclinepi.2003.10.011", size = "10 pages", notes = "PMID: 15246123 [PubMed - indexed for MEDLINE]", } @PhdThesis{biesheuvel:thesis, author = "Cornelis Jan Biesheuvel", title = "Diagnostic Research : improvements in design and analysis", school = "Universiteit Utrecht", year = "2005", address = "Holland", ISBN = "90-393-2706-8", keywords = "genetic algorithms, genetic programming, diagnosis, methodology, prediction research", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/full.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/title.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/contents.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c1.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c2.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c3.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c4.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c5.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c6.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c7.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c8.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/sum.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/sam.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/dank.pdf", URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/cv.pdf", size = "103 pages", abstract = "In the era of evidence-based medicine, diagnostic procedures also need to undergo critical evaluations. In contrast to guidelines for randomised trials and observational etiologic studies, principles and methods for diagnostic evaluations are still incomplete. The research described in this thesis was conducted to further improve the methods for design and analysis of diagnostic studies. In the past, most diagnostic accuracy studies followed a univariable or single test approach with the aim to quantify the sensitivity, specificity or likelihood ratio. However, single test studies and measures do not reflect a test's added value. It is not the singular association between a particular test result or predictor and the diagnostic outcome that is informative, but the test's value independent of diagnostic information. Multivariable modelling is necessary to estimate the value of a particular test conditional on other test results. However, diagnostic prediction rules are not the solution to everything. They have certain drawbacks, such as overoptimistic accuracy when applied to new patients. Recently, methods have been described to overcome some of these drawbacks. Typically, in diagnostic research one selects a cohort of patients with an indication for the diagnostic procedure at interest as defined by the patients' suspicion of having the disease of interest. The data are analysed cross-sectionally. When appropriate analyses are applied, results from nested case-control studies should be virtually identical to results based on a full cohort analysis. We showed that the nested case-control design offers investigators a valid and efficient alternative for a full cohort approach in diagnostic research. This may be particularly important when the results of the test under study are costly or difficult to collect. It is suggested that randomised controlled trials deliver the highest level of evidence to answer research questions. The paradigm of a randomised study design has also been applied to diagnostic research. We described that a randomised study design is not always necessary to evaluate the value of a diagnostic test to change patient outcome. A test's effect on patient outcome can be inferred and indeed considered as quantified -using decision analysis- 1) if the test is meant to include or exclude a disease for which an established reference is available, 2) if a cross-sectional accuracy study has shown the test's ability to adequately detect the presence or absence of that disease based on the reference, and finally 3) if proper, randomised therapeutic studies have provided evidence on efficacy of the optimal management of this disease. In such instances diagnostic research does not require an additional randomised comparison between two (or more) 'test-treatment strategies' (one with and one without the test under study) to establish the test's effect on patient outcome. Accordingly, diagnostic research -including the quantification of the effects of diagnostic testing on patient outcome- may be executed more efficiently. Diagnostic research aims to quantify a test's added contribution given other diagnostic information available to the physician in determining the presence or absence of a particular disease. Commonly, diagnostic prediction rules use dichotomous logistic regression analysis to predict the presence or absence of a disease. We showed that genetic programming and polytomous modelling are promising alternatives for the conventional dichotomous logistic regression analysis to develop diagnostic prediction rules. The main advantage of genetic programming is the possibility to create more flexible models with better discrimination. This is especially important in large data sets in which complex interactions between predictors and outcomes may be present.", abstract = "Using polytomous logistic regression, one can directly model diagnostic test results in relation to several diagnostic outcome categories. Simultaneous prediction of several diagnostic outcome probabilities particularly applies to situations in which more than two disorders are considered in the differential diagnoses. As this is commonly the case, polytomous regression analysis may serve clinical practice better than conventional dichotomous regression analysis. Both alternatives deserve closer attention in future diagnostic research. We also showed that the development of a diagnostic prediction rule is not the end of the 'research line', even when a rule is subsequently adjusted for optimism using internal validation techniques e.g. bootstrap techniques. External validation of such rules in new patients is always required before introducing a rule in daily practice. This indicates that internal validation of prediction models may not be sufficient and indicative for the model's performance in future patients. Rather than viewing a validation data set as a separate study to estimate an existing rule's performance, validation data may be combined with data of previous derivation studies to generate more robust prediction models using recently suggested methods.", notes = "* Title * Contents * Chapter 1: Introduction * Chapter 2: Test research versus diagnostic research * Chapter 3: Distraction from randomisation in diagnostic research * Chapter 4: Reappraisal of the nested case-control design in diagnostic research: updating the STARD guideline * Chapter 5: Validating and updating a prediction rule for neurological sequelae after childhood bacterial meningitis * Chapter 6: Genetic programming or multivariable logistic regression in diagnostic research * Chapter 7: Revisiting polytomous regression for diagnostic studies * Chapter 8: Concluding remarks * Summary * Samenvatting * Dankwoord * Curriculum Vitae * Volledig proefschrift (520 kB) OMEGA KiQ Ltd.", } @InProceedings{bin-murtaza:2024:GECCOcomp, author = "Sardar {Bin Murtaza} and Aidan Mccoy and Zhiyuan Ren and Aidan Murphy and Wolfgang Banzhaf", title = "{LLM} Fault Localisation within Evolutionary Computation Based Automated Program Repair", booktitle = "Large Language Models for and with Evolutionary Computation Workshop", year = "2024", editor = "Erik Hemberg and Roman Senkerik and Una-May O'Reilly and Michal Pluhacek and Tome Eftimov", pages = "1824--1829", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic improvement, fault localisation, large language models, ANN", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664174", size = "6 pages", abstract = "Repairing bugs can be a daunting task for even a human experienced in debugging, so naturally, attempting to automatically repair programs with a computer system is quite challenging. The existing methods of automated program repair leave a lot of room for improvement. Fault localization, which aims to find lines of code that are potentially buggy, minimises the search space of an automated program repair system. Recent work has shown improvement in these fault localization methods, with the use of Large Language Models. Here, we propose a system where a LLM-based fault localization tool, which we call SemiAutoFL, is used within a fully automatic program repair program, ARJA-e. We show that using LLM-based fault localization with ARJA-e can significantly improve its performance on real world bugs. ARJA-e with SemiAutoFL can repair 10 bugs that ARJA-e was previously unable to so do. This finding adds to our understanding of how to improve fault localization and automated program repair, highlighting the potential for more efficient and accurate fault localisation methods being applied to automated program repair.", notes = "GECCO-2024 LLMfwEC A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{1274004, author = "Franck Binard and Amy Felty", title = "An abstraction-based genetic programming system", booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2007)}", year = "2007", month = "7-11 " # jul, editor = "Peter A. N. Bosman", pages = "2415--2422", address = "London, United Kingdom", publisher = "ACM Press", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lambda calculus, languages, polymorphism, types", isbn13 = "978-1-59593-698-1", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2415.pdf", DOI = "doi:10.1145/1274000.1274004", abstract = "We extend tree-based typed Genetic Programming (GP) representation schemes by introducing System F, an expressive l-calculus, for representing programs and types. At the level of programs, System F provides higher-order programming capabilities with functions and types as first-class objects, e.g., functions can take other functions and types as parameters. At the level of types, System F provides parametric polymorphism. The expressiveness of the system provides the potential for a genetic programming system to evolve looping, recursion, lists, trees and many other typical programming structures and behaviour. This is done without introducing additional external symbols in the set of predefined functions and terminals of the system. In fact, we actually remove programming structures such as if/then/else, which we replace by two abstraction operators. We also change the composition of parse trees so that they may directly include types.", notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071", } @InProceedings{Binard:2008:gecco, author = "Franck Binard and Amy Felty", title = "Genetic Programming with Polymorphic Types and Higher-Order Functions", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", pages = "1187--1194", address = "Atlanta, GA, USA", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, lambda calculus, polymorphism, types", isbn13 = "978-1-60558-130-9", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1187.pdf", DOI = "doi:10.1145/1389095.1389330", size = "8 pages", abstract = "we introduce our new approach to program representation for genetic programming (GP). We replace the usual s-expression representation scheme by a strongly-typed abstraction-based representation scheme. This allows us to represent many typical computational structures by abstractions rather than by functions defined in the GP system terminal set. The result is a generic GP system that is able to express programming structures such as recursion and data types without explicit definitions. We demonstrate the expressive power of this approach by evolving simple Boolean programs without defining a set of terminals. We also evolve programs that exhibit recursive behaviour without explicitly defining recursion specific syntax in the terminal set. we present our approach and experimental results.", notes = "System-F GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389330}", } @PhdThesis{Binard:thesis, author = "Franck J. L. Binard", title = "Abstraction-Based Genetic Programming", school = "Ottawa-Carleton Institute for Computer Science, School of Information Technology and Engineering, Faculty of Engineering, University of Ottawa", year = "2009", address = "Ottawa, Canada", keywords = "genetic algorithms, genetic programming", URL = "http://www.site.uottawa.ca/~fbinard/Articles/FranckBinardPhDThesisLastVersion.pdf", URL = "http://hdl.handle.net/10393/29805", DOI = "doi:10.20381/ruor-13147", size = "178 pages", abstract = "This thesis describes a novel method for representing and automatically generating computer programs in an evolutionary computation context. Abstraction-Based Genetic Programming (ABGP) is a typed Genetic Programming representation system that uses System F, an expressive lambda-calculus, to represent the computational components from which the evolved programs are assembled. ABGP is based on the manipulation of closed, independent modules expressing computations with effects that have the ability to affect the whole genotype . These modules are plugged into other modules according to precisely defined rules to form complete computer programs. The use of System F allows the straightforward representation and use of many typical computational structures and behaviours (such as iteration, recursion, lists and trees) in modular form. This is done without introducing additional external symbols in the set of predefined functions and terminals of the system. In fact, programming structures typically included in GP terminal sets, such as if then else, may be removed and represented as abstractions in ABGP for the same problems. ABGP also provides a search space partitioning system based on the structure of the genotypes, similar to the species partitioning system of living organisms and derived from the Curry-Howard isomorphism. This thesis also presents the results obtained by applying this method to a set of problems.", notes = "Supervisor: Amy Felty", } @Book{Binard:book, author = "Franck Binard", title = "Abstraction-Based Genetic Programming: An Application of the polymorphically-typed lambda calculus to genetic programming", publisher = "Verlag Dr. Mueller", year = "2009", month = "6 " # oct, keywords = "genetic algorithms, genetic programming", ISBN = "3-639-19173-0", URL = "https://www.amazon.co.uk/Abstraction-Based-Genetic-Programming-Application-polymorphically-typed/dp/3639191730", size = "184 pages", abstract = "Abstraction-Based Genetic Programming (ABGP) is a novel Genetic Programming (GP) system in which the set of all possible genotypes is partitioned by the proofs to which each program is linked via the Curry-Howard isomorphism. In the context of ABGP, proofs are related to computer programs in the same way as species are related to organisms in the biological world. They can be seen as patterns into which alleles of genes may be plugged in. In this analogy, genes are types and an allele of a gene is a closed typed computational block that may be combined with other blocks to form an organism. The type of an allele is the gene to which it corresponds.", } @InProceedings{Bing:2010:ETCS, author = "Wu Bing and Zhang Wen-qiong and Liang Jia-hong", title = "A Genetic Multiple Kernel Relevance Vector Regression Approach", booktitle = "Second International Workshop on Education Technology and Computer Science (ETCS), 2010", year = "2010", month = mar, volume = "3", pages = "52--55", abstract = "Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasised the requirement to multiple kernel learning. This paper proposes a novel regression technique, called Genetic Multiple Kernel Relevance Vector Regression (GMK RVR), which combines genetic programming and relevance vector regression to evolve a multiple kernel function. The proposed technique are compared with those of a standard RVR using the Polynomial, Gaussian RBF and Sigmoid kernel with various parameter settings, based on several benchmark problems. Numerical experiments show that the GMK performs better than such widely used kernels and prove the validation of the GMK.", keywords = "genetic algorithms, GMK validation, Gaussian RBF, Sigmoid kernel, benchmark problems, genetic multiple kernel relevance vector regression, kernel function, multiple kernel function, multiple kernel learning, parameter selection, relevance vector machine, sparse Bayesian extension, state-of-the-art technique, support vector machine, Bayes methods, learning (artificial intelligence), pattern classification, regression analysis, support vector machines", DOI = "doi:10.1109/ETCS.2010.154", notes = "Not a GP, fixed representation. Also known as \cite{5460012}", } @Article{bingol:2018:NCaA, author = "Sedat Bingol and Hidir Yanki Kilicgedik", title = "Application of gene expression programming in hot metal forming for intelligent manufacturing", journal = "Neural Computing and Applications", year = "2018", volume = "30", number = "3", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://link.springer.com/article/10.1007/s00521-016-2718-5", DOI = "doi:10.1007/s00521-016-2718-5", } @Article{emse19, author = "David Binkley and Nicolas Gold and Syed Islam and Jens Krinke and Shin Yoo", title = "A comparison of tree- and line-oriented observational slicing", journal = "Empirical Software Engineering", year = "2019", volume = "24", pages = "3077--3113", month = oct, note = "Special Section on Source Code Analysis and Manipulation", keywords = "genetic algorithms, genetic programming, ORBS, source code deletion, srcML, XML", ISSN = "1382-3256", URL = "http://www.cs.ucl.ac.uk/staff/j.krinke/publications/emse19.pdf", DOI = "doi:10.1007/s10664-018-9675-9", size = "41 pages", abstract = "Observation-based slicing and its generalization observational slicing are recently-introduced, language-independent dynamic slicing techniques. They both construct slices based on the dependencies observed during program execution, rather than static or dynamic dependence analysis. The original implementation of the observation-based slicing algorithm used lines of source code as its program representation. A recent variation, developed to slice modeling languages (such as Simulink), used an XML representation of an executable model. We ported the XML slicer to source code by constructing a tree representation of traditional source code through the use of srcML. This work compares the tree- and line-based slicers using four experiments involving twenty different programs, ranging from classic benchmarks to million-line production systems. The resulting slices are essentially the same size for the majority of the programs and are often identical. However, structural constraints imposed by the tree representation sometimes force the slicer to retain enclosing control structures. It can also bog down trying to delete single-token subtrees. This occasionally makes the tree-based slices larger and the tree-based slicer slower than a parallelised version of the line-based slicer. In addition, a Java versus C comparison finds that the two languages lead to similar slices, but Java code takes noticeably longer to slice. The initial experiments suggest two improvements to the tree-based slicer: the addition of a size threshold, for ignoring small subtrees, and subtree replacement. The former enables the slicer to run 3.4 times faster while producing slices that are only about 9percent larger. At the same time the subtree replacement reduces size by about 8 to 12 percent and allows the tree-based slicer to produce more natural slices.", notes = "Not on GP but interesting manipulation of human written high level code via tree based XML ? Section 3.1.2 Subtree Replacement, similar to Hoist mutation \cite{kinnear:kinnear} ? Section 6.2.2 X=10 over fitting? srcML used in genetic improvement PyGGI 2.0 eg \cite{an:2019:fse}", } @Article{Birchenhall:1995:EJ, author = "C. R. Birchenhall", copyright = "Copyright 1995 Royal Economic Society", ISSN = "00130133", journal = "The Economic Journal", number = "430", owner = "wlangdon", pages = "788--795", title = "Genetic Algorithms, Classifier Systems and Genetic Programming and their Use in the Models of Adaptive Behaviour and Learning", URL = "http://links.jstor.org/sici?sici=0013-0133%28199505%29105%3A430%3C788%3AGACSAG%3E2.0.CO%3B2-%23", volume = "105", year = "1995", keywords = "genetic algorithms, genetic programming", notes = "Reviewed? in The Economic Journal, vol 106 number 434, 1996 APPROX pages 271", } @InProceedings{DBLP:conf/petra/Bird22, author = "Jordan J. Bird", title = "{EEG} Wavelet Classification for Fall Detection with Genetic Programming", booktitle = "{PETRA} '22: The 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, 29 June 2022 - 1 July 2022", pages = "376--382", publisher = "{ACM}", year = "2022", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1145/3529190.3535339", DOI = "doi:10.1145/3529190.3535339", timestamp = "Tue, 12 Jul 2022 16:32:52 +0200", biburl = "https://dblp.org/rec/conf/petra/Bird22.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bird:2023:GPEM, author = "Jordan J. Bird and Ahmad Lotfi", title = "Fall compensation detection from {EEG} using neuroevolution and genetic hyperparameter optimisation", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "2", pages = "Article number: 6", month = jun, note = "Online first", keywords = "genetic algorithms, genetic programming, ANN, Evolutionary optimisation, Fall detection, EEG, Hyperheuristics, Signal classification", ISSN = "1389-2576", URL = "https://rdcu.be/dcJdp", DOI = "doi:10.1007/s10710-023-09453-3", size = "26 pages", abstract = "Detecting fall compensatory behaviour from large EEG datasets poses a difficult problem in big data which can be alleviated by evolutionary computation-based machine learning strategies. hyperheuristic optimisation solutions via evolutionary optimisation of deep neural network topologies and genetic programming of machine learning pipelines will be investigated. Wavelet extractions from signals recorded during physical activities present a binary problem for detecting fall compensation. The earlier results show that a Gaussian process model achieves an accuracy of 86.48percent. Following this, artificial neural networks are evolved through evolutionary algorithms and score similarly to most standard models; the hyperparameters chosen are well outside the bounds of batch or manual searches. Five iterations of genetic programming scored higher than all other approaches, at a mean 90.52percent accuracy. The best pipeline extracted polynomial features and performed Principal Components Analysis, before machine learning through a randomised set of decision trees, and passing the class prediction probabilities to a 72-nearest-neighbour algorithm. The best genetic solution could infer data in 0.02 seconds, whereas the second best genetic programming solution (89.79percent) could infer data in only 0.3 milliseconds.", } @PhdThesis{Bird:thesis, author = "Sarah Lynn Bird", title = "Optimizing Resource Allocations for Dynamic Interactive Applications", school = "University of California, Berkeley", year = "2014", address = "USA", month = may, keywords = "genetic algorithms, genetic programming, PACORA, Matlab", URL = "https://aspire.eecs.berkeley.edu/publication/optimizing-resource-allocations-for-dynamic-interactive-applications/", URL = "https://people.eecs.berkeley.edu/~krste/papers/bird-phd.pdf", URL = "https://escholarship.org/uc/item/9nf5z4pg", size = "121 pages", abstract = "Modern computing systems are under intense pressure to provide guaranteed responsiveness to their workloads. Ideally, applications with strict performance requirements should be given just enough resources to meet these requirements consistently, without unnecessarily siphoning resources from other applications. However, executing multiple parallel, real-time applications while satisfying response time requirements is a complex optimization problem and traditionally operating systems have provided little support to provide QoS to applications. As a result, client, cloud, and embedded systems have all resorted to over-provisioning and isolating applications to guarantee responsiveness. Instead, we present PACORA, a resource allocation framework designed to provide responsiveness guarantees to a simultaneous mix of high-throughput parallel, interactive, and real-time applications in an efficient, scalable manner. By measuring application behavior directly and using convex optimization techniques, PACORA is able to understand the resource requirements of applications and perform near-optimal resource allocation two percent from the best allocation in 1.4 milliseconds while only requiring a few hundred bytes of storage per application.", notes = "Advisors: Krste Asanovic and Dave Patterson and Burton Smith", } @InCollection{oai:CiteSeerPSU:397549, title = "Schemas and Genetic Programming", author = "Andreas Birk and Wolfgang J. Paul", booktitle = "Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic {II}", publisher = "Kluwer", year = "2001", editor = "Holk Cruse and Jeffrey Dean and Helge Ritter", volume = "26", series = "Studies in Cognitive Systems", pages = "345--357", ebook_pages = "804--816", keywords = "genetic algorithms, genetic programming", ISBN = "0-7923-6666-2", isbn13 = "978-94-010-3792-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-135-22-33673255-0,00.html", URL = "http://www.faculty.iu-bremen.de/birk/publications/schemas_genetic_programming.pdf", URL = "http://arti.vub.ac.be/~cyrano/PUBLICATIONS/schema_gp00.ps.gz", URL = "http://citeseer.ist.psu.edu/397549.html", DOI = "doi:10.1007/978-94-010-0870-9_50", citeseer-isreferencedby = "oai:CiteSeerPSU:106696; oai:CiteSeerPSU:67434; oai:CiteSeerPSU:532836; oai:CiteSeerPSU:86635; oai:CiteSeerPSU:54193; oai:CiteSeerPSU:315750; oai:CiteSeerPSU:89833; oai:CiteSeerPSU:66393; oai:CiteSeerPSU:226046; oai:CiteSeerPSU:360779; oai:CiteSeerPSU:193774", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:397549", rights = "unrestricted", size = "13 pages", abstract = "To investigate the mechanisms which enable systems to learn is among the most challenging of research activities. In computer science alone it is pursued by at least three communities (Carbonel 1990; Natarajan 1991; Ritter et al. 1991). The overwhelming majority of all studies treats situations with strong inductive bias, i.e. there is a fairly narrow class H of algorithms and the concept or algorithm to be learned is known a priori to lie in that class H. With the help of schemas and genetic programming we describe systems which: interact with the real world make theories about the consequences of their actions and dynamically adjust inductive bias. We present experimental data related to learning geometric concepts and moving a block in a microworld.", } @InProceedings{Birtolo:2010:ICEIS, author = "Cosimo Birtolo and Roberto Armenise and Luigi Troiano", title = "Supporting Menu Layout Design by Genetic Programming", booktitle = "Proceedings of the 12th International Conference on Enterprise Information Systems (ICEIS 2010)", year = "2010", editor = "Joaquim Filipe and Jos{\'e} Cordeiro", address = "Funchal, Madeira, Portugal", month = "8 - 12 " # jun, keywords = "genetic algorithms, genetic programming: poster, SBSE", broken = "https://www.poste.it/azienda/research_development/pubblicazioniCentroRicerca.html", URL = "https://www.poste.it/azienda/research_development/pubblicazioni/ICEIS10%20-%20SUPPORTING%20MENU%20LAYOUT%20DESIGN%20BY%20GP.pdf", size = "4 pages", abstract = "Graphical User Interfaces heavily rely on menus to access application functionalities. Therefore designing properly menus poses relevant usability issues to face. Indeed, trading off between semantic preferences and usability makes this task not so easy to be performed. Meta-heuristics represent a promising approach in assisting designers to specify menu layouts. In this paper, we propose a preliminary experience in adopting Genetic Programming as a natural means for evolving a menu hierarchy towards optimal structure.", notes = "http://www.iceis.org/iceis2010/index.htm http://www.iceis.org/Abstracts/2010/ICEIS_2010_Abstracts.htm Despite http://dblp.uni-trier.de/rec/bibtex/conf/iceis/BirtoloAT10 does not appear to be in electronic proceedings published by Springer isbn 978-989-8425-08-9, pages 248-251", } @InProceedings{bisat:1998:ussbctn, author = "Mona T. Bisat and Charles W. Richter and Gerald B. Sheble", title = "Using Adaptive Agents to Study Bilateral Contracts and Trade Networks", booktitle = "Late Breaking Papers at the Genetic Programming 1998 Conference", year = "1998", editor = "John R. Koza", pages = "23--27", address = "University of Wisconsin, Madison, Wisconsin, USA", publisher_address = "Stanford, CA, USA", month = "22-25 " # jul, publisher = "Stanford University Bookstore", keywords = "genetic algorithms, genetic programming, electricity transmission capacity, trade network game, double auction bid, power utility", URL = "http://dakotarichter.com/papers/gp98PosterPaperBisatRichterSheble.pdf", size = "5 pages", abstract = "This research is an extension of research done by Charles Richter, Gerald Sheble' and Dan Ashlock (1997, 1998) on double auction bidding strategies for electric utilities which trade competitively. This research considers the network topology and whether a successful bid transaction can occur given the flow constraints on the network. The ATC (Available Transmission Capacity) of the network is a flow constraint indicator that is used to provide feedback to agents attempting to engage in bilateral contracts. The aim is to develop adaptive agents that are able to recognize with whom they can enter a profitable bilateral contract. In other words, the agents develop preferential partner selection lists and bidding strategies in a simulated electric market. The idea of evolving preferred trading partner lists comes from the Trade Network Game (TNG) developed by Tesfatsion, Ashlock and Stanley (1995). The strategies being developed by the method described here are adaptive. The strategies are encoded in GP-Automata, a technique which combines genetic programming and finite state automata.", notes = "GP-98LB", } @PhdThesis{Bischl:thesis, author = "Bernd Bischl", title = "Model and Algorithm Selection in Statistical Learning and Optimization", school = "Fakultaet Statistik, Technische Universitaet Dortmund", year = "2014", address = "Germany", month = "7 " # feb, keywords = "genetic algorithms, genetic programming, SVM, model selection, algorithm selection, algorithm configuration, tuning, benchmarking, machine learning", URL = "https://eldorado.tu-dortmund.de/bitstream/2003/32861/1/phd.pdf", URL = "http://hdl.handle.net/2003/32861", URL = "https://eldorado.tu-dortmund.de/handle/2003/32861", DOI = "doi:10.17877/DE290R-7142", size = "37 pages", abstract = "Modern data-driven statistical techniques, e.g., non-linear classification and regression machine learning methods, play an increasingly important role in applied data analysis and quantitative research. For real-world we do not know a priori which methods will work best. Furthermore, most of the available models depend on so called hyper- or control parameters, which can drastically influence their performance. This leads to a vast space of potential models, which cannot be explored exhaustively. Modern optimization techniques, often either evolutionary or model-based, are employed to speed up this process. A very similar problem occurs in continuous and discrete optimization and, in general, in many other areas where problem instances are solved by algorithmic approaches: Many competing techniques exist, some of them heavily parametrized. Again, not much knowledge exists, how, given a certain application, one makes the correct choice here. These general problems are called algorithm selection and algorithm configuration. Instead of relying on tedious, manual trial-and-error, one should rather employ available computational power in a methodical fashion to obtain an appropriate algorithmic choice, while supporting this process with machine-learning techniques to discover and exploit as much of the search space structure as possible. In this cumulative dissertation I summarize nine papers that deal with the problem of model and algorithm selection in the areas of machine learning and optimization. Issues in benchmarking, resampling, efficient model tuning, feature selection and automatic algorithm selection are addressed and solved using modern techniques. I apply these methods to tasks from engineering, music data analysis and black-box optimization. The dissertation concludes by summarizing my published R packages for such tasks and specifically discusses two packages for parallelization on high performance computing clusters and parallel statistical experiments.", notes = "p14 'on the considered [SVM] benchmark problems even our improved genetic programming approach leads to disappointing results. Although good-performing common kernel functions can be recovered by the genetic search' In English", } @Article{BISHAYEE:2023:gsd, author = "Bhaskar Bishayee and Abhay Kumar and Sandip Kumar Lahiri and Susmita Dutta and Biswajit Ruj", title = "Modeling, optimization and comparative study on abatement of fluoride from synthetic solution using activated laterite soil and fly ash", journal = "Groundwater for Sustainable Development", volume = "23", pages = "101016", year = "2023", ISSN = "2352-801X", DOI = "doi:10.1016/j.gsd.2023.101016", URL = "https://www.sciencedirect.com/science/article/pii/S2352801X23001169", keywords = "genetic algorithms, genetic programming, Fluoride, Adsorption, Natural and waste material, Kinetic and equilibrium study", abstract = "The supply of potable water by proper treatment of fluoride laden ground water is a challenging task. Batch experiments were executed with activated laterite soil and fly ash individually for removal of fluoride. The process parameters such as particle size (100-620 ?m), dose of adsorbent (10-60 g/L), initial concentration of fluoride (2-12 mg/L), agitation speed (20-120 rpm), contact time (0.5-10 h) and pH (4.5-9.5) were investigated and it was observed that activated laterite soil had better fluoride removal efficiency than fly ash. At pH 4.5, contact time 10 h, particle size 100 ?m, adsorbent dose 60 g/L, initial concentration of fluoride 12 mg/L, and agitation speed 120 rpm, maximum removal efficiencies of fluoride using activated laterite soil and fly ash were found as 85.91 pm 0.62percent and 77.1 pm 0.39percent, respectively. Kinetic study was performed and Pseudo 2nd order kinetic model was found to fit the kinetic data best. The Freundlich isotherm model fit the equilibrium data fairly well. The values of adsorption equilibrium constant as used in Freundlich isotherm model vis-a-vis adsorption capacity (KF) for activated laterite soil and fly ash were obtained as 0.1331 and 0.0772 ((mgg-1)(Lmg-1)1/n). A thermodynamic analysis was conducted to examine the process behaviour. In order to determine whether the adsorbent may be used for further cases, regeneration of the adsorbents was also done. Generally, two types of adsorption mechanisms happen like electrostatic attraction and ion exchange. The electrostatic force of attraction favours the adsorption of negatively charged fluoride ions on positively charged adsorbent surfaces. In ion exchange, fluoride ions are exchanged by hydroxyl and hydronium ion. Based on experimental data, Multi-Gene Genetic Programming (MGGP) model could accurately predict the removal efficiency of fluoride under various operating situations. Finally, using Genetic Algorithm (GA) optimization the maximum output values for both adsorbents were estimated as 99.14percent and 99.02percent", } @InProceedings{conf/evoW/BishopCT14, title = "Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value", author = "Andrew Bishop and Victor Ciesielski and Karen Trist", booktitle = "Evolutionary and Biologically Inspired Music, Sound, Art and Design - Third European Conference, Evo{MUSART} 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers", publisher = "Springer", year = "2014", volume = "8601", editor = "Juan Romero and James McDermott and Joao Correia", isbn13 = "978-3-662-44334-7", pages = "62--73", series = "Lecture Notes in Computer Science", keywords = "genetic algorithms, genetic programming", bibdate = "2014-09-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evomusart2014.html#BishopCT14", URL = "http://dx.doi.org/10.1007/978-3-662-44335-4", } @InProceedings{bishop:2024:GECCO, author = "Jordan T Bishop and Jason Jooste and David Howard", title = "Evolutionary Exploration of Triply Periodic Minimal Surfaces via Quality Diversity", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference", year = "2024", editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva", pages = "1165--1173", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, triply periodic minimal surface, quality diversity, evolutionary design, constrained optimisation, Real World Applications", isbn13 = "979-8-4007-0494-9", DOI = "doi:10.1145/3638529.3654039", size = "9 pages", abstract = "Triply Periodic Minimal Surfaces (TPMSs) are a family of mathematical structures that exhibit constant zero mean curvature and 3-dimensional periodicity. They are often used to produce cellular solids with advantageous structural, thermal, and optical properties. Existing applications represent TPMSs as trigonometric approximations of a Fourier series. Due to the mathematical difficulty of determining new exact forms and their approximations, previous work has mostly evaluated metrics based on geometry, manufacturability, and mechanical performance across parameterisations of a small set of known TPMS equations. In this work, we define TPMS-like structures as having low estimated mean curvature, and apply a coupling of Grammatical Evolution and Quality Diversity to generate a diverse set of novel structures of this kind. We additionally explore the effect of being TPMS-like on the manufacturability of evolved structures. Results show that many TPMS-like designs can be found for different combinations of total surface area and Gaussian curvature, and that there is not a strong relationship between how TPMS-like a design is and its manufacturability. Our method serves as a basis for future application of novel TPMS-like structures and exploration of the pairing of evolutionary design with generative approaches from broader machine learning.", notes = "GECCO-2024 RWA A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Bishop96, author = "P. Bishop and R. Bloomfield", title = "Conservative theory for long-term reliability-growth prediction [of software]", journal = "IEEE Transactions on Reliability", volume = "45", number = "4", month = dec, pages = "550--560", notes = "Theoretical or Mathematical", address = "Adelard, London, UK", year = "1996", ISSN = "0018-9529", URL = "http://ieeexplore.ieee.org/iel1/24/12134/00556578.pdf?isNumber=12134&prod=JNL&arnumber=556578&arSt=550&ared=560&arAuthor=Bishop%2C+P.%3B+Bloomfield%2C+R.", URL = "http://www.adelard.co.uk/resources/papers/pdf/issre96m.pdf", abstract = "This paper describes a different approach to software reliability growth modeling which enables long-term predictions. Using relatively common assumptions, it is shown that the average value of the failure rate of the program, after a particular use-time, t, is bounded by N/(e/spl middot/t), where N is the initial number of faults. This is conservative since it places a worst-case bound on the reliability rather than making a best estimate. The predictions might be relatively insensitive to assumption violations over the longer term. The theory offers the potential for making long-term software reliability growth predictions based solely on prior estimates of the number of residual faults. The predicted bound appears to agree with a wide range of industrial and experimental reliability data. Less pessimistic results can be obtained if additional assumptions are made about the failure rate distribution of faults.", keywords = "software reliability, reliability theory, failure analysis, long-term reliability-growth prediction, software reliability growth modeling, program failure rate, use-time, initial fault number, worst-case bound, residual fault number, failure rate distribution", notes = "cf. \cite{brady:murphy}", } @PhdThesis{Biswas:thesis, author = "Arijit Biswas", title = "Optimization of Hydrometallurgical Processing of Lean Manganese Bearing Resources", school = "Metallurgical and Materials Engineering, Indian Institute of Technology", year = "2010", address = "Kharagpur, India", keywords = "genetic algorithms, genetic programming, Applied science, Chemical Engineering, Evolutionary Algorithm, Evolutionary neural network, Manganese ore, Materials science, Polymetallic sea nodules, Process flowsheeting, Sequential modular approach, Split fraction", URL = "http://www.idr.iitkgp.ac.in/xmlui/handle/123456789/951", abstract = "An evolutionary multi-objective optimization framework is evolved to model the extraction process of manganese from lean manganese bearing resources. The primary objective of this thesis is to develop a generic flowsheet and to come up with a data driven modelling approach for this purpose. Flowsheets developed for processing low grade manganese ores, such as Polymetallic Sea nodules, via various processing routes are optimized using an Evolutionary Multi-objective strategy. The work also aims to provide a considerable insight towards understanding of the leaching processes pertinent to manganese extraction. To analyse and optimize the process flow sheets for treatment of low grade manganese ores, two hydrometallurgical routes based upon ammoniacal and acid leaching in presence of reducing agents are taken up. The analyses suggested that of particular significance is the grade of the ore being treated, the energy consumed and the associated costs, options for by-product recovery, and the relative price of the products. A process scheme has been optimized here for simultaneously maximizing the metal throughput and minimizing the direct operating costs incurred within constraints set for the operating variables. This leads to a multi-objective optimization problem, which has been conducted during this study for the leaching of polymetallic nodules. To analyse the non-linear kinetics of the leaching reaction of lean manganese bearing ores, an analytical model is developed along with a number of data driven models. Terrestrial lean manganese ores need to be processed in acidic medium in presence of reducing agents like glucose, lactose and sucrose, in order to extract manganese values from them. In this study data driven models based on Neural Network and Genetic Programming are compared for two different categories of manganese ores leached in sulphuric acid medium. A Predator-prey Genetic Algorithm approach developed for this purpose is pitted against a number of other established evolutionary techniques, in addition to a commercial software. A leaching model is evolved using the fitted leaching parameters from different data driven models and is thoroughly tested for the goodness of fit against the experimental data. The strategy adopted, once again, is generic in nature and the framework can be extended for any kind of hydrometallurgical process flowsheeting.", } @Article{Biswas:2011:MMP, author = "Arijit Biswas and Ogier Maitre and Debanga Nandan Mondal and Syamal Kanti Das and Prodip Kumar Sen and Pierre Collet and Nirupam Chakraborti", title = "Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies", journal = "Materials and Manufacturing Processes", year = "2011", volume = "26", number = "3", pages = "415--430", keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, Leaching, Manganese, Multiobjective optimisation, Ocean nodules, Optimisation, Pareto frontier", ISSN = "1042-6914", URL = "http://www.tandfonline.com/doi/abs/10.1080/10426914.2010.544809", URL = "http://www.tandfonline.com/doi/pdf/10.1080/10426914.2010.544809", DOI = "doi:10.1080/10426914.2010.544809", size = "16 pages", abstract = "Data-driven models are constructed for leaching processes of various low grade manganese resources using various nature inspired strategies based upon genetic algorithms, neural networks, and genetic programming and subjected to a bi-objective Pareto optimization, once again using several evolutionary approaches. Both commercially available software and in-house codes were used for this purpose and were pitted against each other. The results led to an optimum trade-off between maximising the recovery, which is a profit oriented requirement, along with a minimisation of the acid consumption, which addresses the environmental concerns. The results led to a very complex scenario, often with different trends shown by the different methods, which were systematically analysed.", } @InProceedings{conf/softcomp/BittencourtSLAO10, author = "Evandro Bittencourt and Sidney Schossland and Raul Landmann and Denio {Murilo de Aguiar} and Adilson Gomes {De Oliveira}", title = "The Gene Expression Programming Applied to Demand Forecast", booktitle = "Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010)", year = "2010", editor = "Emilio Corchado and Paulo Novais and Cesar Analide and Javier Sedano", volume = "73", series = "Advances in Intelligent and Soft Computing", pages = "197--200", address = "Guimaraes, Portugal", month = jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-642-13160-8", DOI = "doi:10.1007/978-3-642-13161-5_25", abstract = "This paper examines the use of artificial intelligence (in particular the application of Gene Expression Programming, GEP) to demand forecasting. In the world of production management, many data that are produced in function of the of economic activity characteristics in which they belong, may suffer, for example, significant impacts of seasonal behaviours, making the prediction of future conditions difficult by means of methods commonly used. The GEP is an evolution of Genetic Programming,which is part of the Genetic Algorithms. GEP seeks for mathematical functions, adjusting to a given set of solutions using a type of genetic heuristics from a population of random functions. In order to compare the GEP, we have used the others quantitatives method. Thus, from a data set of about demand of consumption of twelve products line metal fittings, we have compared the forecast data.", bibdate = "2010-11-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/softcomp/soco2010.html#BittencourtSLAO10", } @InProceedings{Bladek:2016:GECCOcomp, author = "Iwo Bladek and Krzysztof Krawiec", title = "Simultaneous Synthesis of Multiple Functions using Genetic Programming with Scaffolding", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", pages = "97--98", keywords = "genetic algorithms, genetic programming, scaffolding, multisynthesis, problem decomposition, Scala: Poster", month = "20-24 " # jul, organisation = "SIGEVO", address = "Denver, USA", publisher = "ACM", publisher_address = "New York, NY, USA", isbn13 = "978-1-4503-4323-7", poster_url = "https://www.cs.put.poznan.pl/ibladek/publications/conferences/gecco16_poster_simult.pdf", DOI = "doi:10.1145/2908961.2908992", size = "2 pages", abstract = "We consider simultaneous evolutionary synthesis of multiple functions, and verify whether such approach leads to computational savings compared to conventional synthesis of functions one-by-one. We also extend the proposed synthesis model with scaffolding, a technique originally intended to facilitate evolution of recursive programs \cite{Moraglio:2012:CEC}, and consisting in fetching the desired output from a test case, rather than calling a function. Experiment concerning synthesis of list manipulation programs in Scala allows us to conclude that parallel synthesis indeed pays off, and that engagement of scaffolding leads to further improvements.", notes = "NSGA-II https://github.com/kkrawiec/fuel Distributed at GECCO-2016.", } @InProceedings{Bladek:2017:EuroGP, author = "Iwo Bladek and Krzysztof Krawiec", title = "Evolutionary Program Sketching", booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming", year = "2017", editor = "Mauro Castelli and James McDermott and Lukas Sekanina", volume = "10196", series = "LNCS", pages = "3--18", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming, program synthesis, satisfiability modulo theory, program sketching", isbn13 = "978-3-319-55695-6", URL = "http://repozytorium.put.poznan.pl/publication/495662", DOI = "doi:10.1007/978-3-319-55696-3_1", size = "16 pages", abstract = "Program synthesis can be posed as a satisfiability problem and approached with generic SAT solvers. Only short programs can be however synthesized in this way. Program sketching by Solar-Lezama assumes that a human provides a partial program (sketch), and that synthesis takes place only within the uncompleted parts of that program. This allows synthesizing programs that are overall longer, while maintaining manageable computational effort. In this paper, we propose Evolutionary Program Sketching (EPS), in which the role of sketch provider is handed over to genetic programming (GP). A GP algorithm evolves a population of partial programs, which are being completed by a solver while evaluated. We consider several variants of EPS, which vary in program terminals used for completion (constants, variables, or both) and in the way the completion outcomes are propagated to future generations. When applied to a range of benchmarks, EPS outperforms the conventional GP, also when the latter is given similar time budget.", notes = "raport z badan Part of \cite{Castelli:2017:GP} EuroGP'2017 held in conjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017", } @Article{Bladek:EC, author = "Iwo Bladek and Krzysztof Krawiec and Jerry Swan", title = "Counterexample-Driven Genetic Programming: Heuristic Program Synthesis from Formal Specifications", journal = "Evolutionary Computation", year = "2018", volume = "26", number = "3", pages = "441--469", month = "Fall", keywords = "genetic algorithms, genetic programming, formal verification, counterexamples, SMT", ISSN = "1063-6560", DOI = "doi:10.1162/evco_a_00228", size = "27 pages", abstract = "Conventional genetic programming (GP) can only guarantee that synthesized programs pass tests given by the provided input-output examples. The alternative to such test-based approach is synthesizing programs by formal specification, typically realized with exact, non-heuristic algorithms. In this paper, we build on our earlier study on Counterexample-Based Genetic Programming (CDGP), an evolutionary heuristic that synthesizes programs from formal specifications. The candidate programs in CDGP undergo formal verification with a Satisfiability Modulo Theory (SMT) solver, which results in counterexamples that are subsequently turned into tests and used to calculate fitness. The original CDGP is extended here with a fitness threshold parameter that decides which programs should be verified, a more rigorous mechanism for turning counterexamples into tests, and other conceptual and technical improvements. We apply it to 24 benchmarks representing two domains: the linear integer arithmetic (LIA) and the string manipulation (SLIA) problems, showing that CDGP can reliably synthesize provably correct programs in both domains. We also confront it with two state-of-the art exact program synthesis methods and demonstrate that CDGP effectively trades longer synthesis time for smaller program size.", notes = "Extended Paper Invited from GECCO 2017", } @InProceedings{Bladek:2019:GECCO, author = "Iwo Bladek and Krzysztof Krawiec", title = "Solving symbolic regression problems with formal constraints", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", pages = "977--984", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, symbolic regression, constraints, formal verification, generalization", isbn13 = "978-1-4503-6111-8", URL = "https://www.cs.put.poznan.pl/ibladek/publications/conferences/gecco19_srfc_paper.pdf", DOI = "doi:10.1145/3321707.3321743", size = "8 pages", abstract = "In many applications of symbolic regression, domain knowledge constrains the space of admissible models by requiring them to have certain properties, like monotonicity, convexity, or symmetry. As only a handful of variants of genetic programming methods proposed to date can take such properties into account, we introduce a principled approach capable of synthesizing models that simultaneously match the provided training data (tests) and meet user-specified formal properties. To this end, we formalize the task of symbolic regression with formal constraints and present a range of formal properties that are common in practice. We also conduct a comparative experiment that confirms the feasibility of the proposed approach on a suite of realistic symbolic regression benchmarks extended with various formal properties. The study is summarized with discussion of results, properties of the method, and implications for symbolic regression.", notes = "Also known as \cite{3321743} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @PhdThesis{bladek:thesis, author = "Iwo Bladek", title = "Machine Learning and Formal Verification for Acquisition of Knowledge in Heuristic Program Synthesis", school = "Politechnika Poznanska", year = "2022", address = "Poznan, Poland", keywords = "genetic algorithms, genetic programming, Counterexample-Driven Genetic Programming, CDGP, Counterexample Driven Symbolic Regression, CDSR, Neuro-Guided Genetic Programming, ANN, Evolutionary Program Sketching, EPS, SMT, formal verification of software, machine learning, program synthesis, programowanie genetyczne, formalna weryfikacja oprogramowania, uczenie maszynowe, synteza programow", language = "en", URL = "https://sin.put.poznan.pl/dissertations/details/d2987", URL = "http://www.cs.put.poznan.pl/ibladek/publications/phdthesis.pdf", size = "159 pages", abstract = "we present and evaluate our techniques for efficient heuristic program synthesis based on genetic programming (GP). The common theme among our approaches is that various kinds of information (knowledge) are collected during runtime or a separate training phase, and then are used to guide GP search. Three of the described techniques, i.e., Evolutionary Program Sketching, Counterexample-Driven GP, and Counterexample-Driven Symbolic Regression, use formal verification/synthesis to either find locally optimal code fragments, or discover counterexamples exposing incorrect behavior of candidate programs. The fourth approach, Neuro-Guided GP, uses machine learning to learn the probability distribution of program instructions given input-output examples, and then uses it to bias variation operators of GP. The computational experiments show that all presented methods outperform or provide some advantages over existing state of the art methods.", abstract_pl = "W rozprawie przedstawiamy i analizujemy opracowane przez nas techniki efektywnej heurystycznej syntezy programow opartej o programowanie genetyczne (GP). Wspolna cecha tych technik jest zdobywanie roznych rodzajow informacji w trakcie dzialania algorytmu lub odrebnej fazie uczenia. Trzy z opisanych podejsc, Evolutionary Program Sketching, Counterexample-Driven GP i Counterexample-Driven Symbolic Regression, korzystaja z formalnej weryfikacji/syntezy w celu znajdowania lokalnie optymalnych fragmentow kodu lub odkrywania kontrprzykladow prezentujacych niepoprawne dzialanie generowanych programow. Czwarte podejscie, Neuro-Guided GP, korzysta z technik uczenia maszynowego w celu odkrycia rozkladu prawdopodobienstwa instrukcji na podstawie przykladow wejscie-wyjscie, a nastepnie wykorzystuje go do ukierunkowania operatorow przeszukiwania w GP. Eksperymenty obliczeniowe wykazaly, ze prezentowane metody sa konkurencyjne i oferuja wiele zalet w porownaniu do rozwiazan dostepnych w literaturze.", notes = "Also known as \cite{Bladek2022:d2987} On-line catalog to2022000422 Supervisor: Krzysztof Krawiec", } @Article{Bladek:ieeeTEC, author = "Iwo Bladek and Krzysztof Krawiec", title = "Counterexample-Driven Genetic Programming for Symbolic Regression with Formal Constraints", journal = "IEEE Transactions on Evolutionary Computation", year = "2023", volume = "27", number = "5", pages = "1327--1339", month = oct, keywords = "genetic algorithms, genetic programming, Satisfiability Modulo Theories, SMT, Symbolic regression, SR", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2022.3205286", size = "13 pages", abstract = "In symbolic regression with formal constraints, the conventional formulation of regression problem is extended with desired properties of the target model, like symmetry, monotonicity, or convexity. We present a genetic programming algorithm that solves such problems using a Satisfiability Modulo Theories solver to formally verify the candidate solutions. The essence of the method consists in collecting the counter examples resulting from model verification and using them to improve search guidance. The method is exact: upon successful termination, the produced model is guaranteed to meet the specified constraints. We compare the effectiveness of the proposed method with standard constraint-agnostic machine learning regression algorithms on a range of benchmarks, and demonstrate that it outperforms them on several performance indicators.", notes = "also known as \cite{9881536} Institute of Computing Science, Poznan University of Technology, Poznan, Poland", } @InProceedings{Blair:2013:CEC, author = "Alan Blair", title = "Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination", booktitle = "2013 IEEE Conference on Evolutionary Computation", volume = "1", year = "2013", month = jun # " 20-23", editor = "Luis Gerardo {de la Fraga}", pages = "605--612", address = "Cancun, Mexico", keywords = "genetic algorithms, genetic programming, HERCL, Evolutionary computation", isbn13 = "978-1-4799-0453-2", URL = "http://www.cse.unsw.edu.au/~blair/pubs/2013BlairCEC.pdf", DOI = "doi:10.1109/CEC.2013.6557624", article_id = "1135", size = "8 pages", abstract = "We describe a new programming language called HERCL, designed for evolutionary computation with the specific aim of allowing new programs to be created by combining patches of code from different parts of other programs, at multiple scales. Large-scale patches are followed up by smaller-scale patches or mutations, recursively, to produce a global random search strategy known as hierarchical evolutionary re-combination. We demonstrate the proposed system on the task of learning to encode with the Caesar or Vigenere Cipher, and show how the evolution of one task may fruitfully be cross-pollinated with evolved solutions from other related tasks.", notes = "Also known as \cite{6557624} CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.", } @InProceedings{Blair:2014:GECCOcomp, author = "Alan Blair", title = "Incremental evolution of HERCL programs for robust control", booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion", year = "2014", editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, artificial life, robotics, and evolvable hardware: Poster", pages = "27--28", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "http://doi.acm.org/10.1145/2598394.2598424", DOI = "doi:10.1145/2598394.2598424", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "We explore the evolution of programs for control tasks using the recently introduced Hierarchical Evolutionary Re-Combination Language (HERCL) which has been designed as an austere and general-purpose language, with a view toward modular evolutionary computation, combining elements from Linear GP with stack-based operations from FORTH. We show that HERCL programs can be evolved to robustly perform a benchmark double pole balancing task from a specified range of initial conditions, with the poles remaining balanced for up to an hour of simulated time.", notes = "Also known as \cite{2598424} Distributed at GECCO-2014.", } @InProceedings{Blair:2019:evomusart, author = "Alan Blair", title = "Adversarial Evolution and Deep Learning - How Does an Artist Play with Our Visual System?", booktitle = "8th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMusArt 2019", year = "2019", editor = "Aniko Ekart and Antonios Liapis and Luz Castro", series = "LNCS", volume = "11453", publisher = "Springer", pages = "18--34", address = "Leipzig, Germany", month = "24-26 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, ANN, Evolutionary art, ai-generated art, Artist-critic coevolution, Adversarial training, Computational creativity", isbn13 = "978-3-030-16666-3", DOI = "doi:10.1007/978-3-030-16667-0_2", abstract = "We create artworks using adversarial coevolution between a genetic program (hercl) generator and a deep convolutional neural network (LeNet) critic. The resulting artificially intelligent artist, whimsically named Hercule LeNet, aims to produce images of low algorithmic complexity which nevertheless resemble a set of real photographs well enough to fool an adversarially trained deep learning critic modelled on the human visual system. Although it is not exposed to any pre-existing art, or asked to mimic the style of any human artist, nevertheless it discovers for itself many of the stylistic features associated with influential art movements of the 19th and 20th Century. A detailed analysis of its work can help us to better understand the way an artist plays with the human visual system to produce aesthetically appealing images.", notes = "Cover SIGEvolution 12(2) \cite{Ochoa:2019:sigevolution}. EvoMusArt2019 held in conjunction with EuroGP'2019 EvoCOP2019 and EvoApplications2019 http://www.evostar.org/2019/cfp_evomusart.php", } @Article{Blasco:2021:JSS, author = "Daniel Blasco and Jaime Font and Mar Zamorano and Carlos Cetina", title = "An evolutionary approach for generating software models: The case of {Kromaia} in Game Software Engineering", journal = "Journal of Systems and Software", year = "2021", volume = "171", pages = "110804", month = jan, note = "Winner Bronze HUMIES", keywords = "genetic algorithms, genetic programming, SBSE, MDE, Model-Driven Engineering, Search-based software engineering, Game Software Engineering", ISSN = "0164-1212", URL = "http://www.human-competitive.org/sites/default/files/blasco-font-zamorano-cetina-text.txt", URL = "http://www.human-competitive.org/sites/default/files/blasco_jss_2021_pre_0.pdf", URL = "https://www.sciencedirect.com/science/article/pii/S0164121220302089", DOI = "doi:10.1016/j.jss.2020.110804", video_url = "http://www.human-competitive.org/sites/default/files/font_for_humies.107mb.mp4", video_url = "https://youtu.be/2z9FcKKB70w", size = "26 pages", abstract = "In the context of Model-Driven Engineering applied to video games, software models are high-level abstractions that represent source code implementations of varied content such as the stages of the game, vehicles, or enemy entities (e.g., final bosses). we present our Evolutionary Model Generation (EMoGen) approach to generate software models that are comparable in quality to the models created by human developers. Our approach is based on an evolution (mutation and crossover) and assessment cycle to generate the software models. We evaluated the software models generated by EMoGen in the Kromaia video game, which is a commercial video game released on Steam and PlayStation 4. Each model generated by EMoGen has more than 1000 model elements. The results, which compare the software models generated by our approach and those generated by the developers, show that our approach achieves results that are comparable to the ones created manually by the developers in the retail and digital versions of the video game case study. However, our approach only takes five hours of unattended time in comparison to ten months of work by the developers. We perform a statistical analysis, and we make an implementation of EMoGen readily available.", notes = "Cyberpunk 2077: how 2020's biggest video game launch turned into a shambles. Many bugs and glitches on (delayed) release games was unplayable. Refunded unhappy players. Crunch. International game developers association. game agents Vermis, Boss 1300 model elements, 15000 model properties. Developer write software model (of agent) rather than C++, model is compilable. GP 6:00 noise, so apply GP to model boss (6:28 genetic modeling) UML. Six quality indicators. Interest from Kraken empire and Tequila works, King 2021 HUMIES prize giving video https://www.youtube.com/watch?v=jrT0sfq6WjM 43:10 -- 47:08 computer 5 hours == human 10 months", } @InProceedings{Blasco:2010:ARES, author = "Jorge Blasco and Agustin Orfila and Arturo Ribagorda", title = "Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming", booktitle = "International Conference on Availability, Reliability, and Security, ARES '10", year = "2010", month = feb, pages = "327--332", abstract = "One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have been systematically ignored. To avoid the blind use of GP shown in previous research, we guide the search by means of a fitness function based on recent advances on IDS evaluation. For the experimental work we use a well-known dataset (i.e. KDD-99) that has become a standard to compare research although its drawbacks. Results clearly show that an intelligent use of GP achieves systems that are comparable (and even better in realistic conditions) to top state-of-the-art proposals in terms of effectiveness, improving them in efficiency and simplicity.", keywords = "genetic algorithms, genetic programming, domain-aware genetic programming, fitness function, intrusive traffic, network intrusion detection, normal traffic, security of data", DOI = "doi:10.1109/ARES.2010.53", notes = "Also known as \cite{5438073}", } @InProceedings{bleuler:2001:mgprbus, author = "Stefan Bleuler and Martin Brack and Lothar Thiele and Eckart Zitzler", title = "Multiobjective Genetic Programming: Reducing Bloat Using SPEA2", booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001", year = "2001", pages = "536--543", address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea", publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA", month = "27-30 " # may, organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming, SPEA, SPEA2, Pareto, external set, bloating, compact programs, convergence speed, even-parity problem, multiobjective evolutionary technique, multiobjective genetic programming, program functionality, convergence, programming theory", ISBN = "0-7803-6658-1", URL = "ftp://ftp.tik.ee.ethz.ch/pub/people/zitzler/BBTZ2001b.ps.gz", URL = "http://citeseer.ist.psu.edu/443099.html", DOI = "doi:10.1109/CEC.2001.934438", size = "8 pages", abstract = "This study investigates the use of multiobjective techniques in Genetic Programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The proposed approach considers the program size as a second, independent objective besides the program functionality. In combination with a recent multiobjective evolutionary technique, SPEA2, this method outperforms four other strategies to reduce bloat with regard to both convergence speed and size of the produced programs on a even-parity problem.", notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = Solutions to even-9 parity.", } @InCollection{Bleuler:2008:MPSN, author = "Stefan Bleuler and Johannes Bader and Eckart Zitzler", title = "Reducing Bloat in GP with Multiple Objectives", booktitle = "Multiobjective Problem Solving from Nature: from concepts to applications", publisher = "Springer", year = "2008", editor = "Joshua Knowles and David Corne and Kalyanmoy Deb", series = "Natural Computing", chapter = "9", pages = "177--200", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-72963-1", DOI = "doi:10.1007/978-3-540-72964-8_9", abstract = "This chapter investigates the use of multiobjective techniques in genetic programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The underlying approach considers the program size as a second, independent objective besides program functionality, and several studies have found this concept to be successful in reducing bloat. Based on one specific algorithm, we demonstrate the principle of multiobjective GP and show how to apply Pareto-based strategies to GP. This approach outperforms four classical strategies to reduce bloat with regard to both convergence speed and size of the produced programs on an even-parity problem. Additionally, we investigate the question of why the Pareto-based strategies can be more effective in reducing bloat than alternative strategies on several test problems. The analysis falsifies the hypothesis that the small but less functional individuals that are kept in the population act as building blocks building blocks for larger correct solutions. This leads to the conclusion that the advantages are probably due to the increased diversity in the population.", notes = "http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-173745027-0", } @InProceedings{BT94, author = "Tobias Blickle and Lothar Thiele", title = "Genetic Programming and Redundancy", booktitle = "Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94, Saarbr{\"u}cken)", editor = "J. Hopf", publisher = "Max-Planck-Institut f{\"u}r Informatik (MPI-I-94-241)", address = " Im Stadtwald, Building 44, D-66123 Saarbr{\"u}cken, Germany ", pages = "33--38", year = "1994", keywords = "genetic algorithms, genetic programming", URL = "http://www.tik.ee.ethz.ch/~tec/publications/bt94/GPandRedundancy.ps.gz", size = "6 pages", abstract = "The Genetic Programming optimization method (GP) elaborated by John Koza [Koza, 1992] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an exchange of subtrees. Empirical analyses show that large parts of those trees are never used or evaluated which means that these parts of the trees are irrelevant for the solution or redundant. This paper is concerned with the identification of the redundancy occurring in GP. It starts with a mathematical description of the behaviour of GP and the conclusions drawn from that description among others explain the size problem which denotes the phenomenon that the average size of trees in the population grows with time.", notes = "From GP list Wed, 22 Mar 95 we did some work on the convergence problem and the redundancy in the trees in GP. It turned out that bloating is a property of GP that arises from the fact that more redundant trees have a higher probability to survive crossover. As a result, the redundant part of the trees grow bigger and bigger because the increased proportion of redundant cut-sites in the tree again lead to a higher probability to survive crossover. Gives a formula for tournament size related to proportion of crossover in a generational GP. Ie recommending T=10 for pc=0.9. This does not apply to steady state GA. ", } @TechReport{blickle:1995:css, author = "Tobias Blickle and Lothar Thiele", title = "A Comparison of Selection Schemes Used in Genetic Algorithms", institution = "TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology", year = "1995", type = "TIK-Report", number = "11", edition = "2", address = "Gloriastrasse 35, 8092 Zurich, Switzerland", month = dec, keywords = "genetic algorithms, genetic programming", URL = "https://tik-old.ee.ethz.ch/file/6c0e384dceb283cd4301339a895b72b8/TIK-Report11.pdf", broken = "http://www.handshake.de/user/blickle/publications/TIK-Report11abstract.html", URL = "http://www.handshake.de/user/blickle/publications/tik-report11_v2.ps", size = "65 pages", abstract = " Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which a new description model is introduced in this paper. With this a mathematical analysis of tournament selection, truncation selection, linear and exponential ranking selection and proportional selection is carried out that allows an exact prediction of the fitness values after selection. The further analysis derives the selection intensity, selection variance, and the loss of diversity for all selection schemes. For completion a pseudo- code formulation of each method is included. The selection schemes are compared and evaluated according to their properties leading to an unified view of these different selection schemes. Furthermore the correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proven.", notes = " Of special interest for the GP community may be the fact that in this report three analytic approximation formulas are obtained using GP for symbolic regression. The method is described in appendix A of the report. Second (extended and corrected) edition available via www and ftp Dec 1995 ", } @Article{blickle:1995:ea, author = "Tobias Blickle", title = "Optimieren nach dem Vorbild der Natur, Evolutionare Algorithmen", journal = "Bulletin SEV/VSE", year = "1995", volume = "86", number = "25", pages = "21--26", keywords = "genetic algorithms, genetic programming", URL = "http://www.handshake.de/user/blickle/publications/EA.ps", size = "6 pages", notes = "Introduction to GA and GP in German", } @TechReport{blickle:1995:YAGPLIC, author = "Tobias Blickle", title = "YAGPLIC User Manual", institution = "Computer Engineering and Communication Network Lab (TIK), Swiss Federal Institute of Technology (ETH)", year = "1995", address = "Gloriastrasse 35, CH-8092, Zurich", keywords = "genetic algorithms, genetic programming", broken = "http://www.tik.ee.ethz.ch/~blickle/YAGPLIC.html", notes = "Yet Another Genetic Programming Library In C Written in C for maximum performance. Object-oriented user-interface. Up to 32 data types possible in a tree and type-consistent crossover. Several selection schemes implemented: proportionate selection, ranking selection, tournament selection, truncation selection. Extensive output of statistical data for post processing with MATHEMATICA.", } @TechReport{blickle:1996:ecs, author = "Tobias Blickle", title = "Evolving Compact Solutions in Genetic Programming: A Case Study", institution = "TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology", year = "1996", type = "TIK-Report", address = "Gloriastrasse 35, 8092 Zurich, Switzerland", keywords = "genetic algorithms, genetic programming", URL = "http://www.handshake.de/user/blickle/publications/ppsn1.ps", abstract = "Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence $y=g({\bf x})$ that approximates a set of data points (${\bf x_i},y_i$). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.", notes = "Presented at PPSN 4 ", size = "10 pages", } @InProceedings{blickle96, author = "Tobias Blickle", title = "Evolving Compact Solutions in Genetic Programming: A Case Study", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "564--573", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "genetic algorithms, genetic programming, bloat, deleting crossover", ISBN = "3-540-61723-X", URL = "http://www.handshake.de/user/blickle/publications/ppsn1.ps", URL = "http://citeseer.ist.psu.edu/blickle96evolving.html", DOI = "doi:10.1007/3-540-61723-X_1020", size = "10 pages", abstract = "Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y=g ( x ) that approximates a set of data points ( x i , y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 same as \cite{blickle:1996:ecs} Test of effectiveness of GP, EDI, deleting and adaptive anti-bloat techniques. Results differ continuous (symbolic regression) v. discrete 6-mux deleting crossover similar to code editing based on code interpretation during fitness evaluation.", affiliation = "Swiss Federal Institute of Technology (ETH) Computer Engineering and Communication Networks Lab (TIK) Gloriastrasse 35 8092 Zurich Switzerland Gloriastrasse 35 8092 Zurich Switzerland", } @PhdThesis{blickle:thesis, author = "Tobias Blickle", title = "Theory of Evolutionary Algorithms and Application to System Synthesis", school = "Swiss Federal Institute of Technology", year = "1996", address = "Zurich, Switzerland", publisher = "vdf Verlag", publisher_address = "CH-8092 Zurich", month = nov, keywords = "genetic algorithms, genetic programming", ISBN = "3-7281-2433-8", URL = "http://www.handshake.de/user/blickle/publications/diss.pdf", DOI = "doi:10.3929/ethz-a-001710359", size = "272 pages", abstract = "The subject of this thesis are Evolutionary Algorithms and their application to the automated synthesis and optimization of complex digital systems composed of hardware and software elements. In Part I the probabilistic optimization method of Evolutionary Algorithms (EAs) is presented. EAs apply the principles of natural evolution (selection and random variation) to a random set of points (population of individuals) in the search space. Evolutionary Algorithms are first embedded in the context of global optimization and the most important and widely used methods for constraint- handling are introduced, including a new method called IOS (individual objective switching). This is followed by a new formal description of selection schemes based on fitness distributions. This description enables an extensive and uniform examination of various selection schemes leading to new insights about the impact of the selection method parameters on the optimization process. Subsequently the variation (recombination) process of Evolutionary Algorithms is examined. As different analysis techniques are necessary depending on the representation of the problem (e.g. bit string, vector, tree, graph) only the recombination process for tree-representation (Genetic Programming) is considered. A major part of the explanation treats the problem of ``bloating'', i.e., the tree-size increase during optimization. Furthermore, a new redundancy based explanation of bloating is given and several methods to avoid bloating are compared. Part II is dedicated to the application of Evolutionary Algorithms to the optimization of complex digital systems. These systems are composed of hardware and software components and characterized by a high complexity caused by their heterogeneity (hardware/ software, electrical/mechanical, analog/digital). Computer-aided synthesis at the abstract system level is advantageous for application specific systems or embedded systems as it enables time-to-market to be reduced with a decrease in design errors and costs. The main task of system-synthesis is the transformation of a behavioral specification (for example given by an algorithm) into a structural specification, such as a composition of processors, general or dedicated hardware modules, memories and busses, while regarding various restrictions, e.g. maximum costs, data throughput rate, reaction time. Problems related to system synthesis are for example performance estimation, architecture optimization and design-space exploration. This thesis introduces a formal description of system-synthesis based on a new graph model where the specification is translated into a specification graph. The main tasks of system-synthesis (allocation, binding and scheduling) are defined for this graph and the process of system synthesis is formulated as a constrained global optimization problem. This optimization problem is solved by Evolutionary Algorithms using the results of Part I of the thesis. Finally, an example of synthesizing implementations of a video codec chip H.261 is described demonstrating the effectiveness of the proposed methodology and the capability of the EA to obtain the Pareto points of the design space in a single optimization run.", notes = "Of special interest for this community might be chapter 5 that deals with recombination and bloating in GP YAGPLIC Supervisor: Lothar Thiele", } @Article{DBLP:journals/ec/BlickleT96, author = "Tobias Blickle and Lothar Thiele", title = "A Comparison of Selection Schemes used in Evolutionary Algorithms", journal = "Evolutionary Computation", volume = "4", number = "4", year = "1996", pages = "361--394", bibsource = "DBLP, http://dblp.uni-trier.de", month = "Winter", keywords = "genetic algorithms, genetic programming, Selection, evolutionary algorithms, diversity, selection intensity, tournament selection, truncation selection, linear ranking", ISSN = "1063-6560", URL = "http://www.handshake.de/user/blickle/publications/ECfinal.ps", DOI = "doi:10.1162/evco.1996.4.4.361", size = "34 pages", abstract = "Evolutionary algorithms are a common probabilistic optimisation method based on the model of natural evolution. One important operator in these algorithms is the selection scheme, for which in this paper a new description model, based on fitness distributions, is introduced. With this, a mathematical analysis of tournament selection, truncation selection, ranking selection, and exponential ranking selection is carried out that allows an exact prediction of the fitness values after selection. The correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proved. Furthermore, several properties of selection schemes are derived (selection intensity, selection variance, loss of diversity), and the three selection schemes are compared using these properties.", notes = "Brief use of GP symbolic regression to find nice formulae. Theoretical analysis. NB see \cite{DBLP:journals/ec/Motoki02} for update on loss of diversity under tournament selection", } @InProceedings{Blot:2019:GI, author = "Aymeric Blot", title = "Fuzzy Edit Sequences in Genetic Improvement", booktitle = "GI-2019, ICSE workshops proceedings", year = "2019", editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer", pages = "30--31", address = "Montreal", month = "28 " # may, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, SBSE, search-based software engineering, fuzzy matching", isbn13 = "978-1-7281-2268-7", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019.pdf", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/blot2019gi.pdf", DOI = "doi:10.1109/GI.2019.00016", size = "2 pages", abstract = "Genetic improvement uses automated search to find improved versions of existing software. Edit sequences have been proposed as a very convenient way to represent code modifications, focusing on the changes themselves rather than duplicating the entire program. However, edits are usually defined in terms of practical operations rather than in terms of semantic changes; indeed, crossover and other edit sequence mutations usually never guarantee semantic preservation. We propose several changes to usual edit sequences, specifically augmenting edits with content data and using fuzzy matching, in an attempt to improve semantic preservation.", notes = "GI-2019 http://geneticimprovementofsoftware.com Slides: http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019_slides.pdf part of \cite{Petke:2019:ICSEworkshop}", } @InProceedings{Blot:2019:GI7, author = "Aymeric Blot and Justyna Petke", title = "On Adaptive Specialisation in Genetic Improvement", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher_address = "New York, NY, USA", publisher = "ACM", address = "Prague, Czech Republic", pages = "1703--1704", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Search-Based Software Engineering, Software Specialisation, Algorithm Selection, Algorithm Configuration", isbn13 = "978-1-4503-6748-6", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-gecco_2019.pdf", DOI = "doi:10.1145/3319619.3326839", size = "2 pages", abstract = "Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve its efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history.", notes = "MiniSAT Also known as \cite{Blot:2019:GECCOcomp} Also known as \cite{3326839} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Blot:2020:EuroGP, author = "Aymeric Blot and Justyna Petke", title = "Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement Case Study: Improvement of {MiniSAT's} Running Time", booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming", year = "2020", month = "15-17 " # apr, editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12101", publisher = "Springer Verlag", address = "Seville, Spain", pages = "68--83", organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Genetic improvement, GI, GP, Search based software engineering, SBSE, Boolean satisfiability, SAT, minisat-2.2.0, linux perf, AST, XML", isbn13 = "978-3-030-44093-0", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_eurogp_2020.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/10097404", video_url = "https://www.youtube.com/watch?v=D-UQr-P3zUQ", DOI = "doi:10.1007/978-3-030-44094-7_5", abstract = "Genetic improvement (GI) uses automated search to findimproved versions of existing software. While most GI work use geneticprogramming (GP) as the underlying search process, focus is usuallygiven to the target software only. As a result, specifics of GP algorithmsfor GI are not well understood and rarely compared to one another. In this work, we propose a robust experimental protocol to compare different GI search processes and investigate several variants of GP and random-based approaches. Through repeated experiments, we report acomparative analysis of these approaches, using one of the previously used GI scenarios: improvement of runtime of the MiniSAT satisfiability solver. We conclude that the test suites used have the most significant impact on the GI results. Both random and GP-based approaches are able to find improved software, even though the percentage of viable software variants is significantly smaller in the random case (14.5percent vs. 80.1percent). We also report that GI produces MiniSAT variants up to twice as fast as the original on sets of previously unseen instances from the same application domain.", notes = "slides http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_eurogp_2020_slides.pdf PyGGI https://github.com/coinse/pyggi minisat 2. 80.1pecent of mutants GP compile, run, produce right answer. About 70percent faster than original, based on instruction count (slide 13). But... (slide 14) over fitting. Hetrogenous SAT dataset: very easy to hard. video 20 mins + questions Figure 2: 5-fold cross validation. CentOS-7 Linux kernel 3.10.0 and GCC 4.8.5. 130 combinatorial interaction testing (CIT) instances. SrcML AST https://www.srcml.org/ also known as \cite{DBLP:conf/eurogp/BlotP20} http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020", } @InProceedings{Blot:2020:GIsp, author = "Aymeric Blot and Justyna Petke", title = "Stack-Based Genetic Improvement", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "289--290", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GI, Automated Program Repair, APR, Search-based software engineering", isbn13 = "978-1-4503-7963-2", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-1.pdf", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392174", size = "2 pages", abstract = "Genetic improvement (GI) uses automated search to find improved versions of existing software. If originally GI directly evolved populations of software, most GI work nowadays use a solution representation based on a list of mutations. This representation however has some limitations, notably in how genetic material can be re-combined. We introduce a novel stack-based representation and discuss its possible benefits", notes = "Push, different stacks for each data and each? code type. RIP Larry Tesler 1945 to 2020: inventor of cut/copy & paste (and more) EP/P023991/1 Video: https://youtu.be/GsNKCifm44A (start 1:30:29 end 1:45:45) Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/synthetic_benchmarks_slides.pdf http://geneticimprovementofsoftware.com/gi2020icse.html", } @InProceedings{Blot:2020:GI, author = "Aymeric Blot and Justyna Petke", title = "Synthetic Benchmarks for Genetic Improvement", booktitle = "GI @ ICSE 2020", year = "2020", month = "3 " # jul, editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce", publisher = "ACM", address = "internet", pages = "287--288", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE", isbn13 = "978-1-4503-7963-2", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2020-2.pdf", video_url = "https://youtu.be/GsNKCifm44A", DOI = "doi:10.1145/3387940.3392175", size = "2 pages", abstract = "Genetic improvement (GI) uses automated search to find improved versions of existing software. If over the years the potential of many GI approaches have been demonstrated, the intrinsic cost of evaluating real-world software makes comparing these approaches in large-scale meta-analyses very expensive. We propose and describe a method to construct synthetic GI benchmarks, to circumvent this bottleneck and enable much faster quality assessment of GI approaches.", notes = "fitness is single query of surogate model using: AST, run-time, testing. Model based on many mutations of the original program Video: https://youtu.be/GsNKCifm44A (start 2:12:38, 2:22:36 end 2:28:15) Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/synthetic_benchmarks_slides.pdf http://geneticimprovementofsoftware.com/gi2020icse.html", } @Article{blot:2021:tevc, author = "Aymeric Blot and Justyna Petke", title = "Empirical Comparison of Search Heuristics for Genetic Improvement of Software", journal = "IEEE Transactions on Evolutionary Computation", year = "2021", volume = "25", number = "5", pages = "1001--1011", month = oct, keywords = "genetic algorithms, genetic programming, genetic improvement, GI, Search-Based Software Engineering, SBSE, Stochastic Local Search, PyGGI, Uniform interleaved crossover", ISSN = "1089-778X", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/publis/#blot:2021:tevc", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_tevc_2021.pdf", code_url = "https://github.com/bloa/tevc_2020_artefact", DOI = "doi:10.1109/TEVC.2021.3070271", size = "11 pages", abstract = "Genetic improvement uses automated search to improve existing software. It has been successfully used to optimise various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. Genetic improvement typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this paper, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of 8 improvement scenarios. Additionally, we also provide new genetic improvement benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than genetic programming approaches. Moreover, improvements were found in all scenarios (between 15percent and 68percent). A replication package can be found online: https://github.com/bloa/tevc_2020_artefact.", notes = "Supplement http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_tevc_2021_supp.pdf computational speed: 'number of CPU instructions as measured by the Linux perf' Table 3 'at least 5 percent faster than the original software'. Over fitting: 'On average, 84 percent of the significant improvements confirmed during validation are also significant in the entire dataset' Table IV numbers less than 100 represent speed up. 'Speedups from 15 percent to 68 percent can be found for all scenarios'. 'the search space is largely neutral'. 'new benchmarks and target software for non-functional genetic improvement'. MiniSAT (two both C++), Sat4j (Java), Combinatorial Interaction Testing CIT, SATLIB. OptiPNG (three all C), MOEA/D (C++) and NSGA-II (C++). Random search. GP (pop=100) crossover mutation. Four types of GP crossover: Concatenation, Single-point, Uniform concatenation, Uniform interleaved. Mutation: deletion, append. GP: ' best individual in the last generation', 'the best individual evaluated over the entire search'. 'eassessed over a larger subset of [test] inputs. Local search (hill climbing, iterative improvement): mutation only. first improvement, best improvement, and tabu search. Neutral moved accepted. No restarts. unnecessary edits. 'reasonable fixed-size subsets of S neighbours of the neighbourhood of the current solution'. 'fixed-length tabu list'. 'two-stage validation step.' k-fold cross-validation. Centos-7 multi core 3.4GHz Intel i7-2600, 16GB ram, GCC 4.8.5. 'local search and genetic programming found identical or semantically equivalent software variants in most cases'. 'all been manually verified to be semantically valid. NSGA-II 'several algorithmic changes are found'. 'Across all repetitions the best performances have been obtained in around 12 percent of all runs.' 'Local search approaches clearly outperform all other approaches on the test data.' 'genetic programming and random search perform similarly' !!!! 'GI performance could be significantly improved if better search approaches are considered'. 'GP use most of the training budged recombining known mutations, while local search have more opportunities to explore software variants.' also known as \cite{9392013}", } @InProceedings{blot:hal-03595447, author = "Aymeric Blot and Justyna Petke", title = "Using Genetic Improvement to Optimise Optimisation Algorithm Implementations", booktitle = "23{\`e}me congr{\`e}s annuel de la Soci{\'e}t{\'e} Fran{\c c}aise de Recherche Op{\'e}rationnelle et d'Aide {\`a} la D{\'e}cision, ROADEF'2022", year = "2022", editor = "Khaled Hadj-Hamou", address = "Villeurbanne - Lyon, France", month = "23--25 " # feb, organization = "INSA Lyon", keywords = "genetic algorithms, genetic programming, Genetic improvement, SBSE, Algorithm design, Software engineering, Optimisation, MOEA/D, NSGA-II, PyGGI , perf, IGD, XML, SrcML", URL = "https://hal.archives-ouvertes.fr/hal-03595447", pdf = "https://hal.archives-ouvertes.fr/hal-03595447/file/roadef_2022.pdf", hal_id = "hal-03595447", hal_version = "v1", URL = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_roadef_2022.pdf", size = "2 pages", abstract = "Genetic improvement (GI) \cite{Petke:gisurvey} uses automated search to improve existing software. It has been successfully used to fix software bugs or improve non-functional properties of software such as running time, memory usage, or energy consumption. Recently, it has been shown that genetic programming, the eponymous GI typical search algorithm, was outperformed by local search strategies \cite{blot:2021:tevc}. One result of that work was that GI was able to find interesting algorithmic changes in the implementation [Hui Li and Qingfu Zhang., 2009] of two state-of-the-art evolutionary algorithms, MOEA-D and NSGA-II. Here, we reproduce and extend this result with a simple local search, obtaining 10percent faster software variants with little to no impact on solution quality in 6/18 GI runs.", notes = "Slides http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_roadef_2022_slides.pdf runtime limited to 3:45:00, five fold cross validation, 12percent reduction in execution time at validation CMOEAD::calc_distance(). Overfitting issues BUT 'generalised most of the time (12/18)' https://roadef2022.sciencesconf.org/ https://roadef2022.sciencesconf.org/data/pages/LivretROADEF_2023.pdf", } @Misc{blot:2022:corr_1, author = "Aymeric Blot and Justyna Petke", title = "{MAGPIE}: Machine Automated General Performance Improvement via Evolution of Software", howpublished = "arXiv", year = "2022", month = "4 " # aug, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, parameter tuning, algorithm configuration, compiler optimisation, local search, multi-objective, running time, solution quality, metaheuristic-based search strategies, srcML, linear GP, GCC, LLVM, Clang, OpenJDK, GraalVM Java", URL = "https://arxiv.org/abs/2208.02811", DOI = "doi:10.48550/arxiv.2208.02811", code_url = "https://github.com/bloa/magpie", size = "19 pages", abstract = "Performance is one of the most important qualities of software. Several techniques have thus been proposed to improve it, such as program transformations, optimisation of software parameters, or compiler flags. Many automated software improvement approaches use similar search strategies to explore the space of possible improvements, yet available tooling only focuses on one approach at a time. This makes comparisons and exploration of interactions of the various types of improvement impractical. We propose MAGPIE, a unified software improvement framework. It provides a common edit sequence based representation that isolates the search process from the specific improvement technique, enabling a much simplified synergistic workflow. We provide a case study using a basic local search to compare compiler optimisation, algorithm configuration, and genetic improvement. We chose running time as our efficiency measure and evaluated our approach on four real-world software, written in C, C++, and Java. Our results show that, used independently, all techniques find significant running time improvements: up to 25percent for compiler optimisation, 97percent for algorithm configuration, and 61percent for evolving source code using genetic improvement. We also show that up to 10percent further increase in performance can be obtained with partial combinations of the variants found by the different techniques. Furthermore, the common representation also enables simultaneous exploration of all techniques, providing a competitive alternative to using each technique individually.", notes = "MiniSAT, LPG (v1.2 AClib), Sat4J, Weka (v3.8) random forrest, Dexter dataset. Written in Python 3.5+, MAGPIE is based on PyGGI 2.0 'Software is never done' 'Machine Automated General Performance Improvement through Evolution of software' 'Automated combination of the patches' (mutations) 'compiler and interpreter flag optimisation' Figure 3: Cross-validation with k-folding and holdout. p7 'only 10 training instances are actually used due to the very high evaluation cost' StmtDelete, StmtReplace, StmtInsert, ConstantSet, ConstantUpdate, ParamSet. fitness = CPU instructions (rather than CPU time). GraalVM disabled use of 'compressed ordinary object pointers'.", } @Misc{blot2022comprehensive, author = "Aymeric Blot and Justyna Petke", title = "A Comprehensive Survey of Benchmarks for Automated Improvement of Software's Non-Functional Properties", howpublished = "arXiv", year = "2022", month = "16 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark", eprint = "2212.08540", archiveprefix = "arXiv", primaryclass = "cs.SE", URL = "https://arxiv.org/abs/2212.08540", data_url = "https://bloa.github.io/nfunc_survey/", size = "43 pages", abstract = "Performance is a key quality of modern software. Although recent years have seen a spike in research on automated improvement of software's execution time, energy, memory consumption, etc., there is a noticeable lack of standard benchmarks for such work. It is also unclear how such benchmarks are representative of current software. Furthermore, frequently non-functional properties of software are targeted for improvement one-at-a-time, neglecting potential negative impact on other properties. In order to facilitate more research on automated improvement of non-functional properties of software, we conducted a survey gathering benchmarks used in previous work. We considered 5 major online repositories of software engineering work: ACM Digital Library, IEEE Xplore, Scopus, Google Scholar, and ArXiV. We gathered 5000 publications (3749 unique), which were systematically reviewed to identify work that empirically improves non-functional properties of software. We identified 386 relevant papers. We find that execution time is the most frequently targeted property for improvement (in 62 percent of relevant papers), while multi-objective improvement is rarely considered (5 percent). Static approaches are prevalent (in 53 percent of papers), with exploratory approaches (evolutionary in 18 percent and non-evolutionary in 14 percent of papers) increasingly popular in the last 10 years. Only 40 percent of 386 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (covered in 33 papers). We also provide recommendations for choice of benchmarks in future work, noting, e.g., lack of work that covers Python or JavaScript. We provide all programs found in the 386 papers on our dedicated web page https://bloa.github.io/nfunc_survey/ We hope that this effort will facilitate more research on the topic of automated improvement of software's non-functional properties.", notes = "See \cite{blot:2025:ACMsurveys}", } @InProceedings{Blot:2024:GI, author = "Aymeric Blot", title = "Automated Software Performance Improvement with {Magpie}", booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024", year = "2024", editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and Justyna Petke", pages = "ix", address = "Lisbon", month = "16 " # apr, publisher = "ACM", note = "Invited tutorial", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "979-8-4007-0573-1/24/04", URL = "http://gpbib.cs.ucl.ac.uk/gi2024/an_2024_GI.pdf", URL = "https://conf.researchr.org/details/icse-2024/gi-2024-papers/8/Automated-Software-Performance-Improvement-with-Magpie", DOI = "doi:10.1145/3643692", slides_url = "http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi@icse_2024_slides.pdf", video_url = "https://youtu.be/ysKDzJMac0Q", video_url = "https://www.youtube.com/watch?v=ysKDzJMac0Q&list=PLI8fiFpB7BoIRqJuY80XwmH-DFT_71y2S&index=5&pp=iAQB", code_url = "https://github.com/bloa/magpie", size = "1 page", abstract = "we present Magpie, a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes, as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie's internals before exploring diverse real-world scenarios. Aymeric Blot is a Senior Lecturer at the Universite of Rennes, France.", notes = "GI @ ICSE 2024, part of \cite{an:2024:GI}", } @Article{blot:2025:ACMsurveys, author = "Aymeric Blot and Justyna Petke", title = "A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties", journal = "ACM Computing Surveys", year = "2025", note = "in press", keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark", URL = "https://discovery.ucl.ac.uk/id/eprint/10203326/1/main.pdf", URL = "https://discovery.ucl.ac.uk/id/eprint/10203326/", data_url = "https://bloa.github.io/nfunc_survey", size = "35 pages", notes = "replaces \cite{blot2022comprehensive}", } @Proceedings{blot:2025:GI, title = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn Faulkner Rainford and Oliver Krauss", address = "Ottawa", month = "27 " # apr, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://geneticimprovementofsoftware.com/events/icse2025", URL = "http://gpbib.cs.ucl.ac.uk/gi2025/blot_2025_GI.pdf", size = "vi + 42 pages", abstract = "The GI workshops continue to bring together researchers from across the world to exchange ideas about using optimisation techniques, particularly evolutionary computation, such as genetic programming, to improve existing software. Contents: \cite{Tan:2025:GI} \cite{Blot_magpie:2025:GI} \cite{chan:2025:GI} \cite{langdon:2025:GI} \cite{songpetchmongkol:2025:GI}, \cite{bose:2025:GI}, \cite{wang:2025:GI}, \cite{bouras:2025:GI}, ", } @InProceedings{Blot_magpie:2025:GI, author = "Aymeric Blot", title = "Automated Software Performance Improvement with {Magpie}", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn Faulkner Rainford and Oliver Krauss", pages = "vi", address = "Ottawa", month = "27 " # apr, publisher = "IEEE", note = "Invited tutorial", keywords = "genetic algorithms, genetic programming, Genetic Improvement", URL = "http://gpbib.cs.ucl.ac.uk/gi2025/blot_2025_GI.pdf", code_url = "https://github.com/bloa/magpie", size = "1 page", abstract = "In this tutorial, I will present Magpie (https://github.com/bloa/magpie), a powerful tool for both Genetic Improvement researchers and practitioners. Magpie stands at the forefront of software evolution, providing a streamlined approach to model, evolve, and automatically improve software systems. Addressing both functional and non-functional concerns, Magpie offers a user-friendly no-code interface that seamlessly integrates various search processes, as well as enabling easy Python code injection for advanced users to further tailor and specialise the improvement process to meet their specific needs. We will provide a concise overview of Magpie internals before exploring diverse real-world scenarios.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI} see also \cite{Blot:2024:GI}", } @Proceedings{Blum:2013:GECCOcomp, title = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", address = "Amsterdam, The Netherlands", publisher_address = "New York, NY, USA", month = "6-10 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and Arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Introductory tutorials, Advanced tutorials, Specialized techniques and applications tutorials, Workshop on problem understanding and real-world optimisation, Symbolic regression and modeling workshop, Black box optimization benchmarking 2013 (BBOB 2013), Sixteenth international workshop on learning classifier systems, Evolutionary computation software systems (EvoSoft'13), Visualisation methods in genetic and evolutionary computation (VizGEC 2013), Evolutionary computation and multi-agent systems and simulation (EcoMass) seventh annual workshop, Medical applications of genetic and evolutionary computation (MedGEC'13), 3rd workshop on evolutionary computation for the automated design of algorithms, Green and efficient energy applications of genetic and evolutionary computation workshop, International workshop on evolutionary computation in bioinformatics, Stack-based workshop, Student workshop, Late-breaking abstracts, Keynote talks", isbn13 = "978-1-4503-1964-5", URL = "http://dl.acm.org/citation.cfm?id=2464576", notes = "Distributed at GECCO-2013.", } @Proceedings{Blum:2013:GECCO, title = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference", year = "2013", editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi", address = "Amsterdam, The Netherlands", publisher_address = "New York, NY, USA", month = "6-10 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Biological and biomedical applications, Digital entertainment technologies and arts, Estimation of distribution algorithms, Evolution strategies and evolutionary programming, Evolutionary combinatorial optimization and metaheuristics, Evolutionary multiobjective optimization, Generative and developmental systems, Genetics based machine learning, Integrative genetic and evolutionary computation, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory", isbn13 = "978-1-4503-1963-8", URL = "http://dl.acm.org/citation.cfm?id=2463372", notes = "GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)", } @InProceedings{blume:2000:ocfromsgesGLEAM, author = "Christian Blume", title = "Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software {GLEAM}", booktitle = "Real-World Applications of Evolutionary Computing", year = "2000", editor = "Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter and Terence C. Fogarty", volume = "1803", series = "LNCS", pages = "327--338", address = "Edinburgh", publisher_address = "Berlin", month = "17 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Industrial Machining Robots", ISBN = "3-540-67353-9", DOI = "doi:10.1007/3-540-45561-2_32", abstract = "The GLEAM algorithm and its implementation are a new evolutionary method application in the field of robotics. The GLEAM software generates control code for real industrial robots. Therefore GLEAM allows a time related description of the robot movement (not only a static description of robot arm configurations). This internal representation of primitive move commands is mapped to a representation of move statements of an industrial robot language, which can be loaded at the robot control and executed.", notes = "Robot command program is a vriable number of very high level command actions. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61", } @InCollection{bobrovnikoff:2000:SEISP, author = "Dmitri Bobrovnikoff", title = "{SoccerBots:} Evolving Intelligent Soccer Players", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "40--45", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @InProceedings{boden:1996:tsobGA, author = "Edward B. Boden and Gilford F. Martino", title = "Testing Software using Order-Based Genetic Algorithms", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Genetic Algorithms", pages = "461--466", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap76.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 GA paper", } @InProceedings{boettcher:1999:EOMC, author = "Stefan Boettcher and Allon G. Percus", title = "Extremal Optimization: Methods derived from Co-Evolution", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "825--832", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "evolution strategies and evolutionary programming", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/9904056.pdf", URL = "http://xxx.lanl.gov/abs/math.OC/9904056", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Boetticher:2006:IRI, author = "G. D. Boetticher and K. Kaminsky", title = "The Assessment and Application of Lineage Information in Genetic Programs for Producing Better Models", booktitle = "IEEE International Conference on Information Reuse and Integration", year = "2006", pages = "141--146", address = "Waikoloa Village, HI, USA", month = sep, publisher = "IEEE", keywords = "genetic algorithms, genetic programming", ISBN = "0-7803-9788-6", DOI = "doi:10.1109/IRI.2006.252403", abstract = "One of the challenges in data mining, and in particular genetic programs, is to provide sufficient coverage of the search space in order to produce an acceptable model. Traditionally, genetic programs generate equations (chromosomes) and consider all chromosomes within a population for breeding purposes. Considering the enormity of the search space for complex problems, it is imperative to examine genetic programs breeding efforts in order to produce better solutions with less training. This research examines chromosome lineage within genetic programs in order to identify breeding patterns. Fitness values for chromosomes are sorted, then partitioned into five classes. Initial experiments reveal a distinct difference between upper, middle, and lower classes. Based upon initial results, a novel genetic programming process is proposed which breeds a new generation exclusively from the top 20 percent of a population. A second set of experiments statistically validate this proposed approach", notes = "Houston Univ., TX", } @InProceedings{Bogdanova:2019:GECCOcomp, author = "Anna Bogdanova and Jair Pereira Junior and Claus Aranha", title = "Franken-swarm: grammatical evolution for the automatic generation of swarm-like meta-heuristics", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "411--412", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3321902", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "Also known as \cite{3321902} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{BOHAIENKO:2021:RCO, author = "Vsevolod Bohaienko", title = "Selection of ?-Caputo derivatives' functional parameters in generalized water transport equation by genetic programming technique", journal = "Results in Control and Optimization", volume = "5", pages = "100068", year = "2021", ISSN = "2666-7207", DOI = "doi:10.1016/j.rico.2021.100068", URL = "https://www.sciencedirect.com/science/article/pii/S2666720721000394", keywords = "genetic algorithms, genetic programming, Moisture transport, Fractional differential equation, Parameters identification, -Caputo derivative", abstract = "The paper considers the usage of genetic programming technique to select an analytic form of functional parameter of the ?-Caputo fractional derivative. We study one-dimensional space-time fractional water transport equation with such derivatives with respect to both time and space variables that generalizes the classical Richards equation. Having water head values measured by Watermark sensors as inputs, the statement of parameters identification problem is performed. The forms of functional parameters are represented as trees and found using a genetic programming algorithm. We compare the accuracy of field data description by the model with fixed and variable forms of derivatives' functional parameters and obtained up to 30percent increase in accuracy for the training dataset and up to 15percent increase for the testing dataset when the considered method was used to select parameters' forms", } @InProceedings{Bohm:2019:GECCOcomp, author = "Clifford Bohm and Alexander Lalejini and Jory Schossau and Charles Ofria", title = "{MABE 2.0}: an introduction to {MABE} and a road map for the future of {MABE} development", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "1349--1356", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3326825", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3326825} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Article{Bohm:2022:AlifeJ, author = "Clifford Bohm and Sarah Albani and Charles Ofria and Acacia Ackles", journal = "Artificial Life", title = "Using the Comparative Hybrid Approach to Disentangle the Role of Substrate Choice on the Evolution of Cognition", year = "2022", volume = "28", number = "4", pages = "423--439", abstract = "Understanding the structure and evolution of natural cognition is a topic of broad scientific interest, as is the development of an engineering toolkit to construct artificial cognitive systems. One open question is determining which components and techniques to use in such a toolkit. To investigate this question, we employ agent-based AI, using simple computational substrates (i.e., digital brains) undergoing rapid evolution. Such systems are an ideal choice as they are fast to process, easy to manipulate, and transparent for analysis. Even in this limited domain, however, hundreds of different computational substrates are used. While benchmarks exist to compare the quality of different substrates, little work has been done to build broader theory on how substrate features interact. We propose a technique called the Comparative Hybrid Approach and develop a proof-of-concept by systematically analysing components from three evolvable substrates: recurrent artificial neural networks, Markov brains, and Cartesian genetic programming. We study the role and interaction of individual elements of these substrates by recombining them in a piecewise manner to form new hybrid substrates that can be empirically tested. Here, we focus on network sparsity, memory discretization, and logic operators of each substrate. We test the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observe many trends, we see that discreteness of memory and the Markov brain logic gates correlate with high performance across our test conditions. Our results demonstrate that the Comparative Hybrid Approach can identify structural subcomponents that predict task performance across multiple computational substrates.", keywords = "genetic algorithms, genetic programming, Digital evolution, artificial intelligence, cognitive substrate, neuroscience, neuroevolution, Markov brain", DOI = "doi:10.1162/artl_a_00372", ISSN = "1064-5462", month = jan, notes = "Also known as \cite{10302012}", } @InProceedings{bohm:1996:eui, author = "Walter Bohm and Andreas Geyer-Schulz", title = "Exact Uniform Initialization for Genetic Programming", booktitle = "Foundations of Genetic Algorithms IV", year = "1996", editor = "Richard K. Belew and Michael Vose", pages = "379--407", address = "University of San Diego, CA, USA", publisher_address = "San Francisco, California, USA", month = "3--5 " # aug, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-460-X", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bohm_1996_eui.pdf", URL = "http://cseweb.ucsd.edu/~rik/foga4/Abstracts/07-wb-abs.html", size = "29 pages", abstract = "In this paper we solve the problem of exactly uniform generation of complete derivation trees from k-bounded context-free languages. The result is applied and is used for developing an exact uniform initialization routine for a genetic programming variant based on an explicit representation of the grammar of the context-free language (simple genetic algorithm over k-bounded context-free languages) [Geyer-Schulz1996b]. In this genetic programming variant the grammar is used to generate complete derivation trees which constitute the genomes for the algorithm. For the case that no a priori information about the solution is available, we prove that this (simple random sampling) algorithm is optimal in the sense of a minimax strategy. An exact uniform initialization routine for Koza's genetic programming variant [Koza1992] is derived as a special case.", notes = "FOGA4 k-bounded context-free languages May also use key Boehm96 Demonstrated on XOR problem", } @InProceedings{Boisbunon:2021:GECCO, author = "Aurelie Boisbunon and Carlo Fanara and Ingrid Grenet and Jonathan Daeden and Alexis Vighi and Marc Schoenauer", title = "Zoetrope Genetic Programming for regression", booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference", year = "2021", editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton", pages = "776--784", address = "internet", series = "GECCO '21", month = jul # " 10-14", organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, ZGP, Symbolic regression, regression, Representation of mathematical functions, Supervised learning by regression, Learning linear models, Feature selection", isbn13 = "9781450383509", hal_id = "hal-03155694", hal_version = "v1", URL = "https://hal.archives-ouvertes.fr/hal-03155694/file/ZGP_regression_arxiv.pdf", DOI = "doi:10.1145/3449639.3459349", code_url = "https://gitlab.devenv.mydatamodels.com/publications/bench-zgp-symbolic-regression/-/tree/master/", size = "9 pages", abstract = "The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targetting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performances with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.", notes = "Supplemental Material https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3449639.3459349&file=p776-boisbunon_suppl.pdf MyDataModels, Sophia Antipolis, France GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)", } @InProceedings{Boisvert:2021:EuroGP, author = "Stephen Boisvert and John Sheppard", title = "Quality Diversity Genetic Programming for Learning Decision Tree Ensembles", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "3--18", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Decision tree ensemble, Quality diversity", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_1", abstract = "Quality Diversity (QD) algorithms are a class of population-based evolutionary algorithms designed to generate sets of solutions that are both fit and diverse. In this paper, we describe a strategy for applying QD concepts to the generation of decision tree ensembles by optimizing collections of trees for both individually accurate and collectively diverse predictive behavior. We compare three variants of this QD strategy with two existing ensemble generation strategies over several classification data sets. We then briefly highlight the effect of the evolutionary algorithm at the core of the strategy. The examined algorithms generate ensembles with distinct predictive behaviors as measured by classification accuracy and intrinsic diversity. The plotted behaviors hint at highly data-dependent relationships between these metrics. QD-based strategies are suggested as a means to optimize classifier ensembles along this performance curve along with other suggestions for future work.", notes = "Random forests, bagging, classification. Quality diversity, NSLC, MAP-Elites, Cully (TEVC 2017) archive update based both on similarity(BC) and fitness. Binary trees height = 4. quality/Diversity QD. accuracy v diversity. UCI. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{bojarczuk:1999:DGP, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "Discovering comprehensible classification rules by using Genetic Programming: a case study in a medical domain", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "953--958", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications", ISBN = "1-55860-611-4", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/gecco99.ps", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://citeseer.ist.psu.edu/340269.html", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-417.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-417.ps", abstract = "This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the discovery of comprehensible knowledge.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). See also \cite{bojarczuk:2000:kdcp}", } @Article{bojarczuk:2000:kdcp, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "Genetic programming for knowledge discovery in chest-pain diagnosis", journal = "IEEE Engineering in Medicine and Biology Magazine", year = "2000", volume = "19", number = "4", pages = "38--44", month = jul # "-" # aug, keywords = "genetic algorithms, genetic programming, data mining, knowledge discovery, chest-pain diagnosis, predictive accuracy, rule set, comprehensible rules, background knowledge, preprocessing step, data sets, medical applications", ISSN = "0739-5175", URL = "http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IEEE-EMB-2000.ps", URL = "http://citeseer.ist.psu.edu/459907.html", size = "7 pages", abstract = "Explores a promising data mining approach. Despite the small number of examples available in the authors' application domain (taking into account the large number of attributes), the results of their experiments can be considered very promising. The discovered rules had good performance concerning predictive accuracy, considering both the rule set as a whole and each individual rule. Furthermore, what is more important from a data mining viewpoint, the system discovered some comprehensible rules. It is interesting to note that the system achieved very consistent results by working from {"}tabula rasa,{"} without any background knowledge, and with a small number of examples. The authors emphasize that their system is still in an experiment in the research stage of development. Therefore, the results presented here should not be used alone for real-world diagnoses without consulting a physician. Future research includes a careful selection of attributes in a preprocessing step, so as to reduce the number of attributes (and the corresponding search space) given to the GP. Attribute selection is a very active research area in data mining. Given the results obtained so far, GP has been demonstrated to be a really useful data mining tool, but future work should also include the application of the GP system proposed here to other data sets, to further validate the results reported in this article.", notes = "lilgp", } @InProceedings{bojarczuk:2001:idamap, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "Data mining with constrained-syntax genetic programming: applications to medical data sets", booktitle = "Proceedings Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001)", year = "2001", note = "a workshop at MedInfo-2001", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications, Constrained-Syntax Genetic Programming", URL = "http://www.ailab.si/idamap/idamap2001/papers/bojarczuk.pdf", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "https://kar.kent.ac.uk/13556/", URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IDAMAP-2001.ps", URL = "http://citeseer.ist.psu.edu/459555.html", abstract = "This work is intended to discover classification rules for diagnosing certain pathologies. In order to discover these rules we have developed a new constrained-syntax genetic programming algorithm based on some concepts of data mining, particularly with emphasis on the discovery of comprehensible knowledge. We compare the performance of the proposed GP algorithm with a genetic algorithm and with the very well-known decision-tree algorithm C4.5.", notes = "IDAMAP workshop http://www.ailab.si/idamap/idamap2001/ Evolves IFTHEN rules. GP syntax contrained similar to STGP. Size of rules used as component of fitness function (actually product of sensitivity, specificity and size releated coefficient. Demonstrated on 3 small medical datasets (2 UCI). ", } @InProceedings{bojarczuk03, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas", title = "An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "11--21", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", DOI = "doi:10.1007/3-540-36599-0_2", ISBN = "3-540-00971-X", abstract = "This paper proposes a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for mining the data", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003", } @Article{bojarczuk:2004:EMBM, author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A. Freitas and Edson L Michalkiewicz", title = "A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets", journal = "Artificial Intelligence in Medicine", year = "2004", volume = "30", number = "1", pages = "27--48", month = jan, ISSN = "0933-3657", keywords = "genetic algorithms, genetic programming, data mining, classification, medical applications", URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html", URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2004/aim2004.pdf", URL = "http://www.sciencedirect.com/science/article/B6T4K-4B42BDH-1/2/77bc597c3188977bd9ffb40ba10802ac", URL = "http://www.harcourt-international.com/journals/aiim/", DOI = "doi:10.1016/j.artmed.2003.06.001", abstract = "We propose a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumour. For this last data set a new preprocessing step was devised for survival prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by comparison with C4.5 and BGP.", } @InProceedings{Bojtar:2020:CINTI, author = "Veronika Bojtar and Janos Botzheim", title = "Queen Bee Based Genetic Programming Method for a Hive Like Behavior", booktitle = "2020 IEEE 20th International Symposium on Computational Intelligence and Informatics (CINTI)", year = "2020", pages = "000127--000132", abstract = "Designing the behavioural attributes of a robot is challenging, and the complexity of this task is even more increased in the case of swarm robotics. For effectively solving such problems special types of evolutionary algorithms can be used such as Genetic Programming and Queen Bee Based Genetic Programming method. The revolutionary idea behind these algorithms is that they use tree based representation for the individuals in a population, thus being able to solve structure optimization problems. The goal of this paper is to introduce the idea of the Queen Bee Based Genetic Programming method and compare its effectiveness with Genetic Programming through the evolution of a successful hive based behavioral program.", keywords = "genetic algorithms, genetic programming, Statistics, Sociology, Task analysis, Informatics, Evolution (biology), queen bee based genetic programming, hive like behavior", DOI = "doi:10.1109/CINTI51262.2020.9305824", ISSN = "2471-9269", month = nov, notes = "Also known as \cite{9305824}", } @InProceedings{Bokhari:2016:GI, author = "Mahmoud Bokhari and Markus Wagner", title = "Optimising Energy Consumption Heuristically on {Android} Mobile Phones", booktitle = "Genetic Improvement 2016 Workshop", year = "2016", editor = "Justyna Petke and David R. White and Westley Weimer", pages = "1139--1140", address = "Denver", publisher_address = "New York, NY, USA", month = jul # " 20-24", organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Embedded systems, Computing methodologies, Heuristic function construction, Randomized search, Power Consumption Modelling, Energy Optimisation", URL = "http://cs.adelaide.edu.au/~markus/pub/2016-gecco-gi-energy.pdf", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Optimising_Energy_Consumption_Heuristically_on_Android_Mobile_Phones.pdf", DOI = "doi:10.1145/2908961.2931691", size = "2 pages", abstract = "In this paper we outline our proposed framework for optimising energy consumption on Android mobile phones. To model the power usage, we use an event-based modelling technique. The internal battery fuel gauge chip is used to measure the amount of energy being consumed and accordingly the model is built on. We use the model to estimate components' energy usages. In addition, we propose the use of evolutionary computations to prolong the battery life. This can be achieved by using the power consumption model as a fitness function, re-configuring the smartphone's default settings and modifying existing code of applications.", notes = "GECCO-2016 Workshop http://geneticimprovementofsoftware.com/", } @Misc{Bokhari:2017:arXiv, author = "Mahmoud A. Bokhari and Yuanzhong Xia and Bo Zhou and Brad Alexander and Markus Wagner", title = "Validation of Internal Meters of Mobile {Android} Devices", year = "2017", month = "24 " # jan, note = "arXiv:1701.07095", keywords = "Software engineering, Adaptive systems, System improvement, Computational intelligence", URL = "https://arxiv.org/abs/1701.07095", size = "2 pages", abstract = "In this paper we outline our results for validating the precision of the internal power meters of smart-phones under different workloads. We compare its results with an external power meter. This is the first step towards creating customized energy models on the fly and towards optimizing battery efficiency using genetic program improvements. Our experimental results indicate that the internal meters are sufficiently precise when large enough time windows are considered. This is part of our work on the dreaming smart-phone. For a technical demonstration please watch our videos https://www.youtube.com/watch?v=xeeFz2GLFdU and https://www.youtube.com/watch?v=C7WHoLW1KYw.", notes = "Used watt meters: Yoctopuce YoctoWatt, Maxim MAX17050. Used smart-phones: Nexus 6, Nexus 9 Not on GP but of interest to genetic improvement", } @InProceedings{Bokhari:2017:GI, author = "Mahmoud A. Bokhari and Bobby R. Bruce and Brad Alexander and Markus Wagner", title = "Deep Parameter Optimisation on {Android} Smartphones for Energy Minimisation - A Tale of Woe and a Proof-of-Concept", booktitle = "GI-2017", year = "2017", editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer", pages = "1501--1508", address = "Berlin", month = "15-19 " # jul, publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, non-functional properties, mobile devices, multi-objective optimisation, dreaming smartphone, Android 6", isbn13 = "978-1-4503-4939-0", URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/bokhari2017_deep_parameter_optimisation.pdf", URL = "http://cs.adelaide.edu.au/~markus/pub/2017gecco-deepandroid.pdf", DOI = "doi:10.1145/3067695.3082519", size = "8 pages", abstract = "With power demands of mobile devices rising, it is becoming increasingly important to make mobile software applications more energy efficient. Unfortunately, mobile platforms are diverse and very complex which makes energy behaviours difficult to model. This complexity presents challenges to the effectiveness of off-line optimisation of mobile applications. we demonstrate that it is possible to automatically optimise an application for energy on a mobile device by evaluating energy consumption in-vivo. In contrast to previous work, we use only the device's own internal meter. Our approach involves many technical challenges but represents a realistic path toward learning hardware specific energy models for program code features.", notes = "Rebound, Java, 44 test cases with test oracles. Sensitivity analysis of all integer and double constants before start reduces 38 parameters to 19. NSGA-II. Sample remaining energy in battery four times a second. flight mode not sufficient. Control temperature. Nexus 6, Nexus 9. Fixed CPU clock speed. Java garbage collector also run 4 times per second. Kolmogorov-Smirnov normality test. Recharge battery between generations. 55 mutants", } @InProceedings{Bokhari:2018:MobiQuitous, author = "Mahmoud A. Bokhari and Brad Alexander and Markus Wagner", title = "In-Vivo and Offline Optimisation of Energy Use in the Presence of Small Energy Signals: A Case Study on a Popular Android Library", booktitle = "Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2018", year = "2018", editor = "Henning Schulzrinne and Pan Li", pages = "207--215", address = "New York", month = "5-7 " # nov, organisation = "EAI", publisher = "ACM", keywords = "genetic algorithms, genetic programming, genetic improvement, electrical batteries, software engineering, Search-based software engineering, SBSE, energy consumption, mobile applications, multi-objective optimisation, Android, Non-functional properties", isbn13 = "9781450360937", URL = "https://cs.adelaide.edu.au/~markus/pub/2018mobiquitous-smallEnergySignals.pdf", URL = "https://srb.sdl.edu.sa/esploro/outputs/conferenceProceeding/In-vivo-and-offline-optimisation-of-energy/9930446008331", DOI = "doi:10.1145/3286978.3287014", size = "9 pages", abstract = "Energy demands of applications on mobile platforms are increasing. As a result, there has been a growing interest in optimising their energy efficiency. As mobile platforms are fast-changing, diverse and complex, the optimisation of energy use is a non-trivial task. To date, most energy optimisation methods either use models or external meters to estimate energy use. Unfortunately, it becomes hard to build widely applicable energy models, and external meters are neither cheap nor easy to set up. To address this issue, we run application variants in-vivo on the phone and use a precise internal battery monitor to measure energy use. We describe a methodology for optimising a target application in-vivo and with application-specific models derived from the device's own internal meter based on jiffies and lines of code. We demonstrate that this process produces a significant improvement in energy efficiency with limited loss of accuracy.", notes = "Surrogate linear model Mobile phone physics library. p214 'energy savings of up to 22 percent'", } @InProceedings{Bokhari:2019:GI7, author = "Mahmoud A. Bokhari and Markus Wagner and Brad Alexander", title = "The Quest for Non-Functional Property Optimisation in Heterogeneous and Fragmented Ecosystems: a Distributed Approach", booktitle = "7th edition of GI @ GECCO 2019", year = "2019", month = jul # " 13-17", editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", publisher_address = "New York, NY, USA", address = "Prague, Czech Republic", pages = "1705--1706", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, smartphone, Hardware, Batteries, Software maintenance tools, Non-functional properties, energy consumption, mobile applications, Android", isbn13 = "978-1-4503-6748-6", URL = "https://cs.adelaide.edu.au/~markus/pub/2019gecco-islands.pdf", DOI = "doi:10.1145/3319619.3326877", size = "2 pages", abstract = "The optimisation of non-functional properties of software is of growing importance in all scales of modern computing (from embedded systems to data-centres). In mobile computing, smart devices have complex interactions between their hardware and software components. Small changes in the environment can greatly impact the measurements of non-functional properties of software. In-vivo optimisation of applications on a platform can be used to evolve robust new solutions. However, the portability of such solutions performance across different platforms is questionable. In this paper we discuss the issue of optimising the non-functional properties of applications running in the Android ecosystem. We also propose a distributed framework that can mitigate such issues.", notes = " Also known as \cite{Bokhari:2019:GECCOcomp} Also known as \cite{3326877} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Bokhari:2020:GI9, author = "Mahmoud A. Bokhari and Markus Wagner and Brad Alexander", title = "Genetic Improvement of Software Efficiency: The Curse of Fitness Estimation", booktitle = "9th edition of GI @ GECCO 2020", year = "2020", month = jul # " 8-12", editor = "Brad Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward", publisher = "ACM", address = "Internet", pages = "1926--1927", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Machine Learning, Non-Functional Properties, batteries, Energy Consumption, Mobile Applications, Android", isbn13 = "978-1-4503-7127-8", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2020/companion_files/wksp145s2-file1.pdf", DOI = "doi:10.1145/3377929.3398109", size = "2 pages", abstract = "Many challenges arise in the application of Genetic Improvement (GI) of Software to improve non-functional requirements of software such as energy use and run-time. These challenges are mainly centred around the complexity of the search space and the estimation of the desired fitness function. For example, such fitness function are expensive, noisy and estimating them is not a straight-forward task. we illustrate some of the challenges incomputing such fitness functions and propose a synergy between in-vivo evaluation and machine learning approaches to overcome such issues.", notes = "nexus6 android7 smartphone, garbage collection, surrogate fitness function. Broken Nov 2024 https://gi-gecco-20.gi-workshops.org/ Also known as \cite{Bokhari:2020:GECCOcomp}. Also known as \cite{10.1145/3377929.3398109} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @InProceedings{Bokhari:2020:GECCO, author = "Mahmoud A. Bokhari and Brad Alexander and Markus Wagner", title = "Towards Rigorous Validation of Energy Optimisation Experiments", year = "2020", editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann", isbn13 = "9781450371285", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", URL = "https://arxiv.org/abs/2004.04500", URL = "https://doi.org/10.1145/3377930.3390245", DOI = "doi:10.1145/3377930.3390245", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference", pages = "1232--1240", size = "9 pages", keywords = "genetic algorithms, genetic programming, genetic improvement, Android, non-functional properties, energy consumption, mobile applications", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", abstract = "The optimisation of software energy consumption is of growing importance across all scales of modern computing, i.e., from embedded systems to data-centres. Practitioners in the field of Search-Based Software Engineering and Genetic Improvement of Software acknowledge that optimising software energy consumption is difficult due to noisy and expensive fitness evaluations. However, it is apparent from results to date that more progress needs to be made in rigorously validating optimisation results. This problem is pressing because modern computing platforms have highly complex and variable behaviour with respect to energy consumption. To compare solutions fairly we propose in this paper a new validation approach called R3-validation which exercises software variants in a rotated-round-robin order. Using a case study, we present an in-depth analysis of the impacts of changing system states on software energy usage, and we show how R3-validation mitigates these. We compare it with current validation approaches across multiple devices and operating systems, and we show that it aligns best with actual platform behaviour.", notes = "Also known as \cite{10.1145/3377930.3390245} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @PhdThesis{Bokhari2020_PhD, author = "Mahmoud Abdulwahab K. Bokhari", title = "Genetic Improvement of Software for Energy Efficiency in Noisy and Fragmented Eco-Systems", school = "School of Computer Science, University of Adelaide", year = "2020", address = "Australia", month = "7 " # dec, keywords = "genetic algorithms, genetic programming, genetic improvement, search based software engineering, SBSE, genetic improvement of software, non-functional properties, energy efficiency, battery optimisation, deep parameter optimisation, mobile computing, Android, validation approach, R-3 validation approch", URL = "http://hdl.handle.net/2440/130174", URL = "https://digital.library.adelaide.edu.au/dspace/bitstream/2440/130174/1/Bokhari2020_PhD.pdf", size = "166 pages", abstract = "Software has made its way to every aspect of our daily life. Users of smart devices expect almost continuous availability and uninterrupted service. However, such devices operate on restricted energy resources. As energy efficiency of software is relatively a new concern for software practitioners, there is a lack of knowledge and tools to support the development of energy efficient software. Optimising the energy consumption of software requires measuring or estimating its energy use and then optimising it. Generalised models of energy behaviour suffer from heterogeneous and fragmented eco-systems (i.e. diverse hardware and operating systems). The nature of such optimisation environments favours in-vivo optimisation which provides the ground-truth for energy behaviour of an application on a given platform. One key challenge in in-vivo energy optimisation is noisy energy readings. This is because complete isolation of the effects of software optimisation is simply infeasible, owing to random and systematic noise from the platform. we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy efficiency in noisy and fragmented eco-systems. First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments. Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite. Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment. Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions.", notes = "Supervisor: Markus Wagner Co-Supervisor: Bradley Alexander", } @InProceedings{DBLP:conf/icaart/BokhariT20, author = "Syed Mohtashim Abbas Bokhari and Oliver E. Theel", editor = "Ana Paula Rocha and Luc Steels and H. Jaap {van den Herik}", title = "Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming", booktitle = "Proceedings of the 12th International Conference on Agents and Artificial Intelligence, ICAART 2020", year = "2020", volume = "2", pages = "120--129", address = "Valletta, Malta", month = feb # " 22-24", publisher = "SCITEPRESS", keywords = "genetic algorithms, genetic programming, Distributed Systems, Fault Tolerance, Data Replication, Quorum Protocols, Operation Availability, Operation Cost, Voting Structures, Optimization, Machine Leaning, Evolutionary Strategies", isbn13 = "978-989-758-395-7", URL = "https://doi.org/10.5220/0008955301200129", DOI = "doi:10.5220/0008955301200129", timestamp = "Tue, 14 Apr 2020 10:44:02 +0200", biburl = "https://dblp.org/rec/conf/icaart/BokhariT20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", size = "10 pages", abstract = "A distributed system is a paradigm which is indispensable to the current world due to countless requests with every passing second. Therefore, in distributed computing, high availability is very important. In a dynamic environment due to the scalability and complexity of the resources and components, systems are fault-prone because millions of computing devices are connected to each other via communication links. Distributed systems allow many users to access shared computing resources which makes faults inevitable. Replication plays its role in masking failures in order to achieve a fault-tolerant distributed environment. Data replication is an appropriate means to provide highly available data access operations at relatively low operation costs. Although there are several contemporary data replication strategies being used, the question still stands which strategy is the best for a given scenario or application class assuming a certain workload, its distribution across a network, av ailability of the individual replicas, and cost of the access operations. In this regard, research focuses on analysis, simulation, and machine learning approaches to automatically identify and design such replication strategies that are optimized for a given application scenario based on predefined constraints and properties exploiting a so-called voting structure.", notes = "Department of Computer Science, University of Oldenburg, Germany", } @InProceedings{Bokhari:2020:CEC, author = "Syed Mohtashim Abbas Bokhari and Oliver Theel", title = "A Genetic Programming-based Multi-objective Optimization Approach to Data Replication Strategies for Distributed Systems", booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020", year = "2020", editor = "Yaochu Jin", pages = "paper id24298", address = "internet", month = "19-24 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-6929-3", DOI = "doi:10.1109/CEC48606.2020.9185598", abstract = "Data replication is the core of distributed systems to enhance their fault tolerance and make services highly available to the end-users. Data replication masks run-time failures and hence, makes the system more reliable. There are many contemporary data replication strategies for this purpose, but the decision to choose an appropriate strategy for a certain environment and a specific scenario is a challenge and full of compromises. There exists a potentially indefinite number of scenarios that cannot be covered entirely by contemporary strategies. It demands designing new data replication strategies optimized for the given scenarios. The constraints of such scenarios are often conflicting in a sense that an increase in one objective could be sacrificial to the others, which implies there is no best solution to the problem but what serves the purpose. In this regard, this research provides a genetic programming-based multi-objective optimization approach that endeavors to not only identify, but also design new data replication strategies and optimize their conflicting objectives as a single-valued metric. The research provides an intelligent, automatic mechanism to generate new replication strategies as well as easing up the decision making so that relevant strategies with satisfactory trade-offs of constraints can easily be picked and used from the generated solutions at run-time. Moreover, it makes the notion of hybrid strategies easier to accomplish which otherwise would have been very cumbersome to achieve, therefore, to optimize.", notes = "https://wcci2020.org/ University of Oldenburg, Germany. Also known as \cite{9185598}", } @InProceedings{Bokhari:2020:PRDC, author = "Syed Mohtashim Abbas Bokhari and Oliver Theel", title = "Introducing Novel Crossover and Mutation Operators into Data Replication Strategies for Distributed Systems", booktitle = "IEEE 25th Pacific Rim International Symposium on Dependable Computing, PRDC 2020", year = "2020", pages = "21--30", address = "Perth, Australia", month = "1-4 " # dec, keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-7281-8004-5", DOI = "doi:10.1109/PRDC50213.2020.00013", size = "10 pages", abstract = "In a distributed paradigm, data replication plays a vital role in achieving high availability and fault tolerance of a system. In a connected network, faults are inevitable, replication masks those faults at run-time while users are unaware of it and the system continues to work as expected. There are different strategies to enforce such fault-tolerant behavior on a system. However, there are numerous scenarios reflecting different trade-offs between several quality metrics and to identify a relevant strategy for a specific scenario is quite cumbersome since there could exist potentially infinite scenarios and solutions are limited. This requires designing new solutions satisfying the constraints of such scenarios, which may be left unaddressed otherwise. In this regard, this paper develops a mechanism to automatically design new replication strategies (up-to-now unknown), optimized for given scenarios. The paper uses genetic programming to explore unknown replication strategies. It evolves the population of replication strategies (representing each a computer program) gradually, but consistently to make them optimized to eventually meet the desired criteria. Furthermore, it introduces strong multi-crossover and multi-mutation operators into replication, which strengthens our machine learning framework, at the same time guaranteeing consistency of the solutions, to generate innovative hybrid replication strategies.", notes = "University of Oldenburg, Department of Computer Science, Germany Also known as \cite{9320430} \cite{DBLP:conf/prdc/BokhariT20}", } @InProceedings{Bokhari:2020:ICPADS, author = "Syed Mohtashim Abbas Bokhari and Oliver Theel", title = "Use of Genetic Programming Operators in Data Replication and Fault Tolerance", booktitle = "2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)", year = "2020", pages = "290--299", abstract = "Distributed systems are a need of the current times to balance the workload since providing highly accessible data objects is of utmost importance. Faults hinder the availability of the data, thereby leading systems to fail. In this regard, data replication in distributed systems is a means to mask failures and mitigate any such possible hindrances in the availability of the data. This replicated behavior is then controlled by data replication strategies, but there are numerous scenarios reflecting different trade-offs between several quality metrics. It demands designing new replication strategies optimized for the given scenarios, which may be left unaddressed otherwise. This research, therefore, uses an automatic mechanism based on genetic programming to construct new optimized replication strategies (up-to-now) unknown. This mechanism uses a so-called voting structure of directed acyclic graphs (each representing a computer program) as a unified representation of replication strategies. These structures are interpreted by our general algorithm at run-time in order to derive respective quorums to manage replicated objects eventually. For this, the research particularly demonstrates the usefulness of various genetic operators through their instances, exploiting the heterogeneity between existing strategies, thereby creating innovative strategies flexibly. This mechanism creates new hybrid strategies and evolves them over several generations of evolution, to make them optimized while maintaining the consistency (validity) of the solutions. Our approach is very effective and extremely flexible to offer competitive results with respect to the contemporary strategies as well as generating novel strategies even with a slight use of relevant genetic operators.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICPADS51040.2020.00047", ISSN = "2690-5965", month = dec, notes = "Also known as \cite{9359219}", } @Article{olandi2019intelligent, author = "Hamed Bolandi and Wolfgang Banzhaf and Nizar Lajnef and Kaveh Barri and Amir H. Alavi", title = "An Intelligent Model for the Prediction of Bond Strength of {FRP} Bars in Concrete: A Soft Computing Approach", journal = "Technologies", year = "2019", volume = "7", number = "2", pages = "42", month = jun, keywords = "genetic algorithms, genetic programming, multi-gene genetic programming, data mining, bond strength, FRP-bar", ISSN = "ISSN 2227-7080", owner = "banzhaf", timestamp = "2019.06.11", URL = "https://www.mdpi.com/2227-7080/7/2/42/pdf", URL = "https://doi.org/10.3390/technologies7020042", DOI = "doi:10.3390/technologies7020042", size = "16 pages", abstract = "Accurate prediction of bond behaviour of fibre reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.", } @InProceedings{Bolandi:2019:GECCOcomp, author = "Hamed Bolandi and Wolfgang Banzhaf and Nizar Lajnef and Kaveh Barri and Amir. H Alavi", title = "Bond strength prediction of {FRP}-bar reinforced concrete: a multi-gene genetic programming approach", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "364--364", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322066", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322066} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @Misc{boldi2022environmentaldiscontinuityhypothesisdownsampled, author = "Ryan Boldi and Thomas Helmuth and Lee Spector", title = "The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selection", year = "2022", howpublished = "arXiv 2205.15931", month = "31 " # may, keywords = "genetic algorithms, genetic programming", eprint = "2205.15931", archiveprefix = "arXiv", primaryclass = "cs.NE", URL = "https://arxiv.org/abs/2205.15931", size = "10 pages", } @InProceedings{boldi:2023:GECCOcomp, author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini", title = "The Problem Solving Benefits of {Down-Sampling} Vary by Selection Scheme", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "527--530", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, program synthesis, down-sampling, selection, regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590713", size = "4 pages", abstract = "Genetic programming systems often use large training sets to evaluate candidate solutions, which can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use lexicase parent selection. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.", notes = "See \cite{Boldi:2024:ALife} GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{boldi:2023:GECCOcomp2, author = "Ryan Boldi and Alexander Lalejini and Thomas Helmuth and Lee Spector", title = "A Static Analysis of Informed {Down-Samples}", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "531--534", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, down-sampling, selection, program synthesis, regression: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590751", size = "4 pages", abstract = "We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintains population-level test coverage when compared to random down-sampling. Additionally, we show that both forms of down-sampling cause greater test coverage loss than standard lexicase selection with no down-sampling. However, given more information about the population, we found that informed down-sampling can further reduce its test coverage loss. We also recommend wider adoption of the static population analyses we present in this work.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{boldi:2024:GECCOcomp3, author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini", title = "A Comprehensive Analysis of Down-sampling for Genetic Programming-based Program Synthesis", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Ting Hu and Aniko Ekart", pages = "487--490", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming: Poster", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3654134", size = "4 pages", abstract = "Genetic programming systems typically require large computational resource investments for training-set evaluations. Down-sampling these sets has proven to decrease costs and improve problem-solving success, particularly with the lexicase parent selection algorithm. We investigated its effectiveness when applied to three other common selection methods and across various program synthesis problems. Our findings show that down-sampling notably enhances all three methods, indicating its potential broad applicability. Additionally, we found informed down-sampling to be more successful than its random counterpart, particularly in selection schemes maintaining diversity like lexicase selection. We conclude that down-sampling is a promising strategy for test-based genetic programming problems, irrespective of selection scheme.This paper is a comprehensive extension of a previous poster paper [1].", notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Boldi:2024:ALife, author = "Ryan Boldi and Ashley Bao and Martin Briesch and Thomas Helmuth and Dominik Sobania and Lee Spector and Alexander Lalejini", title = "Untangling the Effects of Down-Sampling and Selection in Genetic Programming", booktitle = "ALIFE 2024: Proceedings of the 2024 Artificial Life Conference", year = "2024", editor = "Andres Faina and Sebastian Risi and Eric Medvet and Kasper Stoy and Bert Chan and Karine Miras and Payam Zahadat and Djordje Grbic and Giorgia Nadizar", pages = "705--716", address = "Copenhagen", month = jul # " 22-26", organization = "The International Society for Artificial Life", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming, lexicase, PushGP, program synthesis benchmark problems, tournament, fitness proportional selection, mplicit Fitness Sharing, IFS, diversity maintenance", URL = "https://direct.mit.edu/isal/proceedings/isal2024/36/88/123536", URL = "https://direct.mit.edu/isal/proceedings-pdf/isal2024/36/88/2461089/isal_a_00832.pdf", DOI = "doi:10.1162/isal_a_00832", code_url = "https://github.com/lspector/propeller", size = "12 pages", abstract = "the lexicase parent selection algorithm. We test whether these down-sampling techniques can also improve problem-solving success in the context of three other commonly used selection methods, fitness-proportionate, tournament, implicit fitness sharing plus tournament selection, across six program synthesis GP problems. We verified that down-sampling can significantly improve the problem-solving success for all three of these other selection schemes, demonstrating its general efficacy. We discern that the selection pressure imposed by the selection scheme does not interact with the down-sampling method. However, we find that informed down-sampling can improve problem solving success significantly over random down-sampling when the selection scheme has a mechanism for diversity maintenance like lexicase or implicit fitness sharing. Overall, our results suggest that down-sampling should be considered more often when solving test-based problems, regardless of the selection scheme in use.", notes = "'we expand on...'\cite{boldi:2023:GECCOcomp} 'tournament sizes of t= 2,5,10,30'. IFS tournament size = 30. 'farthest first traversal (Hochbaum and Shmoys, 1985)' Count Odds, Fizz Buzz, Scrabble Score, Small or Large, Fuel Cost, Middle Character. Turing complete instruction set...looping and conditional execution. Diversity maintenance. Selection Pressure. ALIFE 2024", } @Article{Boldi:ECJ, author = "Ryan Boldi and Martin Briesch and Dominik Sobania and Alexander Lalejini and Thomas Helmuth and Franz Rothlauf and Charles Ofria and Lee Spector", title = "Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving", journal = "Evolutionary Computation", year = "2024", volume = "32", number = "4", pages = "307--337", month = "Winter", keywords = "genetic algorithms, genetic programming, lexicase selection, informed down-sampling", ISSN = "1063-6560", URL = "https://arxiv.org/abs/2301.01488", URL = "https://doi.org/10.1162/evco_a_00346", eprint = "https://direct.mit.edu/evco/article-pdf/doi/10.1162/evco_a_00346/2352336/evco_a_00346.pdf", DOI = "doi:10.1162/evco_a_00346", abstract = "Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases, allowing for more individuals to be explored with the same number of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs.", notes = "boldi2024informeddownsampledlexicaseselection University of Massachusetts, Amherst, MA 01003, USA", } @PhdThesis{Bolelli-Broinizi:thesis, author = "Marcos Eduardo {Bolelli Broinizi}", title = "Ordenacao evolutiva de anuncios em publicidade computacional", title_en = "Evolutionary ad ranking for computational advertising", school = "Instituto de Matematica e Estatistica, Universidade de Sao Paulo, USP", year = "2015", address = "Sao Paulo, Brazil", month = aug, keywords = "genetic algorithms, genetic programming, computational advertising, contextual advertising, exploratory data analysis, learning to advertising, online advertising, principal component analysis", bibsource = "OAI-PMH server at www.teses.usp.br", contributor = "Joao Eduardo Ferreira", language = "pt", oai = "oai:teses.usp.br:tde-09112015-104805", rights = "Open access", URL = "http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/", URL = "http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/publico/def_mbroinizi.pdf", URL = "http://www.teses.usp.br/teses/disponiveis/45/45134/tde-09112015-104805/en.php", DOI = "doi:10.11606/T.45.2015.tde-09112015-104805", size = "121 pages", abstract = "Otimizar simultaneamente os interesses dos usu{\'a}rios, anunciantes e publicadores {\'e} um grande desafio na {\'a}rea de publicidade computacional. Mais precisamente, a ordena{\c c}{\~a}o de an{\'u}ncios, ou ad ranking, desempenha um papel central nesse desafio. Por outro lado, nem mesmo as melhores f{\'o}rmulas ou algoritmos de ordena{\c c}{\~a}o s{\~a}o capazes de manter seu status por um longo tempo em um ambiente que est{\'a} em constante mudan{\c c}a. Neste trabalho, apresentamos uma an{\'a}lise orientada a dados que mostra a import{\^a}ncia de combinar diferentes dimens{\~o}es de publicidade computacional por meio de uma abordagem evolutiva para ordena{\c c}{\~a}o de an{\'u}ncios afim de responder a mudan{\c c}as de forma mais eficaz. N{\'o}s avaliamos as dimens{\~o}es de valor comercial, desempenho hist{\'o}rico de cliques, interesses dos usu{\'a}rios e a similaridade textual entre o an{\'u}ncio e a p{\'a}gina. Nessa avalia{\c c}{\~a}o, n{\'o}s averiguamos o desempenho e a correla{\c c}{\~a}o das diferentes dimens{\~o}es. Como consequ{\^e}ncia, n{\'o}s desenvolvemos uma abordagem evolucion{\'a}ria para combinar essas dimens{\~o}es. Essa abordagem {\'e} composta por tr{\^e}s partes: um reposit{\'o}rio de configura{\c c}{\~o}es para facilitar a implanta{\c c}{\~a}o e avalia{\c c}{\~a}o de experimentos de ordena{\c c}{\~a}o; um componente evolucion{\'a}rio de avalia{\c c}{\~a}o orientado a dados; e um motor de programa{\c c}{\~a}o gen{\'e}tica para evoluir f{\'o}rmulas de ordena{\c c}{\~a}o de an{\'u}ncios. Nossa abordagem foi implementada com sucesso em um sistema real de publicidade computacional respons{\'a}vel por processar mais de quatorze bilh{\~o}es de requisi{\c c}{\~o}es de an{\'u}ncio por m{\^e}s. De acordo com nossos resultados, essas dimens{\~o}es se complementam e nenhuma delas deve ser neglicenciada. Al{\'e}m disso, n{\'o}s mostramos que a combina{\c c}{\~a}o evolucion{\'a}ria dessas dimens{\~o}es n{\~a}o s{\'o} {\'e} capaz de superar cada uma individualmente, como tamb{\'e}m conseguiu alcan{\c c}ar melhores resultados do que m{\'e}todos est{\'a}ticos de ordena{\c c}{\~a}o de an{\'u}ncios.", abstract = "Simultaneous optimisation of users, advertisers and publishers' interests has been a formidable challenge in online advertising. More concretely, ranking of advertising, or more simply ad ranking, has a central role in this challenge. However, even the best ranking formula or algorithm cannot withstand the ever-changing environment of online advertising for a long time. In this work, we present a data-driven analysis that shows the importance of combining different aspects of online advertising through an evolutionary approach for ad ranking in order to effectively respond to changes. We evaluated aspects ranging from bid values and previous click performance to user behaviour and interests, including the textual similarity between ad and page. In this evaluation, we assessed commercial performance along with the correlation between different aspects. Therefore, we proposed an evolutionary approach for combining these aspects. This approach was composed of three parts: a configuration repository to facilitate deployment and evaluation of ranking experiments; an evolutionary data-based evaluation component; and a genetic programming engine to evolve ad ranking formulae. Our approach was successfully implemented in a real online advertising system that processes more than fourteen billion ad requests per month. According to our results, these aspects complement each other and none of them should be neglected. Moreover, we showed that the evolutionary combination of these aspects not only outperformed each of them individually, but was also able to achieve better overall results than static ad ranking methods.", notes = "in Portuguese", } @InProceedings{bolis:2001:EuroGP, author = "Enzo Bolis and Christian Zerbi and Pierre Collet and Jean Louchet and Evelyne Lutton", title = "A GP Artificial Ant for image processing: preliminary experiments with EASEA", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "246--255", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Image processing, Contour detection, EASEA, Animat: Poster", ISBN = "3-540-41899-7", URL = "http://minimum.inria.fr/evo-lab/Publications/EuroGPFinal.ps.gz", DOI = "doi:10.1007/3-540-45355-5_19", size = "10 pages", abstract = "This paper describes how animat-based food foraging techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient lines with a minimum exploration time. The resulting animats do not use standard scanning + filtering techniques but develop other image exploration strategies close to contour tracking. Experimental results on grey level images are presented.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{Bollegala:2011:GECCO, author = "Danushka Bollegala and Nasimul Noman and Hitoshi Iba", title = "{RankDE:} learning a ranking function for information retrieval using differential evolution", booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation", year = "2011", editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger", isbn13 = "978-1-4503-0557-0", pages = "1771--1778", keywords = "genetic algorithms, genetic programming, Real world applications", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", DOI = "doi:10.1145/2001576.2001814", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Learning a ranking function is important for numerous tasks such as information retrieval (IR), question answering, and product recommendation. For example, in information retrieval, a Web search engine is required to rank and return a set of documents relevant to a query issued by a user. We propose RankDE, a ranking method that uses differential evolution (DE) to learn a ranking function to rank a list of documents retrieved by a Web search engine. To the best of our knowledge, the proposed method is the first DE-based approach to learn a ranking function for IR. We evaluate the proposed method using LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method significantly outperforms previously proposed rank learning methods that use evolutionary computation algorithms such as Particle Swam Optimization (PSO) and Genetic Programming (GP), achieving a statistically significant mean average precision (MAP) of 0.339 on TD2003 dataset and 0.430 on the TD2004 dataset. Moreover, the proposed method shows comparable results to the state-of-the-art non-evolutionary computational approaches on this benchmark dataset. We analyze the feature weights learnt by the proposed method to better understand the salient features for the task of learning to rank for information retrieval.", notes = "Also known as \cite{2001814} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)", } @Misc{oai:CiteSeerX.psu:10.1.1.36.6062, author = "Beate Bollig and Martin Sauerhoff and Ingo Wegener", title = "Approximability and Non-Approximability by Binary Decision Diagrams (Extended Abstract)", year = "1995", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.36.6062", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", keywords = "genetic algorithms, genetic programming, subject classification, computational and structural complexity", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6062", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6062.pdf", broken = "http://ls2-www.cs.uni-dortmund.de/~wegener/papers/Paper95a.ps", size = "22 pages", abstract = "The usual applications of BDDs (binary decision diagrams), e. g., in verification and for CAD problems, require an exact representation of the considered Boolean functions. However, if BDDs are used for learning Boolean functions f on the basis of classified examples (e. g., in the environment of genetic programming), it is sufficient to produce the representation of a function g approximating f . This motivates the investigation of the size of the smallest BDD approximating f . Here exponential lower bounds for several BDD variants are proved and the relations between the size of approximating BDDs, randomised BDDs, communication complexity and general approximation techniques are revealed.", notes = "see also doi:10.1006/inco.2002.3174", } @InProceedings{bollini:1999:dpEAdp, author = "Alessandro Bollini and Marco Piastra", title = "Distributed and Persistent Evolutionary Algorithms: a Design Pattern", booktitle = "Genetic Programming, Proceedings of EuroGP'99", year = "1999", editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty", volume = "1598", series = "LNCS", pages = "173--183", address = "Goteborg, Sweden", publisher_address = "Berlin", month = "26-27 " # may, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-65899-8", DOI = "doi:10.1007/3-540-48885-5_14", abstract = "In the scenario of distributed processing for evolutionary algorithms the adoption of object-oriented database management systems (ODBMS) may yield improvements in terms of both robustness and flexibility. Populations of evolvable individuals can be made persistent across several evolutionary runs, making it possible to devise incremental strategies. Moreover, virtually any number of evolutionary processes may be run in parallel on the same underlying population without explicit synchronisation beyond that provided by the locking mechanism of the ODBMS. This paper describes a design pattern for a genetic programming environment that allows combining existing techniques with persistent population storage and management.", notes = "EuroGP'99, part of \cite{poli:1999:GP} Java objectstore database", } @InProceedings{bollini:1999:A, author = "Alessandro Bollini and Marco Piastra", title = "A persistent blackboard for distributed evolutionary computation", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "48--56", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Java", notes = "GECCO-99LB", } @Article{BOMARITO:2021:CS, author = "G. F. Bomarito and T. S. Townsend and K. M. Stewart and K. V. Esham and J. M. Emery and J. D. Hochhalter", title = "Development of interpretable, data-driven plasticity models with symbolic regression", journal = "Computer \& Structures", volume = "252", pages = "106557", year = "2021", ISSN = "0045-7949", DOI = "doi:10.1016/j.compstruc.2021.106557", URL = "https://www.sciencedirect.com/science/article/pii/S0045794921000791", keywords = "genetic algorithms, genetic programming, Plasticity, Homogenization, Symbolic regression", abstract = "In many applications, such as those which drive new material discovery, constitutive models are sought that have three characteristics: (1) the ability to be derived in automatic fashion with (2) high accuracy and (3) an interpretable nature. Traditionally developed models are usually interpretable but sacrifice development time and accuracy. Purely data-driven approaches are usually fast and accurate but lack interpretability. In the current work, a framework for the rapid development of interpretable, data-driven constitutive models is pursued. The approach is characterized by the use of symbolic regression on data generated with micromechanical finite element models. Symbolic regression is the search for equations of arbitrary functional form which match a given dataset. Specifically, an implicit symbolic regression technique is developed to identify a plastic yield potential from homogenized finite element response data. Through three controlled test cases of varying complexity, the approach is shown to successfully produce interpretable plasticity models. The controlled test cases are used to investigate the robustness and scalability of the method and provide reasonable recommendations for more complex applications. Finally, the recommendations are used in the application of the method to produce a porous plasticity model from data corresponding to a representative volume element of voids within a metal matrix", } @InProceedings{bomarito:2022:GECCOcomp, author = "Geoffrey Bomarito and Patrick Leser and Nolan Strauss and Karl Garbrecht and Jacob Hochhalter", title = "Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "526--529", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528899", abstract = "When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.The efficacy of a Genetic Programming (GP) [1] solution is often characterized by its (1) fitness, i.e. ability to perform a training task, (2) complexity, and (3) generalizability, i.e. ability to perform its task in an unseen scenario. Bloat is a common phenomenon for GP in which continued training results in significant increases in complexity with minimal improvements in fitness. There are several theories for the prevalence of bloat in GP which postulate possible evolutionary benefits of bloat [2]; however, for most practical purposes bloat is a hindrance rather than a benefit. For example, bloated solutions are less interpretable and more computationally expensive. Overfitting is another common phenomena in GP and the broader machine learning field. Overfitting occurs when continued training results in better fitness but reduced generalizability.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @Article{BOMARITO:2023:cma, author = "G. F. Bomarito and P. E. Leser and N. C. M. Strauss and K. M. Garbrecht and J. D. Hochhalter", title = "Automated learning of interpretable models with quantified uncertainty", journal = "Computer Methods in Applied Mechanics and Engineering", volume = "403", pages = "115732", year = "2023", ISSN = "0045-7825", DOI = "doi:10.1016/j.cma.2022.115732", URL = "https://www.sciencedirect.com/science/article/pii/S0045782522006879", keywords = "genetic algorithms, genetic programming, Interpretable machine learning, Symbolic regression, Bayesian model selection, Fractional Bayes factor", abstract = "Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently interpretable machine learning, but relatively little work has focused on the use of symbolic regression on noisy data and the accompanying necessity to quantify uncertainty. A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is introduced that uses model evidence (i.e., marginal likelihood) to formulate replacement probability during the selection phase of evolution. Model parameter uncertainty is automatically quantified, enabling probabilistic predictions with each equation produced by the GPSR algorithm. Model evidence is also quantified in this process, and its use is shown to increase interpretability, improve robustness to noise, and reduce overfitting when compared to a conventional GPSR implementation on both numerical and physical experiments", } @Article{Bonakdari:2016:FMI, author = "Hossein Bonakdari and Amir Hossein Zaji", title = "Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network", journal = "Flow Measurement and Instrumentation", volume = "49", pages = "46--51", year = "2016", ISSN = "0955-5986", DOI = "doi:10.1016/j.flowmeasinst.2016.04.003", URL = "http://www.sciencedirect.com/science/article/pii/S0955598616300309", abstract = "Determining the appropriate hidden layers neuron number is one of the most important processes in modelling the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN). Despite the significant effect of the MLP-ANN neurons number on predicting accuracy, there is no definite rule for its determination. In this study, a new self-neuron number adjustable, hybrid Genetic Algorithm-Artificial Neural Network (GA-ANN), is introduced and its application examined on the complex velocity field prediction of an open channel junction. The results of GA-ANN were compared with those got by the Genetic Programming (GP) method as two applications of the Genetic Algorithm (GA). The comparisons showed that the GA-ANN model can predict the open channel junction velocity with higher accuracy than the GP model, with Root Mean Squared Error (RMSE) of 0.086 and 0.156, respectively. Finally the equation, obtained by applying the GA-ANN model, predicting the velocity at the open channel junction is presented.", keywords = "genetic algorithms, genetic programming, Artificial neural network, Neuron number determination, Open channel junction, Velocity prediction", } @Article{BONAKDARI:2018:AMC, author = "Hossein Bonakdari and Zohreh Sheikh Khozani and Amir Hossein Zaji and Navid Asadpour", title = "Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study", journal = "Applied Mathematics and Computation", volume = "338", pages = "400--411", year = "2018", keywords = "genetic algorithms, genetic programming, Apparent shear stress, Artificial neural network, Compound channel, Hybrid method", ISSN = "0096-3003", DOI = "doi:10.1016/j.amc.2018.06.016", URL = "http://www.sciencedirect.com/science/article/pii/S0096300318305046", abstract = "Apparent shear stress acting on a vertical interface between the main channel and floodplain in a compound channel is used to quantify the momentum transfer between these sub-areas of a cross section. In order to simulate the apparent shear stress, two soft computing techniques, including the Genetic Algorithm-Artificial Neural Network (GA-ANN) and Genetic Programming (GP) along with Multiple Linear Regression (MLR) were used. The proposed GA-ANN is a novel self-hidden layer neuron adjustable hybrid method made by combining the Genetic Algorithm (GA) with the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) method. In order to find the optimum condition of the methods considered in modeling apparent shear stress, various input combinations, fitness functions, transfer functions (for the GAA method), and mathematical functions (for the GP method) were investigated. Finally, the results of the optimum GAA and GP methods were compared with the MLR as a basic method. The results show that the hybrid GAA method with RMSE of 0.5326 outperformed the GP method with RMSE of 0.6651. In addition, the results indicate that both GAA and GP methods performed significantly better than MLR with RMSE of 1.5409 in simulating apparent shear stress in symmetric compound channels", } @Misc{journals/corr/abs-2002-02751, author = "Hossein Bonakdari and Isa Ebtehaj and Bahram Gharabaghi and Ali Sharifi and Amir Mosavi", title = "Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming", howpublished = "arXiv", year = "2020", volume = "abs/2002.02751", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "https://arxiv.org/abs/2002.02751", bibdate = "2020-02-10", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2002.html#abs-2002-02751", } @InProceedings{conf/sai/BonakdariGES20, author = "Hossein Bonakdari and Bahram Gharabaghi and Isa Ebtehaj and Ali Sharifi", title = "A New Approach to Estimate the Discharge Coefficient in Sharp-Crested Rectangular Side Orifices Using Gene Expression Programming", booktitle = "Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 3", year = "2020", editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia", pages = "77--96", series = "Advances in Intelligent Systems and Computing", volume = "1230", publisher = "Springer", keywords = "genetic algorithms, genetic programming, gene expression programming", isbn13 = "978-3-030-52242-1", bibdate = "2020-07-07", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sai/sai2020-3.html#BonakdariGES20", DOI = "doi:10.1007/978-3-030-52243-8_7", } @Article{bonakdari:2020:Entropy, author = "Hossein Bonakdari and Azadeh Gholami and Amir Mosavi and Amin Kazemian-Kale-Kale and Isa Ebtehaj and Amir Hossein Azimi", title = "A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle", journal = "Entropy", year = "2020", volume = "22", number = "11", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "1099-4300", URL = "https://www.mdpi.com/1099-4300/22/11/1218", DOI = "doi:10.3390/e22111218", abstract = "This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier (λ) as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope (St¯) value comprehensively using a Gene Expression Programming (GEP) by knowing the initial information (discharge (Q) and mean sediment size (d50)) related to the intended problem. An explicit and simple equation of the St¯ of banks and the geometric and hydraulic parameters of flow is introduced based on the GEP in combination with the previous shape profile equation related to previous researchers. Therefore, a reliable numerical hybrid model is designed, namely Entropy-based Design Model of Threshold Channels (EDMTC) based on entropy theory combined with the evolutionary algorithm of the GEP model, for estimating the bank profile shape and also dimensions of threshold channels. A wide range of laboratory and field data are used to verify the proposed EDMTC. The results demonstrate that the used Shannon entropy model is accurate with a lower average value of Mean Absolute Relative Error (MARE) equal to 0.317 than a previous model proposed by Cao and Knight (1997) (MARE = 0.98) in estimating the bank profile shape of threshold channels based on entropy for the first time. Furthermore, the EDMTC proposed in this paper has acceptable accuracy in predicting the shape profile and consequently, the dimensions of threshold channel banks with a wide range of laboratory and field data when only the channel hydraulic characteristics (e.g., Q and d50) are known. Thus, EDMTC can be used in threshold channel design and implementation applications in cases when the channel characteristics are unknown. Furthermore, the uncertainty analysis of the EDMTC supports the models high reliability with a Width of Uncertainty Bound (WUB) of ±0.03 and standard deviation (Sd) of 0.24.", notes = "also known as \cite{e22111218}", } @InProceedings{bonarini:1999:CRLAACFLCS, author = "Andrea Bonarini", title = "Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "52--59", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-876.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{conf/icnc/BonfimC05, title = "{FranksTree:} A Genetic Programming Approach to Evolve Derived Bracketed {L-systems}", author = "Danilo Mattos Bonfim and Leandro Nunes {de Castro}", year = "2005", pages = "1275--1278", editor = "Lipo Wang and Ke Chen and Yew-Soon Ong", booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3611", address = "Changsha, China", month = aug # " 27-29", bibdate = "2005-07-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2005-1.html#BonfimC05", keywords = "genetic algorithms, genetic programming, interactive evolution", ISBN = "3-540-28325-0", DOI = "doi:10.1007/11539087_168", size = "4 pages", abstract = "L-system is a grammar-like formalism introduced to simulate the development of organisms. The L-system grammar can be viewed as a sort of genetic information that will be used to generate a specific structure. However, throughout development, the string (genetic information) that will effectively be used to 'draw' the phenotype of an individual is a result of the derivation of the L-system grammar. This work investigates the effect of applying a genetic programming approach to evolve derived L-systems instead of evolving the Lsystem grammar. The crossing over of plants from different species results in hybrid plants resembling a 'Frankstree', i.e. plants resultant from phenotypically different parents that present unusual body structures.", notes = " Crossover based on identifying branches in pictures? No mutation. population=6", } @InProceedings{bongard:1999:ECAL, author = "Josh C. Bongard", title = "Coevolutionary Dynamics of a Multi-population Genetic Programming System", booktitle = "Advances in Artificial Life", year = "1999", editor = "D. Floreano and J.-D. Nicoud and F. Mondada", volume = "1674", series = "LNAI", pages = "154", address = "Lausanne", month = "13-17 " # sep, publisher = "Springer Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-66452-1", URL = "http://www.cs.uvm.edu/~jbongard/papers/s067.ps.gz", URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66452-1", URL = "http://citeseer.ist.psu.edu/319504.html", notes = "ECAL-99", } @InProceedings{bongard:2000:legion, author = "Josh C. Bongard", title = "The Legion System: A Novel Approach to Evolving Heterogeneity for Collective Problem Solving", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "16--28", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-67339-3", DOI = "doi:10.1007/978-3-540-46239-2_2", abstract = "We investigate the dynamics of agent groups evolved to peform a collective task, and in which the behavioural heterogeneity of the group is under evolutionary control. Two task domains are studied: solutions are evolved for the two tasks using an evolutionary algorithm called the Legion system. A new metric of heterogeneity is also introduced, which measures the heterogeneity of evolved group behaviours. It was found that the amount of heterogeneity evolved in an agent group is dependent on the given problem domain: for the first task, the Legion system evolved heterogeneous groups; for the second task, primarily homogeneous groups evolved. We conclude that the proposed system, in conjunction with the introduced heterogeneity measure, can be used as a tool for investigating various issues concerning redundancy, robustness and division of labour in the context of evolutionary approaches to collective problem solving.", notes = "EuroGP'2000, part of \cite{poli:2000:GP}", } @Article{Bongard:2007:PNAS, author = "Josh Bongard and Hod Lipson", title = "Automated reverse engineering of nonlinear dynamical systems", journal = "PNAS, Proceedings of the National Academy of Sciences of the United States of America", year = "2007", volume = "104", number = "24", pages = "9943--9948", month = "12 " # jun, keywords = "genetic algorithms, genetic programming, Physical Sciences, Computer Sciences, coevolution, modelling, symbolic identification", DOI = "doi:10.1073/pnas.0609476104", size = "6 pages", abstract = "Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilising the system to extract its less observable characteristics, and automatically simplifying the equations during modelling. We demonstrate this method on four simulated and two real systems spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated reverse engineering approaches for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future.", notes = "Cited by Philosophy of Science Machine Science James A. Evans and Andrey Rzhetsky Science 23 July 2010: Vol. 329 no. 5990 pp. 399-400 DOI:10.1126/science.1189416", } @Article{Bongard:2009:TEC, author = "Josh C. Bongard", title = "Accelerating Self-Modeling in Cooperative Robot Teams", journal = "IEEE Transactions on Evolutionary Computation", year = "2009", volume = "13", number = "2", pages = "321--332", month = apr, keywords = "genetic algorithms, genetic programming, Robots, Robot sensing systems, Training data, Sensors, Data models, Service robots, Computational modeling, self-modeling, Collective robotics, evolutionary robotics", DOI = "doi:10.1109/TEVC.2008.927236", abstract = "One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.", } @InCollection{Bongard:2009:GPTP, author = "Josh Bongard", title = "A Functional Crossover Operator for Genetic Programming", booktitle = "Genetic Programming Theory and Practice {VII}", year = "2009", editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy", series = "Genetic and Evolutionary Computation", address = "Ann Arbor", month = "14-16 " # may, publisher = "Springer", chapter = "12", pages = "195--210", keywords = "genetic algorithms, genetic programming, homologous crossover, crossover operators, system identification", isbn13 = "978-1-4419-1653-2", DOI = "doi:10.1007/978-1-4419-1626-6_12", abstract = "Practitioners of evolutionary algorithms in general, and of genetic programming in particular, have long sought to develop variation operators that automatically preserve and combine useful genetic substructure. This is often pursued with crossover operators that swap genetic material between genotypes that have survived the selection process. However in genetic programming, crossover often has a large phenotypic effect, thereby drastically reducing the probability of a beneficial crossover event. In this paper we introduce a new crossover operator, Functional crossover (FXO), which swaps subtrees between parents based on the subtrees' functional rather than structural similarity. FXO is employed in a genetic programming system identification task, where it is shown that FXO often outperforms standard crossover on both simulated and physically-generated data sets.", notes = "part of \cite{Riolo:2009:GPTP}", } @InProceedings{Bongard:2010:gecco, author = "Josh C. Bongard", title = "A probabilistic functional crossover operator for genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "925--932", keywords = "genetic algorithms, genetic programming", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830649", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The original mechanism by which evolutionary algorithms were to solve problems was to allow for the gradual discovery of sub-solutions to sub-problems, and the automated combination of these sub-solutions into larger solutions. This latter property is particularly challenging when recombination is performed on genomes encoded as trees, as crossover events tend to greatly alter the original genomes and therefore greatly reduce the chance of the crossover event being beneficial. A number of crossover operators designed for tree-based genetic encodings have been proposed, but most consider crossing genetic components based on their structural similarity. In this work we introduce a tree-based crossover operator that probabilistically crosses branches based on the behavioural similarity between the branches. It is shown that this method outperforms genetic programming without crossover, random crossover, and a deterministic form of the crossover operator in the symbolic regression domain.", notes = "Also known as \cite{1830649} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @Article{Bongard:2012:ieeetec, author = "Josh C. Bongard", title = "Innocent Until Proven Guilty: Reducing Robot Shaping from Polynomial to Linear Time", journal = "IEEE Transactions on Evolutionary Computation", year = "2011", volume = "15", number = "4", pages = "571--585", month = aug, keywords = "genetic algorithms, genetic programming, Early stopping, Evolutionary computation, Joints, Manipulators, Neurons, Robot sensing systems, evolutionary robotics", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2010.2096540", abstract = "In evolutionary algorithms, much time is spent evaluating inferior phenotypes that produce no offspring. A common heuristic to address this inefficiency is to stop evaluations early if they hold little promise of attaining high fitness. However, the form of this heuristic is typically dependent on the fitness function used, and there is a danger of prematurely stopping evaluation of a phenotype that may have recovered in the remainder of the evaluation period. Here a stopping method is introduced that gradually reduces fitness over the phenotype's evaluation, rather than accumulating fitness. This method is independent of the fitness function used, only stops those phenotypes that are guaranteed to become inferior to the current offspring-producing phenotypes, and realises significant time savings across several evolutionary robotics tasks. It was found that for many tasks, time complexity was reduced from polynomial to sublinear time, and time savings increased with the number of training instances used to evaluate a phenotype as well as with task difficulty.", notes = "Also known as \cite{5703121}", } @Misc{Bongard:2017:ECML, author = "Joshua Bongard and Anuradha Kodali and Marcin Szubert and Kamalika Das and Sangram Ganguly", title = "Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis", year = "2017", keywords = "genetic algorithms, genetic programming, earth resources and remote sensing", bibsource = "OAI-PMH server at ntrs.nasa.gov", coverage = "Unclassified, Unlimited, Publicly available", date = "20170918", identifier = "Document ID: 20170011183", oai = "oai:casi.ntrs.nasa.gov:20170011183", rights = "Copyright, Public use permitted", URL = "http://hdl.handle.net/2060/20170011183", abstract = "The tropical rainforests are the largest reserves of terrestrial carbon sink and therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and ongoing drought since 2000 in the Congo region there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. Contradicting research has claimed that these forests are resilient to such extreme climatic events. A significant reason behind these diverse conclusions is the lack of a holistic spatio-temporal analysis of the remote sensing data available for these regions. Small scale studies that use statistical correlation measure and simple linear regression to model the climate-vegetation interactions have suffered from the lack of complete data representation and the use of simple (linear) models that fail to represent physical processes accurately, thereby leading to inconclusive or incorrect predictions about the future. In this paper we use a genetic programming (GP) based approach called symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space and build a nonlinear GP model for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Overall it shows that the rainforests exhibit tremendous resiliency in the face of severe droughts. Based on the partitioning of the observed data points, we can conclude that in the absence of adequate precipitation, the trees adopt to reach a different steady state and recover as soon as precipitation is back to normal.", notes = "Submitted to ECML PKDD 2017 ecmlpkdd2017.ijs.si ARC-E-DAA-TN42006; The European Conference on Machine Learning \& Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017) ; 18-22 Sep. 2017; Skopje; Macedonia May 2018 This requested resource (Online NTRS full-text PDF) is no longer available from NTRS. email at help@sti.nasa.gov also known as \cite{oai:casi.ntrs.nasa.gov:20170011183}", } @InProceedings{bonham:1999:AIEEWCOGA, author = "Christopher R. Bonham and Ian C. Parmee", title = "An Investigation of Exploration and Exploitation Within Cluster Oriented Genetic Algorithms (COGAs)", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1491--1497", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "real world applications", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-765.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/RW-765.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @Article{Bonifaci:2013:GPEM, author = "Vincenzo Bonifaci", title = "Andrew Adamatzky: Physarum Machines: Computers from Slime Mould", journal = "Genetic Programming and Evolvable Machines", year = "2013", volume = "14", number = "1", pages = "123--124", month = mar, note = "Book Review", keywords = "genetic algorithms, genetic programming, evolvable life", ISSN = "1389-2576", URL = "https://rdcu.be/dR8fU", DOI = "doi:10.1007/s10710-012-9169-2", size = "2 pages", } @Article{Pedraza:2011:JSC, author = "Cesar Pedraza and Javier Castillo and Jose I. Martinez and Pablo Huerta and Jose L. Bosque and Javier Cano", title = "Genetic Algorithm for {Boolean} minimization in an {FPGA} cluster", journal = "The Journal of Supercomputing", year = "2011", volume = "58", number = "2", pages = "244--252", month = nov, note = "Special issue on HPC in computational Science and Engineering. Part I", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Performance evaluation, FPGA, Boolean synthesis, Hardware co-design", ISSN = "0920-8542", DOI = "doi:10.1007/s11227-010-0401-7", size = "9 pages", abstract = "Evolutionary algorithms are an alternative option to the Boolean synthesis due to that they allow one to create hardware structures that would not be able to be obtained with other techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a cluster of FPGAs that takes full advantage of parallel programming and hardware/software co-design techniques. The performance of our cluster of FPGAs implementation has been compared with an HPC implementation. The experimental results have shown an excellent behaviour in terms of speed up (up to x500) and in terms of solving the scalability problems of this algorithms present in previous works.", notes = "SMILE cluster, gigabit ethernet, ALTAMIRA JS20 VHDL. cited by \cite{Bonilla:2011:LASCAS}", affiliation = "DATCCCIA, ETSII, Universidad Rey Juan Carlos, Madrid, Spain", } @InProceedings{Bonilla:2011:LASCAS, author = "Cesar Pedraza Bonilla and Carlos Ivan Camargo", title = "Low Cost Platform for Evolvable-Based {Boolean} Synthesis", booktitle = "IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), 2011", year = "2011", month = feb, abstract = "Evolutionary algorithms are another option for combinational synthesis because they allow for the generation of hardware structures that cannot be obtained with other techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a low cost cluster of an embedded platform called SIE, based on a 32-bit processor and a Spartan-3 FPGA. Some tasks of the PGP have been accelerated in hardware and results have been compared with an HPC implementation, resulting in speedup values up to approximately 180.", keywords = "genetic algorithms, genetic programming, 32-bit processor, HPC implementation, PGP Boolean synthesis implementation, SIE, combinational synthesis, embedded platform, evolutionary algorithms, evolvable-based Boolean synthesis, hardware structures, low cost cluster, low cost platform, parallel genetic programming, spartan-3 FPGA, speedup values, Boolean functions, combinational circuits, embedded systems, field programmable gate arrays, logic design, microprocessor chips", DOI = "doi:10.1109/LASCAS.2011.5750310", notes = "Also known as \cite{5750310}", } @Article{Pedraza_Oyaga_Gomez_2013, author = "Cesar A. Pedraza and Jaime V. Oyaga and Ricardo C. Gomez", title = "Sintesis booleanacon programacion genetica paralela en {CPU y GPU}", title_en = "Genetic parallel programing-based Boolean synthesis with CPU and GPU", journal = "Ingenium Revista de la facultad de ingenieria", year = "2013", volume = "14", pages = "117--130", month = "ene.", keywords = "genetic algorithms, genetic programming, Programacion paralela, sintesis booleana, GPU, algoritmo evolutivo, Parallel programming, Boolean synthesis, GPU, evolutionary algorithm", URL = "https://revistas.usb.edu.co/index.php/Ingenium/article/view/1325/1116", URL = "https://revistas.usb.edu.co/index.php/Ingenium/article/view/1325", DOI = "doi:10.21500/01247492.1325", size = "14 pages", resumen = "La sintesis booleana o combinacional es un proceso mediante el cual se optimiza una red de puertas logicas, con el fin de reducir su consumo, minimizar costos, minimizar area y aumentar el rendimiento a la hora de ser implementada. Por otra parte, la programacion genetica es una alternativa importante para generar estructuras de hardware interesantes y eficientes. Se ha demostrado que los algoritmos evolutivos (AE) tienen mejor rendimiento si se implementan en sistemas paralelos. Este articulo presenta la implementacion de un algoritmo genetico paralelo (PGP) para realizar sintesis booleana en una plataforma basada en CPU-GPU. Esta implementacion emplea el modelo de islas, el cual permite la evolucion paralela e independiente del PGP a traves de las multiples unidades de procesamiento de la GPU y los multiples nucleos de un procesador de ultima generacion. Se probaron diferentes alternativas de mapeo del PGP en la plataforma en orden de optimizar el tiempo de respuesta. Como resultado se muestra una aceleracion superior a 41.", abstract = "The Boolean or combinational synthesis is a process that optimizes a logic gates net-work, in order to reduce power consumption, minimize costs, minimize area and increase the performance when it is implemented. Moreover genetic programming is an important alternative to generate interesting and efficient hardware structures. It has been shown that evolvable algorithms are faster when implemented in parallel systems. This paper presents the implementation of a parallel genetic programming (PGP) for boolean synthesis on a GPU-CPU based platform. Our implementation uses the island mode which allows the parallel and independent evolution of the PGP through the multiple processing units of the GPU and the multiple cores of a new generation desktop processors. We tested multiple mapping alternatives of the PGP on the platform in order to optimize the PGP response time. As a result we show that our approach achieves a speedup up to 41.", notes = "in spanish Universidad Santo Tomas", } @Article{bonin:2024:GPEM, author = "Lorenzo Bonin and Luigi Rovito and Andrea {De Lorenzo} and Luca Manzoni", title = "Cellular geometric semantic genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no: 8", note = "Online first", keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming, Cellular genetic programming", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09480-8", size = "32 pages", } @InProceedings{Bonson:2016:SSCI, author = "J. P. C. Bonson and A. R. McIntyre and M. I. Heywood", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)", title = "On novelty driven evolution in Poker", year = "2016", abstract = "This work asks the question as to whether `novelty as an objective' is still beneficial under tasks with a lot of ambiguity, such as Poker. Specifically, Poker represents a task in which there is partial information (public and private cards) and stochastic changes in state (what card will be dealt next). In addition, bluffing plays a fundamental role in successful strategies for playing the game. On the face of it, it appears that multiple sources of variation already exist, making the additional provision of novelty as an objective unwarranted. Indeed, most previous work in which agent strategies are evolved with novelty appearing as an explicit objective are not rich in sources of ambiguity. Conversely, the task of learning strategies for playing Poker, even under the 2-player case of heads-up Limit Texas Hold'em, is widely considered to be particularly challenging on account of the multiple sources of uncertainty. We benchmark a form of genetic programming, both with and without (task independent) novelty objectives. It is clear that pursuing behavioural diversity, even under the heads-up Limit Texas Hold'em task is central to learning successful strategies. Benchmarking against static and Bayesian opponents illustrates the capability of the resulting Genetic Programming (GP) agents to bluff and vary their style of play.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2016.7849968", month = dec, notes = "Also known as \cite{7849968}", } @InProceedings{Bonte:2010:AIAI, author = "Bert Bonte and Bart Wyns", title = "Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming", booktitle = "6th IFIP Advances in Information and Communication Technology AIAI 2010", year = "2010", editor = "Harris Papadopoulos and Andreas Andreou and Max Bramer", volume = "339", series = "IFIP Advances in Information and Communication Technology", pages = "86--93", address = "Larnaca, Cyprus", month = oct # " 6-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1007/978-3-642-16239-8_14", abstract = "Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration.", affiliation = "Department of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, 9052 Zwijnaarde, Belgium", notes = "http://www.cs.ucy.ac.cy/aiai2010/", } @InProceedings{Bonyadi:2007:ICEE, author = "M. R. Bonyadi and S. M. R. Azghadi and N. M. Rad and K. Navi and E. Afjei", title = "Logic Optimization for Majority Gate-Based Nanoelectronic Circuits Based on Genetic Algorithm", booktitle = "International Conference on Electrical Engineering, 2007. ICEE '07", year = "2007", address = "Lahore", month = "11-12 " # apr, keywords = "genetic algorithms, genetic programming, QCA", ISBN = "1-4244-0893-8", DOI = "doi:10.1109/ICEE.2007.4287307", size = "5 pages", abstract = "In this paper we propose a novel and efficient method for majority gate-based design. The basic Boolean primitive in quantum cellular automata (QCA) is the majority gate. Method for reducing the number of majority gates required for computing Boolean functions is developed to facilitate the conversion of sum of products (SOP) expression into QCA majority logic. This method is based on genetic algorithm and can reduce the hardware requirements for a QCA design. We will show that the proposed approach is very efficient in deriving the simplified majority expression in QCA design.", notes = "Also known as \cite{4287307}", } @InCollection{booker:2000:EC1, author = "Lashon B. Booker and David B. Fogel and Darrell Whitley and Peter J. Angeline and A. E. Eiben", title = "Recombination", booktitle = "Evolutionary Computation 1 Basic Algorithms and Operators", publisher = "Institute of Physics Publishing", year = "2000", editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz", chapter = "33", pages = "256--307", address = "Bristol", keywords = "genetic algorithms, genetic programming", ISBN = "0-7503-0664-5", URL = "http://www.crcpress.com/product/isbn/9780750306645", notes = "section 33.5 parse trees p286--289", size = "52 pages", } @Article{Boone:2017:CZ, author = "Randall B. Boone", title = "Evolutionary computation in zoology and ecology", journal = "Current Zoology", year = "2017", volume = "63", number = "6", pages = "675--686", month = dec, keywords = "genetic algorithms, genetic programming, agent-based modeling, case studies, evolutionary programming, evolutionary strategies", ISSN = "1674-5507", URL = "https://doi.org/10.1093/cz/zox057", DOI = "doi:10.1093/cz/zox057", size = "12 pages", abstract = "Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.", notes = "Natural Resource Ecology Laboratory and the Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1499, USA", } @InProceedings{booth:2004:eurogp, author = "Richard F. Booth and Alexandre V. Borovik", title = "Coevolution of Algorithms and Deterministic Solutions of Equations in Free Groups", booktitle = "Genetic Programming 7th European Conference, EuroGP 2004, Proceedings", year = "2004", editor = "Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule", volume = "3003", series = "LNCS", pages = "11--22", address = "Coimbra, Portugal", publisher_address = "Berlin", month = "5-7 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-21346-5", DOI = "doi:10.1007/978-3-540-24650-3_2", abstract = "We discuss the use of evolutionary algorithms for solving problems in combinatorial group theory, using a class of equations in free groups as a test bench. We find that, in this context, there seems to be a correlation between successful evolutionary algorithms and the existence of good deterministic algorithms. We also trace the convergence of co-evolution of the population of fitness functions to a deterministic solution.", notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004", } @InProceedings{conf/ic3k/BorcheninovO11, author = "Yaroslav V. Borcheninov and Yuri S. Okulovsky", title = "Genetic Programming with Embedded Features of Symbolic Computations", booktitle = "KDIR International Conference on Knowledge Discovery and Information Retrieval", year = "2011", editor = "Joaquim Filipe and Ana L. N. Fred", pages = "476--479", address = "Paris, France", month = "26-29 " # oct, publisher = "SciTePress", keywords = "genetic algorithms, genetic programming: poster", isbn13 = "978-989-8425-79-9", DOI = "doi:10.5220/0003682004760479", abstract = "Genetic programming is a methodology, widely used in data mining for obtaining the analytic form that describes a given experimental data set. In some cases, genetic programming is complemented by symbolic computations that simplify found expressions. We propose to unify the induction of genetic programming with the deduction of symbolic computations in one genetic algorithm. Our approach was implemented as the .NET library and successfully tested at various data mining problems: function approximation, invariants finding and classification.", notes = "Paper Nr: 126 http://www.ic3k.org/IC3K2011/ http://www.ic3k.org/IC3K2011/IC3K_paperList.htm http://www.kdir.ic3k.org/Abstracts/2011/KDIR_2011_Abstracts.htm", bibdate = "2012-05-04", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ic3k/kdir2011.html#BorcheninovO11", } @InProceedings{Borcheninov:2012:SYRCoSE, title = "Internal and online simplification in genetic programming: an experimental comparison", author = "Yaroslav Borcheninov and Yuri Okulovsky", booktitle = "Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering", year = "2012", editor = "Alexander S. Kamkin and Alexander K. Petrenko and Andrey N. Terekhov", volume = "6", pages = "134--138", address = "Perm, Russia", month = may, organisation = "Institute for System Programming of the Russian Academy of Sciences (ISPRAS) and Saint-Petersburg State University (SPbSU) jointly with NRU HSE", keywords = "genetic algorithms, genetic programming, symbolic computations, computer algebra systems", isbn13 = "978-5-91474-019-8", annote = "The Pennsylvania State University CiteSeerX Archives", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.300.1191", rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.1191", URL = "http://syrcose.ispras.ru/2012/files/submissions/22_syrcose2012_submission_1.pdf", URL = "http://syrcose.ispras.ru/?q=node/30", DOI = "doi:10.15514/SYRCOSE-2012-6-22", size = "5 pages", abstract = "Genetic programming is an evolutionary algorithm, which allows performing symbolic regression --- the important task of obtaining the analytical form of a model by the data, produced by the model. One of the known problems of genetic programming is expressions bloating that results in ineffectively long expressions. To prevent bloating, symbolic simplification of expression is used. We introduce a new approach to simplification in genetic programming, making it a uniform part of the evolutionary process. To do that, we develop a genetic programming on the basis of transformation rules, similarly to computer algebra systems. We compare our approach with existed solution, and prove its adequacy and effectiveness.", notes = "Ural Federal University, Yekaterinburg, Lenina str. 51. http://syrcose.ispras.ru/ https://socionet.ru/collection.xml?h=spz:cyberleninka:31892&page=4&s=sa", } @InProceedings{Borg:2007:CSAW, author = "Claudia Borg and Mike Rosner and Gordon Pace", title = "Towards Automatic Extraction of Definitions from Text", booktitle = "5th Computer Science Annual Workshop CSAW 2007", year = "2007", editor = "Claudia Borg and Sandro Spina and Charlie Abela", address = "Bighi, Malta", month = "5-6 " # nov, organisation = "CSAI Department - University of Malta", keywords = "genetic algorithms, genetic programming", language = "en", oai = "oai:CiteSeerX.psu:10.1.1.296.4714", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.4714", URL = "http://staff.um.edu.mt/cbor7/publications/csaw_msc.pdf", size = "14 pages", abstract = "Definition Extraction can be useful for the creation of glossaries and in Question Answering Systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we will review some attempts at rule-based approaches and discuss their results. We will then propose a novel experiment involving Genetic Programming and Genetic Algorithms, aimed at assisting the discovery of grammar rules which can be used for the task of Definition Extraction.", notes = "http://www.cs.um.edu.mt/~csaw/programme.html", } @InProceedings{Borges:2010:gecco, author = "Cruz E. Borges and Cesar L. Alonso and Jose L. Montana", title = "Model selection in genetic programming", booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", isbn13 = "978-1-4503-0072-8", pages = "985--986", keywords = "genetic algorithms, genetic programming, Poster", month = "7-11 " # jul, organisation = "SIGEVO", address = "Portland, Oregon, USA", DOI = "doi:10.1145/1830483.1830662", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "In this paper we discuss the problem of model selection in Genetic Programming. We present empirical comparisons between classical statistical methods (AIC, BIC) adapted to Genetic Programming and the Structural Risk Minimisation method (SRM) based on Vapnik-Chervonenkis theory (VC), for symbolic regression problems with added noise. We also introduce a new model complexity measure for the SRM method that tries to measure the non-linearity of the model. The experimentation suggests practical advantages of using VC-based model selection with the new complexity measure, when using genetic training.", notes = "Also known as \cite{1830662} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)", } @InProceedings{Borges:2010:ICEC, author = "Cruz Enrique Borges and Cesar L. Alonso and Jose Luis Montana and Marina {de la Cruz Echeandia} and Alfonso {Ortega de la Puente}", title = "Coevolutionary Architectures with Straight Line Programs for solving the Symbolic Regression Problem", booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)", year = "2010", editor = "Joaquim Filipe and Janusz Kacprzyk", pages = "41--50", address = "Valencia, Spain", month = "24-26 " # oct, organisation = "INSTICC, AAAI, WfMC", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-8425-31-7", URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/10ICEC.pdf", URL = "https://www.scitepress.org/PublishedPapers/2010/30751/", URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf", DOI = "doi:10.5220/0003075100410050", size = "10 pages", abstract = "To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort.", notes = " https://ecta.scitevents.org/ICEC2010/Program_Monday.htm Broken http://www.icec.ijcci.org/ICEC2010/home.asp Broken http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm Also known as \cite{DBLP:conf/ijcci/BorgesAMEP10}", } @PhdThesis{BorgesHernandez:thesis, author = "Cruz Enrique {Borges Hernandez}", title = "Programacion Genetica, Algoritmos Evolutivos y Aprendizaje Inductivo: Hacia una solucion al problema xvii de Smale en el caso real", school = "Universidad de Cantabria Departamento de Matematicas, Estadistica y Computacion", year = "2010", address = "Santander, Spain", month = "25 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/11Tesis.pdf", size = "215 pages", notes = "In Spanish Dirigida por: Jose Luis Montana Arnaiz and Luis Miguel Pardo Vasallo", } @Article{Borges2011706, author = "Eduardo N. Borges and Moises G. {de Carvalho} and Renata Galante and Marcos Andre Goncalves and Alberto H. F. Laender", title = "An unsupervised heuristic-based approach for bibliographic metadata deduplication", journal = "Information Processing \& Management", volume = "47", number = "5", pages = "706--718", year = "2011", note = "Managing and Mining Multilingual Documents", ISSN = "0306-4573", DOI = "doi:10.1016/j.ipm.2011.01.009", URL = "http://www.sciencedirect.com/science/article/pii/S0306457311000100", keywords = "genetic algorithms, genetic programming, Digital libraries, Metadata, Deduplication, Similarity", abstract = "Digital libraries of scientific articles contain collections of digital objects that are usually described by bibliographic meta data records. These records can be acquired from different sources and be represented using several metadata standards. These metadata standards may be heterogeneous in both, content and structure. All of this implies that many records may be duplicated in the repository, thus affecting the quality of services, such as searching and browsing. In this article we present an approach that identifies duplicated bibliographic metadata records in an efficient and effective way. We propose similarity functions especially designed for the digital library domain and experimentally evaluate them. Our results show that the proposed functions improve the quality of metadata de-duplication up to 188percent compared to four different baselines. We also show that our approach achieves statistical equivalent results when compared to a state-of-the-art method for replica identification based on genetic programming, without the burden and cost of any training process.", } @InProceedings{Boric:2007:cec, author = "Neven Boric and Pablo A. Estevez", title = "Genetic Programming-Based Clustering Using an Information Theoretic Fitness Measure", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "31--38", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1285.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424451", abstract = "A clustering method based on multitree genetic programming and an information theoretic fitness is proposed. A probabilistic interpretation is given to the output of trees that does not require a conflict resolution phase. The method can cluster data with irregular shapes, estimate the underlying models of the data for each class and use those models to classify unseen patterns. The proposed scheme is tested on several real and artificial data sets, outperforming k-means algorithm in all of them.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InProceedings{conf/evoW/BorlikovaPSO16, author = "Gilyana Borlikova and Michael Phillips and Louis Smith and Michael O'Neill", title = "Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution", booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016", year = "2016", editor = "Giovanni Squillero and Paolo Burelli", volume = "9597", series = "Lecture Notes in Computer Science", pages = "46--57", address = "Porto, Portugal", month = mar # " 30 -- " # apr # " 1", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Clinical trials, Enrolment, Grammar-based genetic programming", bibdate = "2016-03-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#BorlikovaPSO16", isbn13 = "978-3-319-31204-0", URL = "https://link.springer.com/chapter/10.1007/978-3-319-31204-0_4", DOI = "doi:10.1007/978-3-319-31204-0_4", abstract = "Successful and timely completion of prospective clinical trials depends on patient recruitment as patients are critical to delivery of the prospective trial data. There exists a pressing need to develop better tools/techniques to optimise patient recruitment in multi-centre clinical trials. In this study Grammatical Evolution (GE) is used to evolve classification models to predict future patient enrolment performance of investigators/site to be selected for the conduct of the trial. Prediction accuracy of the evolved models is compared with results of a range of machine learning algorithms widely used for classification. The results suggest that GE is able to successfully induce classification models and analysis of these models can help in our understanding of the factors providing advanced indication of a trial sites' future performance.", notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016", } @InProceedings{Borlikova:2017:GECCO, author = "Gilyana Borlikova and Michael O'Neill and Louis Smith and Michael Phillips", title = "Development of a Multi-model System to Accommodate Unknown Misclassification Costs in Prediction of Patient Recruitment in Multicentre Clinical Trials", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion", series = "GECCO '17", year = "2017", isbn13 = "978-1-4503-4939-0", address = "Berlin, Germany", pages = "263--264", size = "2 pages", URL = "http://doi.acm.org/10.1145/3067695.3076062", DOI = "doi:10.1145/3067695.3076062", acmid = "3076062", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, keywords = "genetic algorithms, genetic programming, Grammatical evolution", abstract = "Clinical trials are an essential step in a new drug's approval process. Optimisation of patient recruitment is one of the major challenges facing pharma and contract research organisations (CRO) in conducting multicentre clinical trials. Improving the quality of selection of investigators/sites at the start of a trial can help to address this business problem. Grammatical Evolution (GE) was previously used to evolve classification models to predict the future patient enrolment performance of investigators/sites considered for a trial. However, the unknown target misclassification costs at the model development stage pose additional challenges. To address them we use a new composite fitness function to develop a multi-model system of decision-tree type classifiers that optimise a range of possible trade-offs between the correct classification and errors. The predictive power of the GE-evolved models is compared with a range of machine learning algorithms widely used for classification. The results of the study demonstrate that the GE-evolved multi-model system can help to circumvent uncertainty at the model development stage by providing a collection of customised models for rapid deployment in response to business needs of a clinical trial.", notes = "Also known as \cite{Borlikova:2017:DMS:3067695.3076062} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)", } @InProceedings{10.1007/978-3-319-55702-1_50, author = "Gilyana Borlikova and Michael Phillips and Louis Smith and Miguel Nicolau and Michael O'Neill", editor = "Andreas Fink and Armin Fuegenschuh and Martin Josef Geiger", title = "Alternative Fitness Functions in the Development of Models for Prediction of Patient Recruitment in Multicentre Clinical Trials", booktitle = "Operations Research Proceedings 2016", year = "2018", publisher = "Springer International Publishing", pages = "375--381", keywords = "genetic algorithms, genetic programming, Grammatical evolution", URL = "https://link.springer.com/chapter/10.1007/978-3-319-55702-1_50", DOI = "doi:10.1007/978-3-319-55702-1_50", abstract = "For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. At present, patient recruitment is a major bottleneck in conducting clinical trials. Pharma and contract research organisations (CRO) are actively looking into optimisation of different aspects of patient recruitment. One of the avenues to approach this business problem is to improve the quality of selection of investigators/sites at the start of a trial. This study builds upon previous work that used Grammatical Evolution (GE) to evolve classification models to predict the future patient enrolment performance of investigators/sites considered for a trial. Selection of investigators/sites, depending on the business context, could benefit from the use of either especially conservative or more liberal predictive models. To address this business need, decision-tree type classifiers were evolved using different fitness functions to drive GE. The functions compared were classical accuracy, balanced accuracy and F-measure with different values of parameter beta. The issue of models' generalisability was addressed by introduction of a validation procedure. The predictive power of the resultant GE-evolved models on the test set was compared with performance of a range of machine learning algorithms widely used for classification. The results of the study demonstrate that flexibility of GE induced classification models can be used to address business needs in the area of patient recruitment in clinical trials.", isbn13 = "978-3-319-55702-1", } @InCollection{Borlikova:2018:hbge, author = "Gilyana Borlikova and Louis Smith and Michael Phillips and Michael O'Neill", title = "Business Analytics and Grammatical Evolution for the Prediction of Patient Recruitment in Multicentre Clinical Trials", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "19", pages = "461--486", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_19", abstract = "For a drug to be approved for human use, its safety and efficacy need to be evidenced through clinical trials. Optimisation of patient recruitment is an active area of business interest for pharma and contract research organisations (CRO) conducting clinical trials. The healthcare industry and CROs are gradually starting to adapt business analytics techniques to improve processes and help boost performance. Development of methods able to predict at the outset which prospective investigators/sites will succeed in patient recruitment can provide powerful tools for this business problem. In this chapter we describe the application of Grammatical Evolution to the prediction of patient recruitment in multicentre clinical trials.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Borrelli:2006:PhysicaA, author = "A. Borrelli and I. {De Falco} and A. {Della Cioppa} and M. Nicodemi and G. Trautteur", title = "Performance of genetic programming to extract the trend in noisy data series", journal = "Physica A: Statistical and Theoretical Physics", year = "2006", volume = "370", number = "1", pages = "104--108", month = "1 " # oct, note = "Econophysics Colloquium - Proceedings of the International Conference {"}Econophysics Colloquium{"}", keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming, Stochastic time series", DOI = "doi:10.1016/j.physa.2006.04.025", abstract = "In this paper an approach based on genetic programming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is applied to the MIB30 Index series.", } @Article{Borrett:2023:GPEM, author = "Fraser Borrett and Mark Beckerleg", title = "A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot", journal = "Genetic Programming and Evolvable Machines", year = "2023", volume = "24", number = "1", pages = "Article number: 5", month = jun, note = "Online first", keywords = "genetic algorithms, evolvable hardware, Evolutionary robots, Artificial neural network, ANN, Hexapod robotic, Robot gait, MATLAB, EHW, FPGA, ARM", ISSN = "1389-2576", URL = "https://rdcu.be/c8Hyy", DOI = "doi:10.1007/s10710-023-09452-4", video_url = "https://link.springer.com/article/10.1007/s10710-023-09452-4#citeas", size = "30 pages", abstract = "investigates the implementation of a novel evolvable hardware controller used in evolutionary robotics. The evolvable hardware consists of a Cartesian based array of logic blocks comprised of multiplexers and logic elements. The logic blocks are configured by a bit stream which is evolved using a genetic algorithm. A comparison is performed between an evolvable hardware and an artificial neural network controller evolved using the same genetic algorithm to produce the gait of a hexapod robot. To compare the two controllers, differences in their evolutionary efficiency and robot performance are investigated. The evolutionary efficiency is measured by the required number of generations to achieve an optimal fitness. An optimal hexapod controller allows the robot to walk forward in a straight line maintaining a constant heading and body attitude. It was found that the evolutionary efficiency and performance of the evolvable hardware and artificial neural network were similar, however the EHW was the most evolutionary efficient requiring less generations on average to evolve. Both evolved controllers were evaluated in simulation, and on a physical robot using a softcore processor and custom hardware implemented on a FPGA. The implementation showed that the controllers performed equally well when deployed, allowing the hexapod to meet the optimal gait criteria. These findings have shown that the evolvable hardware controller is a valid option for robotic control of a multi-legged robot such as a hexapod as its evolutionary efficiency and deployed performance on a real robot is comparable to that of an artificial neural network. One future application of these evolvable controllers is in fault tolerance where the robot can dynamically adapt to a fault by evolving the controller to adjust to the fault conditions.", notes = " Auckland University of Technology", } @InProceedings{borrett:2024:CEC, author = "Fraser Borrett and Mark Beckerleg", title = "The Virtual Programmable Logic Device, a Novel Machine Learning Architecture", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Performance evaluation, Multiplexing, Legged locomotion, Evolutionary robotics, Programmable logic devices, Artificial neural networks, Machine learning, virtual programmable logic device, artificial neural network, evolvable hardware, evolutionary robots, hexapod robot, robot gait", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612129", abstract = "This paper introduces a novel architecture for robotic control, called a Virtual Programmable Logic Device (VPLD) where the operation of the device can be dynamically configured using machine learning methods. The VPLD is based on the structure of a Programable Logic Device (PLD) comprising of a two-dimensional feed-forward array of function blocks, with each block containing multiplexers and function elements. However, the VPLD is implemented in software rather than hardware allowing the VPLD to be run on a CPU based system, such as PC's and ARM based embedded systems. The operation of the VPLD is determined by a configuration bitstream which configures the multiplexers (routing) and function elements of each block. A genetic algorithm is used to evolve the configuration bitstream to produce the walking gait of a hexapod robot. The controller performance and the evolutionary efficiency of the VPLD are compared with an evolved artificial neural network (ANN), and an evolvable hardware (EHW) device. It was found the VPLD and ANN had similar controller performance and evolutionary efficiency, while the EHW had a comparable controller performance however its evolutionary efficiency was poor. It is shown that the VPLD is a viable alternative to an ANN for evolutionary robotic control.", notes = "also known as \cite{10612129} WCCI 2024", } @InProceedings{borrett:2024:CEC2, author = "Fraser Borrett and Mark Beckerleg", title = "A Comparison of a Digital and Floating-Point Virtual Programmable Logic Device and an Artifical Neural Network Evolved for Robotic Navigation", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Program processors, Navigation, Programmable logic devices, Artificial neural networks, Evolutionary computation, Software, Hardware, Virtual Programmable Logic Device, Evolvable Hardware, Artificial Neural Network, Robotic Navigation, Genetic Algorithm", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612180", abstract = "Two novel architectures, the digital and floatingpoint virtual programmable logic device (VPLD) are compared with an artificial neural network, evolved for the robotic navigational tasks of obstacle avoidance and light following for a two wheeled robot. The VPLD is based on a programmable logic device but is coded in software rather than in hardware. This allows the VPLD to be implemented on CPUs allowing it to be run on a wide range of platforms including PCs, mobile phones and ARM processors. The function of the VPLD is governed by a configuration bitstream which can be evolved by evolutionary computation. It was found that the digital and floating-point VPLDs performed well against the ANN in the navigational tasks, making the VPLD a viable alternative to an ANN for robotic navigation.", notes = "also known as \cite{10612180} WCCI 2024", } @Article{borunda:2020:Energies, author = "Monica Borunda and Katya Rodriguez-Vazquez and Raul Garduno-Ramirez and Javier {de la Cruz-Soto} and Javier Antunez-Estrada and Oscar A. Jaramillo", title = "{Long-Term} Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming", journal = "Energies", year = "2020", volume = "13", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "1996-1073", URL = "https://www.mdpi.com/1996-1073/13/8/1885", DOI = "doi:10.3390/en13081885", abstract = "Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterise it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.", notes = "also known as \cite{en13081885}", } @InProceedings{boryczka:2002:gecco, author = "Mariusz Boryczka and Zbigniew J. Czech", title = "Solving Approximation Problems By Ant Colony Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "133", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, artificial life, adaptive behavior, agents, ant colony optimization, poster paper, ant colony programming, approximation problems, automatic programming", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/aaaa288.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/aaaa288.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-02.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{boryczka:2002:gecco:lbp, author = "Mariusz Boryczka and Zbigniew J. Czech", title = "Solving Approximation Problems by Ant Colony Programming", booktitle = "Late Breaking Papers at the Genetic and Evolutionary Computation Conference ({GECCO-2002})", editor = "Erick Cant{\'u}-Paz", year = "2002", month = jul, pages = "39--46", address = "New York, NY", publisher = "AAAI", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, automatic programming, ant colony programming, ACO, approximation problems", URL = "http://www-zo.iinf.polsl.gliwice.pl/pub/zjc/bc02.ps.Z", URL = "https://dl.acm.org/doi/pdf/10.5555/2955491.2955512", size = "8 pages", abstract = "A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.", notes = "10.5555/2955491.2955512 is one page summary Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms ({ICGA-2002}) and the seventh Annual Genetic Programming Conference ({GP-2002}) part of cantu-paz:2002:GECCO:lbp", } @InProceedings{DBLP:conf/vstte/BosamiyaGLPH20, author = "Jay Bosamiya and Sydney Gibson and Yao Li and Bryan Parno and Chris Hawblitzel", title = "Verified Transformations and Hoare Logic: Beautiful Proofs for Ugly Assembly Language", booktitle = "Software Verification - 12th International Conference, {VSTTE} 2020, and 13th International Workshop, {NSV} 2020", year = "2020", editor = "Maria Christakis and Nadia Polikarpova and Parasara Sridhar Duggirala and Peter Schrammel", volume = "12549", series = "Lecture Notes in Computer Science", pages = "106--123", address = "Los Angeles, CA, USA", month = jul # " 20-21", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, genetic improvement, Hoare Logic", isbn13 = "978-3-030-63618-0", timestamp = "Mon, 04 Jan 2021 16:36:23 +0100", biburl = "https://dblp.org/rec/conf/vstte/BosamiyaGLPH20.bib", bibsource = "dblp computer science bibliography, https://dblp.org", DOI = "doi:10.1007/978-3-030-63618-0_7", size = "18 pages", abstract = "Hand-optimized assembly language code is often difficult to formally verify. This paper combines Hoare logic with verified code transformations to make it easier to verify such code. This approach greatly simplifies existing proofs of highly optimized OpenSSL-based AES-GCM cryptographic code. Furthermore, applying various verified transformations to the AES-GCM code enables additional platform-specific performance improvements.", notes = "p119 'we developed a genetic algorithm to search for faster instruction orderings on a given processor' cited by \cite{Chuengsatiansup:2022:GI}", } @InProceedings{bo-ka-09, author = "Alexander Boschmann and Paul Kaufmann and Marco Platzner and Michael Winkler", title = "Towards Multi-movement Hand Prostheses: Combining Adaptive Classification with High Precision Sockets", booktitle = "Technically Assisted Rehabilitation (TAR)", year = "2009", address = "Berlin, Germany", month = mar # " 18-19", organisation = "Technische Universitaet Berlin", keywords = "genetic algorithms, genetic programming, Electromyographic, EMG", URL = "http://www.ige.tu-berlin.de/fileadmin/fg176/IGE_Printreihe/TAR_2009/paper/05_boschmann.pdf", size = "4 pages", abstract = "The acceptance of hand prostheses strongly depends on their user-friendliness and functionality. Current prostheses are limited to a few movements and their operation is all but intuitive. The development of practically applicable multi-movement prostheses requires the combination of modern classification methods with novel techniques for manufacturing high precision sockets. In this paper, we introduce an approach for classifying EMG signals taken from forearm muscles using support vector machines. This classifier technique is used in an adaptive operation mode and customized to the amputee, which allows us to recognize eleven different hand movements with high accuracy. Then, we present a novel manufacturing technique for prosthesis sockets enabling a precise amputee-specific fitting and EMG sensor placement.", notes = "2nd Conference O.T.W. Orthop Aedietechnik Winkler, Minden, Germany", } @InProceedings{bose:2025:GI, author = "Damien Bose and Carol Hanna and Justyna Petke", title = "Enhancing Software Runtime with Reinforcement Learning-Driven Mutation Operator Selection in Genetic Improvement", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss", address = "Ottawa", month = "27 " # apr, note = "forthcoming", keywords = "genetic algorithms, genetic programming, Genetic Improvement, Reinforcement learning", URL = "https://rps.ucl.ac.uk/viewobject.html?cid=1&id=2360068", URL = "https://gpbib.cs.ucl.ac.uk/gi2025/bose_2025_GI.pdf", URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers", size = "8 pages", abstract = "Genetic Improvement employs heuristic search algorithms to explore the search space of program variants by modifying code using mutation operators. This research focuses on operators that delete, insert and replace source code statements. Traditionally, in GI, an operator is chosen uniformly at random at each search iteration. Reinforcement Learning to intelligently guide the selection of these operators specifically to improve program runtime. We propose to integrate RL into the operator selection process. Four Multi-Armed bandit RL algorithms (Epsilon Greedy, UCB, Probability Matching, and Policy Gradient) were integrated within a GI framework, and their efficacy and efficiency were bench marked against the traditional GI operator selection approach. These RL-guided operator selection strategies have demonstrated empirical superiority over the traditional GI methods of randomly selecting a search operator, with UCB emerging as the top-performing RL algorithm. On average, the UCB-guided Hill Climbing search algorithm produced variants that compiled and passed all tests 44% of the time, while only 22% of the variants produced by the traditional uniform random selection strategies compiled and passed all tests.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI} UCL ID: 10204649", } @Article{Bose20091, author = "Indranil Bose and Xi Chen", title = "Quantitative models for direct marketing: A review from systems perspective", journal = "European Journal of Operational Research", volume = "195", number = "1", pages = "1--16", year = "2009", ISSN = "0377-2217", DOI = "doi:10.1016/j.ejor.2008.04.006", URL = "http://www.sciencedirect.com/science/article/B6VCT-4S7SV3H-3/2/39d97985eecf3aa2b863955e4227cbb0", keywords = "genetic algorithms, genetic programming, Marketing, Data mining, Customer profiling, Customer targeting, Statistical modelling, Performance evaluation", abstract = "In this paper, quantitative models for direct marketing models are reviewed from a systems perspective. A systems view consists of input, processing, and output and the six key activities of direct marketing that take place within these constituent parts. A discussion about inputs for direct marketing models is provided by describing the various types of data used, by determining the significance of the data, and by addressing the issue of selection of appropriate data. Two types of models, statistical and machine learning based, are popularly used for conducting direct marketing activities. The advantages and disadvantages of these two approaches are discussed along with enhancements to these models. The evaluation of output for direct marketing models is done on the basis of accuracy and profitability. Some challenges in conducting research in the area of quantitative direct marketing models are listed and some significant research questions are proposed.", notes = "Survey", } @TechReport{SAND2005-0014, author = "Mark Boslough and Michael Peters and Arthurine Pierson", title = "Graduated Embodiment for Sophisticated Agent Evolution and Optimization", institution = "Sandia National Laboratories", year = "2005", number = "SAND2005-0014", address = "P.O. Box 5800, Albuquerque, NM 87185-0318, USA", month = jan, keywords = "genetic algorithms, genetic programming, Algorithms Design, Evaluation, Genetics, Hypothesis, Navigation, Optimization, Programming Robots, Simulation, Training, Intelligent agents (Computer software), Robots-Control systems, Evolutionary programming (Computer science)", URL = "https://www.sandia.gov/research/publications/details/graduated-embodiment-for-sophisticated-agent-evolution-and-optimization-2005-01-01/", URL = "http://www.cs.sandia.gov/web1433/pubsagent/Graduated_Embodiment.pdf", DOI = "doi:10.2172/921610", size = "53 pages", abstract = "We summarise the results of a project to develop evolutionary computing methods for the design of behaviours of embodied agents in the form of autonomous vehicles. We conceived and implemented a strategy called graduated embodiment. This method allows high-level behavior algorithms to be developed using genetic programming methods in a low-fidelity, disembodied modelling environment for migration to high-fidelity, complex embodied applications. This project applies our methods to the problem domain of robot navigation using adaptive waypoints, which allow navigation behaviors to be ported among autonomous mobile robots with different degrees of embodiment, using incremental adaptation and staged optimisation. Our approach to biomimetic behaviour engineering is a hybrid of human design and artificial evolution, with the application of evolutionary computing in stages to preserve building blocks and limit search space. The methods and tools developed for this project are directly applicable to other agent-based modeling needs, including climate-related conflict analysis, multiplayer training methods,and market-based hypothesis evaluation.", notes = "Unlimited Release Mark Boslough Michael Peters Evolutionary Computing & Agent Based Modeling Department Arthurine Pierson Intelligent Systems Principles Department", } @InProceedings{Boslough:2017:ieeeAero, author = "Mark Boslough", booktitle = "2017 IEEE Aerospace Conference", title = "Autonomous dynamic soaring", year = "2017", month = "4-11 " # mar, address = "Big Sky, MT, USA", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/AERO.2017.7943967", size = "20 pages", abstract = "This project makes use of biomimetic behavioural engineering in which adaptive strategies used by animals in the real world are applied to the development of autonomous robots. The key elements of the biomimetic approach are to observe and understand a survival behaviour exhibited in nature, to create a mathematical model and simulation capability for that behaviour, to modify and optimise the behaviour for a desired robotics application, and to implement it. The application described in this report is dynamic soaring, a behaviour that certain sea birds use to extract flight energy from laminar wind velocity gradients in the shallow atmospheric boundary layer directly above the ocean surface. Theoretical calculations, computational proof-of-principle demonstrations, and the first instrumented experimental flight test data for dynamic soaring are presented to address the feasibility of developing dynamic soaring flight control algorithms to sustain the flight of unmanned airborne vehicles (UAVs). Both hardware and software were developed for this application. Eight-foot custom foam glider were built and flown in a steep shear gradient. A logging device was designed and constructed with custom software to record flight data during dynamic soaring manoeuvres. A computational tool kit was developed to simulate dynamic soaring in special cases and with a full 6-degree of freedom flight dynamics model in a generalised time-dependent wind field. Several 3-dimensional visualization tools were built to replay the flight simulations. A realistic aerodynamics model of an eight-foot sailplane was developed using measured aerodynamic derivatives. Genetic programming methods were developed and linked to the simulations and visualization tools. These tools can now be generalised for other biomimetic behaviour applications. This work was carried out in 2000 and 2001, and until now its results have only been available in an internal Sandia report.", notes = "Wandering albatross Also known as \cite{7943967}", } @InProceedings{bosman:1999:LIPIDEA, author = "Peter A. N. Bosman and Dirk Thierens", title = "Linkage Information Processing In Distribution Estimation Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "60--67", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-812.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-812.ps", size = "8 pages", abstract = "The last few years there has been an increasing amount of interest in the field of distribution estimation optimization algorithms. As more techniques are introduced, the variety in tested distribution structures increases. we analyze the implications of the form of such a structure. We show that learning the linkage relations alone and using them directly in a distribution estimation algorithm to generate new samples is not sufficient for building competent evolutionary algorithms. The information needs to be processed to identify and use the building blocks.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{UUCS2004047, author = "Peter A. N. Bosman and Edwin D. {de Jong}", year = "2004", title = "Grammar Transformations in an EDA for Genetic Programming", number = "UU-CS-2004-047", institution = "Department of Information and Computing Sciences, Utrecht University", pubcat = "techreport", address = "The Netherlands", keywords = "genetic algorithms, genetic programming, EDA, grammar", URL = "http://www.cs.uu.nl/research/techreps/repo/CS-2004/2004-047.pdf", URL = "http://www.cs.uu.nl/research/techreps/UU-CS-2004-047.html", abstract = "In this paper we present a new Estimation of Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimization as a result of the transformations that were applied.", notes = "Royal Tree. See also \cite{bosman:2004:obu:panbos}", size = "13 pages", } @InProceedings{bosman:2004:obu:panbos, author = "Peter A. N. Bosman and Edwin D. {de Jong}", title = "Grammar Transformations in an EDA for Genetic Programming", editor = "R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. {de Jong} and J. Koza and H. Suzuki and H. Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M. Jakiela", booktitle = "GECCO 2004 Workshop Proceedings", year = "2004", month = "26-30 " # jun, address = "Seattle, Washington, USA", keywords = "genetic algorithms, genetic programming", abstract = "we present a new Estimation-of-Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimisation as a result of the transformations that were applied.", URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/WOBU001.pdf", notes = "See also \cite{UUCS2004047} GECCO-2004WKS Distributed on CD-ROM at GECCO-2004", } @InProceedings{Bosman:PPSN:2004, author = "Peter A. N. Bosman and Edwin D. {de Jong}", title = "Learning Probabilistic Tree Grammars for Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VIII", year = "2004", editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel", volume = "3242", pages = "192--201", series = "LNCS", address = "Birmingham, UK", publisher_address = "Berlin", month = "18-22 " # sep, publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, EDA", ISBN = "3-540-23092-0", URL = "http://www.cs.uu.nl/~dejong/publications/edagpppsn.pdf", URL = "https://rdcu.be/dc0ju", DOI = "doi:10.1007/b100601", DOI = "doi:10.1007/978-3-540-30217-9_20", size = "10 pages", abstract = "Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation-of-Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problems structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context-free grammar to represent local structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach.", notes = "'The results indicate that our EDA for GP is feasible.'", } @PhdThesis{Van_den_Bossche:thesis, author = "Ruben {Van den Bossche}", title = "Cost-aware resource management in clusters and clouds", school = "Departement Wiskunde-Informatica, Universiteit Antwerpen", year = "2014", address = "Antwerp, Belgium", month = jun, keywords = "genetic algorithms, genetic programming", URL = "http://hdl.handle.net/10067/1174120151162165141", URL = "http://anet.uantwerpen.be/docman/irua/aae65f/8269.pdf", size = "163 pages", notes = "Supervisors prof. Dr. Jan Broeckhove and Dr. Kurt Vanmechelen COMP [c:irua:117412] Ruben Bossche", } @MastersThesis{bot:1999:masters, author = "Martijn Bot", title = "Application of Genetic Programming to the Induction of Linear Programming Trees", school = "Vrije Universiteit", year = "1999", address = "Amsterdam, The Netherlands", month = "1 " # jul, keywords = "genetic algorithms, genetic programming, data mining", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/verslag.ps.gz", URL = "http://citeseer.ist.psu.edu/243957.html", size = "48 pages", notes = "See also \cite{bot:1999:GPilct}, \cite{bot:2000:GPilct}", } @InProceedings{bot:1999:GPilct, author = "Martijn Bot and William B. Langdon", title = "Application of Genetic Programming to Induction of Linear Classification Trees", booktitle = "Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)", year = "1999", editor = "Eric Postma and Marc Gyssens", pages = "107--114", address = "Kasteel Vaeshartelt, Maastricht, Holland", month = "3-4 " # nov, organisation = "BNVKI, Dutch and the Belgian AI Association", keywords = "genetic algorithms, genetic programming, data mining", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz", size = "8 pages", abstract = "A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a benchmark of classification problems. In particular, using GP we are able to induce decision trees with a linear combination of variables in each function node. The effects of techniques as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.", notes = "http://www.bnvki.org/ https://documentserver.uhasselt.be/handle/1942/4193 July 2024 broken http://www.cs.unimaas.nl/~bnvki/", } @InProceedings{bot:2000:GPilct, author = "Martijn C. J. Bot and William B. Langdon", title = "Application of Genetic Programming to Induction of Linear Classification Trees", booktitle = "Genetic Programming, Proceedings of EuroGP'2000", year = "2000", editor = "Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty", volume = "1802", series = "LNCS", pages = "247--258", address = "Edinburgh", publisher_address = "Berlin", month = "15-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-67339-3", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.eurogp2000.19jan.ps.gz", URL = "http://citeseer.ist.psu.edu/318695.html", DOI = "doi:10.1007/978-3-540-46239-2_18", abstract = "A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong typing in GP is introduced. With this representation it is possible to let the GP classify into any number o f classes. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.", notes = "See also \cite{bot:1999:GPilct} EuroGP'2000, part of \cite{poli:2000:GP}", } @InProceedings{Bot:2000:GECCO, author = "Martijn C. J. Bot", title = "Improving Induction of Linear Classification Trees with Genetic Programming", pages = "403--410", year = "2000", publisher = "Morgan Kaufmann", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)", editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer", address = "Las Vegas, Nevada, USA", publisher_address = "San Francisco, CA 94104, USA", month = "10-12 " # jul, keywords = "genetic algorithms, genetic programming", ISBN = "1-55860-708-0", URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP185.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.gecco2000.19jan.ps.gz", URL = "http://citeseer.ist.psu.edu/316984.html", abstract = "Decision trees are a well known technique in machine learning for describing the underlying structure of a dataset. In [Bot and Langdon, 2000] a new representation of decision trees using strong typing in GP was introduced. In the function nodes, a linear combination of variables is made. The effects of techniques such as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated on a set of benchmark classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified. Results indicate that GP can be applied successfully to classification problems. Limited error fitness reduces runtime while maintaing equal accuracy. Pareto scoring works well against bloat. Fitness sharing Pareto works better than domination Pareto.", notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}", } @InProceedings{bot:2001:EuroGP, author = "Martijn C. J. Bot", title = "Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "256--267", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Feature Extraction, Machine Learning: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_20", size = "12 pages", abstract = "In pattern recognition the curse of dimensionality can be handled either by reducing the number of features, e.g. with decision trees or by extraction of new features. We propose a genetic programming (GP) framework for automatic extraction of features with the express aim of dimension reduction and the additional aim of improving accuracy of the k-nearest neighbour (k-NN) classifier. We will show that our system is capable of reducing most datasets to one or two features while k-NN accuracy improves or stays the same. Such a small number of features has the great advantage of allowing visual inspection of the dataset in a two-dimensional plot. Since k-NN is a non-linear classification algorithm, we compare several linear fitness measures. We will show the a very simple one, the accuracy of the minimal distance to means (mdm) classifier outperforms all other fitness measures. We introduce a stopping criterion gleaned from numeric mathematics. New features are only added if the relative increase in training accuracy is more than a constant d, for the mdm classifier estimated to be 3.3%.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @InProceedings{bot:2001:fencgp, author = "Martijn C. J. Bot", title = "Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming", booktitle = "Graduate Student Workshop", year = "2001", editor = "Conor Ryan", pages = "397--400", address = "San Francisco, California, USA", month = "7 " # jul, keywords = "genetic algorithms, genetic programming", notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS", } @InCollection{Botros:2004:EMTP, author = "Michael Botros", title = "Evolving Controllers for Miniature Robots", year = "2004", booktitle = "Evolvable Machines: Theory \& Practice", pages = "73--100", volume = "161", series = "Studies in Fuzziness and Soft Computing", chapter = "4", editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Berlin", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-22905-1", URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html", notes = "Springer says published in 2005 but available Nov 2004", } @InCollection{botros:2006:GSP, author = "Michael Botros", title = "Evolving Complex Robotic Behaviors Using Genetic Programming", year = "2006", booktitle = "Genetic Systems Programming: Theory and Experiences", pages = "175--194", volume = "13", series = "Studies in Computational Intelligence", editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo Mourelle}", publisher = "Springer", address = "Germany", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-29849-5", DOI = "doi:10.1007/3-540-32498-4_8", abstract = "In this chapter, two possible approaches for evolving complex behaviours were discussed. In the first approach, the GP is used to explore possible hierarchy in the solution through implementing ADF and maintaining a subroutine library or using neural networks as primitive functions. In the second approach, human programmer set the architecture of the robot controller and then the GP is used to evolve each module of this architecture. Two examples of architectures were discussed, the subsumption architecture and action selection architecture. Two experiments were presented to demonstrate this approach. The first used subsumption architecture to control a team of two robots with different capabilities to implement a cooperative behavior. The second experiment used action selection architecture to allow switching between the simpler behaviours that constitute the main behavior", notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html", } @InProceedings{Botwey:2014:EMBC, author = "Ransford Henry Botwey and Elena Daskalaki and Peter Diem and Stavroula G. Mougiakakou", booktitle = "36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014)", title = "Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events", year = "2014", month = aug, pages = "4843--4846", address = "Chicago, IL, USA", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-7929-0", ISSN = "1557-170X", DOI = "doi:10.1109/EMBC.2014.6944708", size = "4 pages", abstract = "Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction, cARX, and a recurrent neural network, RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25percent, and 100percent correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.", notes = "Diabetes Technology Research Group, University of Bern, Switzerland Also known as \cite{6944708}", } @InProceedings{Botzheim:2004:ishrCI, title = "Model Identification by Bacterial Optimization", author = "J. Botzheim and L. T. Koczy", booktitle = "Proceedings of the 5th International Symposium of Hungarian Researchers on Computational Intelligence", year = "2004", pages = "91--102", address = "Budapest, Hungary", month = nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.bmf.hu/conferences/mtn/botzheim.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.7233", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.135.7233", abstract = "In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as artificial neural networks and fuzzy systems is in progress. In this paper a recent kind of evolutionary method called bacterial algorithm is introduced. This method can be used for fuzzy rule extraction and optimization. Bacterial Programming is also proposed in this paper. This approach is the combination of the bacterial algorithm and the genetic programming techniques and can be applied for the optimization of the structure of Bspline neural networks.", } @Article{BotzheimCabritaKoczyRuano07, author = "Janos Botzheim and Cristiano Cabrita and Laszlo T. Koczy and Antonio E. Ruano", title = "Genetic and Bacterial Programming for {B}-Spline Neural Networks Design", journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics", year = "2007", volume = "11", number = "2", pages = "220--231", month = feb, keywords = "genetic algorithms, genetic programming, constructive algorithms, B-splines, bacterial programming", ISSN = "1343-0130", URL = "http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001100020012.xml", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/BotzheimCabritaKoczyRuano07.pdf", DOI = "doi:10.20965/jaciii.2007.p0220", size = "12 pages", abstract = "The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.", } @PhdThesis{Botzheim:thesis, author = "Janos Botzheim", title = "Intelligens szamitastechnikai modellek identifiacioja evolucios es gradiens alapu tanulo algoritmusokkal", school = "Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics", type = "{Ph.D.} thesis", year = "2007", address = "Hungary", month = "11 " # nov, keywords = "genetic algorithms, genetic programming", URL = "http://www.sze.hu/~botzheim/hid/disszertacio.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/thesisbooklet.pdf", size = "124 pages", abstract = "The thesis discusses identification techniques of soft computing models. Its goal is to develop identification methods based on numerical data that can produce results better in terms of quality criteria (e.g. mean square error) relevant for the given applications than other techniques known from the literature. The first statement proposes the Bacterial Evolutionary Algorithm for the extraction of Mamdani-type fuzzy rules with trapezoidal membership functions. The second statement proposes the application of the Levenberg-Marquardt algorithm for local optimisation of fuzzy rules. The third statement introduces the Bacterial Memetic Algorithm, a combination of the Bacterial Evolutionary and the Levenberg-Marquardt algorithm. The fourth statement deals with Takagi-Sugeno-type fuzzy systems. The fifth statement proposes a new technique called Bacterial Programming for the design process of B-spline neural networks. Finally, the sixth statement presents the application of Bacterial Evolutionary Algorithm for the feature selection problem.", notes = "In Hungarian. 24 page english summary", } @Article{Bouaziz:2013:Neurocomputing, author = "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and Ajith Abraham", title = "A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model", journal = "Neurocomputing", volume = "117", pages = "107--117", year = "2013", keywords = "genetic algorithms, genetic programming, Flexible Beta Basis Function Neural Tree Model, Opposite-based particle swarm optimization algorithm, Time-series forecasting, Control system", ISSN = "0925-2312", DOI = "doi:10.1016/j.neucom.2013.01.024", URL = "http://www.sciencedirect.com/science/article/pii/S0925231213001975", abstract = "In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimised based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimisation algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods.", } @InProceedings{Bouaziz:2014:CEC, title = "{PSO}-Based Update Memory for Improved Harmony Search Algorithm to the Evolution of {FBBFNT'} Parameters", author = "Souhir Bouaziz and Adel M. Alimi and Ajith Abraham", pages = "1951--1958", booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation", year = "2014", month = "6-11 " # jul, editor = "Carlos A. {Coello Coello}", address = "Beijing, China", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, Extended Genetic Programming, Memetic, multi-meme and hybrid algorithms", DOI = "doi:10.1109/CEC.2014.6900304", abstract = "In this paper, a PSO-based update memory for Improved Harmony Search (PSOUM-IHS) algorithm is proposed to learn the parameters of Flexible Beta Basis Function Neural Tree (FBBFNT) model. These parameters are the Beta parameters of each flexible node and the connected weights of the network. Furthermore, the FBBFNT's structure is generated and optimised by the Extended Genetic Programming (EGP) algorithm. The combination of the PSOUM-IHS and EGP in the same algorithm is so used to evolve the FBBFNT model. The performance of the proposed evolving neural network is evaluated for nonlinear systems of prediction and identification and then compared with those of related models.", notes = "harmony search is just evolutionary strategies http://www.dennisweyland.net/blog/?p=12 http://dl.acm.org/citation.cfm?id=2433395 WCCI2014", } @Article{Bouaziz:2016:ASC, author = "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and Ajith Abraham", title = "Evolving flexible beta basis function neural tree using extended genetic programmin \& Hybrid Artificial Bee Colony", journal = "Applied Soft Computing", year = "2016", volume = "47", pages = "653--668", ISSN = "1568-4946", DOI = "doi:10.1016/j.asoc.2016.03.006", URL = "http://www.sciencedirect.com/science/article/pii/S1568494616301156", keywords = "genetic algorithms, genetic programming, Flexible beta basis function neural tree model, Hybrid Artificial Bee Colony algorithm, Time-series forecasting", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc47.html#BouazizDAA16", abstract = "In this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. This hybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with the guided Opposite-based Particle Swarm Optimization (OPSO) position. Such modification can minimize the delay which might be lead by the random position, in reaching the global solution. The performance of the proposed model is evaluated for benchmark problems drawn from time series prediction area and is compared with those of related methods.", notes = "also known as \cite{journals/asc/BouazizDAA16}", } @InProceedings{Boudardara:2018:ISMSIT, author = "Fateh Boudardara and Beyza Gorkemli", booktitle = "2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)", title = "Application of Artificial Bee Colony Programming to Two Trails of the Artificial Ant Problem", year = "2018", abstract = "Automatic navigation of mobile robots has an increasing importance in many application fields such as robotics, mining industry, underwater exploration, aerospace research, virtual environments and games. In this study, we use artificial bee colony programming (ABCP), which is a novel evolutionary computation based automatic programming method, to solve the artificial ant problem that is considered as one of the basic test problems in robotic path planning. In order to see the performance of this method, a series of experiments are carried out on Santa Fe and Los Altos Hills trails. The results are compared with those of genetic programming.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ISMSIT.2018.8567048", month = oct, notes = "Also known as \cite{8567048}", } @Article{Boukhelifa:2016:EC, author = "Nadia Boukhelifa and Anastasia Bezerianos and Waldo Cancino and Evelyne Lutton", title = "Evolutionary Visual Exploration: Evaluation of an {IEC} Framework for Guided Visual Search", journal = "Evolutionary Computation", year = "2017", volume = "25", number = "1", pages = "55--86", month = mar, keywords = "genetic algorithms, genetic programming, interactive evolutionary computation, visual analytics, information visualization, data mining, interactive evolutionary algorithms", publisher = "HAL CCSD; Massachusetts Institute of Technology Press (MIT Press)", ISSN = "1063-6560", annote = "Universit{\'e} Paris-Sud - Paris 11 (UP11); Interacting with Large Data (ILDA) ; Laboratoire de Recherche en Informatique (LRI) ; Universit{\'e} Paris-Sud - Paris 11 (UP11) - Centre National de la Recherche Scientifique (CNRS) - Universit{\'e} Paris-Sud - Paris 11 (UP11) - Centre National de la Recherche Scientifique (CNRS) - INRIA Saclay - Ile de France ; INRIA - INRIA; G{\'e}nie et Microbiologie des Proc{\'e}d{\'e}s Alimentaires (GMPA) ; AgroParisTech (AgroParisTech) - Institut national de la recherche agronomique (INRA)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Analysis and Visualization (AVIZ) and INRIA Saclay - Ile de France and INRIA - INRIA and Interacting with Large Data and G{\'e}nie et Microbiologie des Proc{\'e}d{\'e}s Alimentaires", identifier = "hal-01218959", language = "en", oai = "oai:HAL:hal-01218959v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1162/EVCO_a_00161", URL = "https://hal.inria.fr/hal-01218959", URL = "https://hal.inria.fr/hal-01218959/document", URL = "https://hal.inria.fr/hal-01218959/file/boukhelifa_eve_preprint.pdf", DOI = "DOI:10.1162/EVCO_a_00161", size = "32 pages", abstract = "We evaluate and analyse a framework for Evolutionary Visual Exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional datasets towards two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this paper, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clear predefined task, that examines how users leverage the system and how the system evolves to match their needs. While previously we showed that using EVE, domain experts were able to formulate interesting hypothesis and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating Interactive Evolutionary Computation (IEC) techniques based on user study methodologies.", notes = "Also known as \cite{boukhelifa-EVCO2016}", } @Article{Boukhelifa:2018:GPEM, author = "Nadia Boukhelifa and Evelyne Lutton", title = "Guest editorial: Special issue on genetic programming, evolutionary computation and visualization", journal = "Genetic Programming and Evolvable Machines", year = "2018", volume = "19", number = "3", pages = "313--315", month = sep, keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://doi.org/10.1007/s10710-018-9333-4", DOI = "doi:10.1007/s10710-018-9333-4", size = "3 pages", } @InProceedings{boulas_ground_2016, author = "Konstantinos Boulas and Valilios P. Androvitsaneas and Ioannis F. Gonos and Georgios Dounias and Ioannis A. Stathopulos", title = "Ground {Resistance} {Estimation} using {Genetic} {Programming}", booktitle = "5th International Symposium and 27th National Conference on Operation Research", editor = "Athanasios Spyridakos and Lazaros Vryzidis", year = "2016", month = jun, address = "Aigaleo, Athens", pages = "66--71", keywords = "genetic algorithms, genetic programming, gene expression programming, symbolic regression, ground resistance", isbn13 = "978-618-80361-6-1", URL = "http://eeee2016.teipir.gr/ConferenceBookHELORS2016.pdf", abstract = "The objective of this paper is to use genetic programming methodologies for the modelling and estimation of ground resistance with the use of field measurements related to weather data. Grounding is important for the safe operation of any electrical installation and protects it against lightning and fault currents. The work uses both, conventional and intelligent data analysis techniques, for ground resistance modeling from field measurements. Experimental data consist of field measurements that have been performed in Greece during the previous four years. Five linear regression models have been applied to a properly selected dataset, as well as an intelligent approach based on Gene Expression Programming (GEP). Every model corresponds to a specific grounding system. A heuristic approach using GEP was performed in order to produce more robust and general models for grounding estimation. The results show that evolutionary techniques such as those based on Genetic Programming (GP) are promising for the estimation of the ground resistance.", } @InCollection{boulas_approximating_2017, author = "Konstantinos Boulas and Georgios Dounias and Chrissoleon Papadopoulos", title = "Approximating {Throughput} of {Small} {Production} {Lines} {Using} {Genetic} {Programming}", booktitle = "Operational {Research} in {Business} and {Economics}: 4th {International} {Symposium} and 26th {National} {Conference} on {Operational} {Research}, 2015", editor = "Evangelos Grigoroudis and Michael Doumpos", year = "2017", pages = "185--204", address = "Chania, Greece", month = "4-6 " # jun, publisher = "Springer", keywords = "genetic algorithms, genetic programming, production lines, symbolic regression, throughput", isbn13 = "978-3-319-33003-7", URL = "http://link.springer.com/10.1007/978-3-319-33003-7_9", DOI = "doi:10.1007/978-3-319-33003-7_9", abstract = "Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics such as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation and to deal with problems like code bloat or over fitting. We improve previous work on estimation of throughput for three stages and present a formula for the estimation of throughput of production lines with four stations. Further work is needed, but so far, results are encouraging.", } @InProceedings{boulas_acquisition_2015, author = "Konstantinos Boulas and Georgios Dounias and Chrissoleon Papadopoulos and Athanasios Tsakonas", title = "Acquisition of {Accurate} or {Approximate} {Throughput} {Formulas} for {Serial} {Production} {Lines} through {Genetic} {Programming}", booktitle = "Proceedings of the 4th {International} {Symposium} \& 26th {National} {Conference} on {Operational} {Research}, HELORS-2015", year = "2015", volume = "1", pages = "128--133", address = "Chania, Greece", month = jun, publisher = "Hellenic Operational Research Society", keywords = "genetic algorithms, genetic programming, serial production lines, symbolic regression", isbn13 = "978-618-80361-4-7", URL = "http://mde-lab.aegean.gr/images/stories/docs/CC97.pdf", abstract = "Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation, and to deal with problems like code bloat or over fitting. Further work is needed, but so far, results are encouraging.", notes = "http://mde-lab.aegean.gr/research-material", } @InProceedings{boulas_acquisition_2018, address = "Rio Patras, Greece", title = "Acquisition of approximate throughput formulas for serial production lines with parallel machines using intelligent techniques", isbn13 = "978-1-4503-6433-1", URL = "http://dl.acm.org/citation.cfm?doid=3200947.3201028", DOI = "doi:10.1145/3200947.3201028", abstract = "Estimating the performance of a production line is a difficult problem because of the enormous number of states that exist when analysing such systems. In addition to the methods developed to address the problem, it is very useful to have a formula linking the characteristics of the line to its performance. Three cases of sort serial production lines with parallel and identical machines in each workstation are examined in this paper. By using a combinational method that applies genetic programming (GP) and an innovative nature inspired method, named sonar inspired optimization (SIO) to improve the results, three models are derived to obtain the throughput of the corresponding lines. Further work will take place because results derived in this paper are encouraging.", language = "en", booktitle = "Proceedings of the 10th {Hellenic} {Conference} on {Artificial} {Intelligence}", publisher = "ACM Press", author = "Konstantinos Boulas and Alexandros Tzanetos and Georgios Dounias", month = jul, year = "2018", note = "Article No 18", keywords = "genetic algorithms, genetic programming, parallel-machine stations, performance evaluation, serial production lines, Sonar Inspired Optimization", pages = "18:1--18:7", } @Article{Boumanchar:2018:WMR, author = "Imane Boumanchar and Younes Chhiti and Fatima Ezzahrae M'hamdi Alaoui and Abdelaziz Sahibed-Dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel", title = "Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques", journal = "Waste Management \& Research", year = "2018", volume = "37", number = "6", pages = "578--589", month = dec # "~19", keywords = "genetic algorithms, genetic programming, energy, higher heating value, multiple regression, municipal solid waste, prediction, chemical sciences/material chemistry, chemical sciences/polymers", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Unite Materiaux et Transformations - UMR 8207", description = "International audience", identifier = "hal-02922402; DOI: 10.1177/0734242x18816797; WOS: 000474405200003", language = "en", oai = "oai:HAL:hal-02922402v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1177/0734242x18816797", URL = "https://hal.univ-lille.fr/hal-02922402", DOI = "doi:10.1177/0734242x18816797", abstract = "Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an oxygen bomb calorimeter. This process is, however, expensive. In this light, the purpose of this paper was to develop empirical models for the prediction of MSW higher heating value (HHV) from ultimate analysis. Two methods were used: multiple regression analysis and genetic programming formalism. Both techniques gave good results. Genetic programming, however, provides more accuracy compared to published works in terms of a great correlation coefficient (CC) and a low root mean square error (RMSE).", annote = "Universite Chouaib Doukkali (UCD); Unite Materiaux et Transformations - UMR 8207 (UMET) ; Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Superieure de Chimie de Lille (ENSCL)-Universite de Lille", } @Article{Boumanchar:2018:IJGE, author = "Imane Boumanchar and Younes Chhiti and Fatima Ezzahrae M'Hamdi Alaoui and Abdelaziz Sahibed-Dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel", title = "Multiple regression and genetic programming for coal higher heating value estimation", journal = "International Journal of Green Energy", year = "2018", volume = "15", number = "14-15", pages = "958--964", keywords = "genetic algorithms, genetic programming, coal, higher heating value, multiple regression, prediction, life sciences", URL = "https://hal.inrae.fr/hal-02620955", DOI = "doi:10.1080/15435075.2018.1529591", publisher = "Taylor \& Francis", ISSN = "1543-5075", eissn = "1543-5083", abstract = "The higher heating value (HHV) is an important characteristic for the determination of fuels quality. Nevertheless, its experimental measurement requires intricate technologies. In this work, the HHV of coal was predicted from ultimate composition using two methods: multiple regression and genetic programming. A dataset of 100 samples from literature was exploited (75percent for training and 25percent for testing). A comparative study was elaborated between the developed models and published ones in terms of correlation coefficient, root mean square error, and mean absolute percent error. The adopted models gave a good statistical performance. Abbreviations: C: Carbon; CC: Correlation coefficient; H: Hydrogen; HHV: Higher heating valueI; GT: Institute of gas technology; GP: Genetic programming; LHV: Lower heating value; MAPE: Mean absolute percent error; N: Nitrogen; O: Oxygen; RMSE: Root mean square error; S: sulfur; Wt: Weight percentage", ISSN = "1543-5075", annote = "Universite Chouaib Doukkali (UCD); Unite Materiaux et Transformations - UMR 8207 (UMET) ; Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Superieure de Chimie de Lille (ENSCL)-Universite de Lille-Centre National de la Recherche Scientifique (CNRS)", bibsource = "OAI-PMH server at api.archives-ouvertes.fr", contributor = "Unite Materiaux et Transformations - UMR 8207", identifier = "hal-02620955; DOI: 10.1080/15435075.2018.1529591; PRODINRA: 465078; WOS: 000450845200007", language = "en", oai = "oai:HAL:hal-02620955v1", relation = "info:eu-repo/semantics/altIdentifier/doi/10.1080/15435075.2018.1529591", } @Article{boumanchar:BCaB, author = "Imane Boumanchar and Kenza Charafeddine and Younes Chhiti and Fatima Ezzahrae M'hamdi Alaoui and Abdelaziz Sahibed-dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel", title = "Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming", journal = "Biomass Conversion and Biorefinery", year = "2019", volume = "9", number = "3", pages = "499--509", month = sep, keywords = "genetic algorithms, genetic programming, Higher heating value, HHV prediction, Multiple variable regression", ISSN = "2190-6815", URL = "http://link.springer.com/article/10.1007/s13399-019-00386-5", DOI = "doi:10.1007/s13399-019-00386-5", size = "11 pages", abstract = "The higher heating value (HHV) is a significant parameter for the determination of fuel quality. However, its measurement is time-consuming and requires sophisticated equipment. For this reason, several researches have been interested to develop mathematical models for the prediction of HHV from fundamental composition. The purpose of this study is to develop new correlations to determine the biomass HHV from ultimate analysis. As a result, two models were elaborated. The first was developed using multiple variable regression analysis while the second has adopted genetic programming formalism. Data of 171 from various types of biomass samples were randomly used for the development (75percent) and the validation (25percent) of new equations. The accuracy of the established models was compared to previous literature works in terms of correlation coefficient (CC), average absolute error (AAE), and average bias error (ABE). The proposed models were more performing with the highest CC and the smallest errors.", } @InProceedings{Boumaza:2001:EvoWorks, author = "Amine M. Boumaza and Jean Louchet", title = "Dynamic Flies: Using Real-Time Parisian Evolution in Robotics", booktitle = "Applications of Evolutionary Computing", year = "2001", editor = "Egbert J. W. Boers and Stefano Cagnoni and Jens Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther R. Raidl and Robert E. Smith and Harald Tijink", volume = "2037", series = "LNCS", pages = "288--297", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", note = "best paper award", keywords = "genetic algorithms, genetic programming, fly algorithm, robot", ISBN = "3-540-41920-9", DOI = "doi:10.1007/3-540-45365-2_30", abstract = "The Fly algorithm is a Parisian evolution strategy devised for parameter space exploration in computer vision applications, which has been applied to stereovision. The resulting scene model is a set of 3-D points which concentrate upon the surfaces of obstacles. In this paper, we present how the evolutionary scene analysis can be continuously updated and integrated into a specific real-time mobile robot navigation system. Simulation-based experimental results are presented.", notes = "EvoWorkshops2001", } @InProceedings{Boumaza:evowks03, author = "Amine M. Boumaza and Jean Louchet", title = "Mobile Robot Sensor Fusion Using Flies", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}", year = "2003", editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf", volume = "2611", series = "LNCS", pages = "357--367", address = "University of Essex, England, UK", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, evolutionary computation, applications", isbn13 = "978-3-540-00976-4", DOI = "doi:10.1007/3-540-36605-9_33", abstract = "The Fly algorithm is a fast artificial evolution-based image processing technique. Previous work has shown how to process stereo image sequences and use the evolving population of 'flies' as a continuously updated representation of the scene for obstacle avoidance in a mobile robot. In this paper, we show that it is possible to use several sensors providing independent information sources on the surrounding scene and the robot's position, and fuse them through the introduction of corresponding additional terms into the fitness function. This sensor fusion technique keeps the main properties of the fly algorithm: asynchronous processing. no low-level image pre-processing or costly image segmentation, fast reaction to new events in the scene. Simulation test results are presented.", notes = "EvoWorkshops2003", } @Article{Boumaza:2012:GPEM, author = "Amine Boumaza", title = "Cameron Browne: Evolutionary game design, Springer briefs in computer science series", journal = "Genetic Programming and Evolvable Machines", year = "2012", volume = "13", number = "3", pages = "407--409", month = sep, note = "Book review", publisher = "Springer", keywords = "genetic algorithms, genetic programming", ISSN = "1389-2576", URL = "https://rdcu.be/dR8gF", DOI = "doi:10.1007/s10710-012-9165-6", size = "3 pages", notes = "Review of \cite{CameronBrowne:book} Quote appears in SIGEVOlution 6(2) page 16. http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf", affiliation = "University of Lille Nord de France, 59000 Lille, France", } @InProceedings{bouras:2025:GI, author = "Dimitrios Stamatios Bouras and Justyna Petke and Sergey Mechtaev", title = "LLM-Assisted Crossover in Genetic Improvement of Software", booktitle = "14th International Workshop on Genetic Improvement @ICSE 2025", year = "2025", editor = "Aymeric Blot and Vesna Nowack and Penn {Faulkner Rainford} and Oliver Krauss", address = "Ottawa", month = "27 " # apr, note = "forthcoming", keywords = "genetic algorithms, genetic programming, Genetic Improvement, MAGPIE, Large Language Models, LLM, ANN", URL = "https://gpbib.cs.ucl.ac.uk/gi2025/bouras_2025_GI.pdf", URL = "https://geneticimprovementofsoftware.com/events/icse2025#accepted-papers", size = "8 pages", abstract = "We evaluated against five traditional crossover methods across seven benchmarks, measuring performance on four key metrics: average ranking, best variant execution time, efficiency in reaching performance milestones, and viable variant count. Results show that LLM-assisted crossover achieved an average ranking of 2.27 (on a scale where 1 is best and 6 is worst), making it the top-performing method across benchmarks based on the quality of the optimal variants produced. The LLM-based approach also improved the fitness (execution time) by an average of 8.5% over the best variant produced by the traditional methods. In terms of efficiency, the LLM-assisted crossover required on average 25.6% fewer variants to reach 25%, 50%, 75%, and 100% of the final performance improvement, compared to the traditional methods. Additionally, the LLM-assisted crossover produced 4.8% more viable variants across scenarios, including both source code modification and parameter tuning cases. These findings suggest that LLMs can significantly enhance genetic programming by guiding the crossover process toward more effective and viable solutions, providing motivation for further research in LLM-assisted evolutionary algorithms.", notes = "GI @ ICSE 2025, part of \cite{blot:2025:GI}", } @InProceedings{Bourmistrova:2007:cec, author = "A. Bourmistrova and S. Khantsis", title = "Control System Design Optimisation via Genetic Programming", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1993--2000", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1691.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424718", abstract = "This paper describes a stochastic approach for comprehensive diagnostics and validation of control system architecture for Unmanned Aerial Vehicle (UAV). Mathematically based diagnostics of a 6 DoF system provides capability for a complex evaluation of system components behaviour, but are typically both memory and computationally expensive. Design and optimisation of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behaviour. Evolutionary Algorithms (EAs) are known for their robustness for a wide range of optimising functions, when no a priori knowledge of the search space is available. Thus it makes evolutionary approach a promising technique to design the task controllers for complex dynamic systems such as an aircraft. In this study, EAs are used to design a controller for recovery (landing) of a small fixed-wing UAV on a frigate ship deck. The control laws are encoded in a way common for Evolutionary Programming. However, parameters (numeric coefficients in the control equations) are optimised independently using effective Evaluation Strategies, while structural changes occur at a slower rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The need of a well defined approach to the control system validation is dictated by the nature of UAV application, where the major source of mission success is based on autonomous control system architecture reliability. The results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is evaluated and a set of reliable algorithm parameters is validated.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @InCollection{Bourmistrova:2009:AV, author = "Anna Bourmistrova and Sergey Khantsis", title = "Flight Control System Design Optimisation via Genetic Programming", booktitle = "Aerial Vehicles", publisher = "InTech", year = "2009", editor = "Thanh Mung Lam", chapter = "7", keywords = "genetic algorithms, genetic programming, mobile robotics", isbn13 = "978-953-7619-41-1", URL = "http://www.intechopen.com/download/pdf/pdfs_id/5969", bibsource = "OAI-PMH server at www.intechopen.com", language = "eng", oai = "oai:intechopen.com:5969", URL = "http://www.intechopen.com/articles/show/title/flight_control_system_design_optimisation_via_genetic_programming", DOI = "doi:10.5772/6470", abstract = "In this chapter, an application of the Evolutionary Design (ED) is demonstrated. The aim of the design was to develop a controller which provides recovery of a fixed-wing UAV onto a ship under the full range of disturbances and uncertainties that are present in the real world environment. The controller synthesis is a multistage process. However, the approach employed for synthesis of each block is very similar. Evolutionary algorithm is used as a tool to evolve and optimise the control laws. One of the greatest advantages of this methodology is that minimum or no a priori knowledge about the control methods is used, with the synthesis starting from the most basic proportional control or even from `null' control laws. During the evolution, more complex and capable laws emerge automatically. As the resulting control laws demonstrate, evolution does not tend to produce parsimonious solutions. The method demonstrating remarkable robustness in terms of convergence indicating that a near optimal solution can be found. In very limited cases, however, it may take too long time for the evolution to discover the core of a potentially optimal solution, and the process does not converge. More often than not, this hints at a poor choice of the algorithm parameters. The most important and difficult problem in Evolutionary Design is preparation of the fitness evaluation procedure with predefined special intermediate problems. Computational considerations are also of the utmost importance. Robustness of EAs comes at the price of computational cost, with many thousands of fitness evaluations required. The simulation testing covers the entire operational envelope and highlights several conditions under which recovery is risky. All environmental factors--sea wave, wind speed and turbulence--have been found to have a significant effect upon the probability of success. Combinations of several factors may result in very unfavourable conditions, even if each factor alone may not lead to a failure. For example, winds up to 12 m/s do not affect the recovery in a calm sea, and a severe ship motion corresponding to Sea State 5 also does not represent a serious threat in low winds. At the same time, strong winds in a high Sea State may be hazardous for the aircraft.", size = "34 pages", } @InCollection{Bourmistrova:2010:naEC, title = "Genetic Programming in Application to Flight Control System Design Optimisation", author = "Anna Bourmistrova and Sergey Khantsis", booktitle = "New Achievements in Evolutionary Computation", publisher = "InTech", year = "2010", editor = "Peter Korosec", chapter = "10", month = feb, keywords = "genetic algorithms, genetic programming, UAV", isbn13 = "978-953-307-053-7", language = "eng", oai = "oai:intechopen.com:8542", URL = "http://www.intechopen.com/articles/show/title/genetic-programming-in-application-to-flight-control-system-design-optimisation", URL = "http://www.intechopen.com/download/pdf/pdfs_id/8542", DOI = "DOI:10.5772/8055", notes = "the first seminal book to introduce GP as a solid and practical technique is John Koza's Genetic Programming, dated 1992. RMIT", size = "34 pages", } @InProceedings{Bousquet:2007:EA, author = "Aurelie Bousquet and Jean Louchet and Jean-Marie Rocchisani", title = "Fully Three-Dimensional Tomographic Evolutionary Reconstruction in Nuclear Medicine", booktitle = "Artificial Evolution, 2007", year = "2007", editor = "Nicolas Monmarche and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton", volume = "4926", series = "Lecture Notes in Computer Science", pages = "231--242", address = "Tours, France", month = oct # " 29-31", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-79304-5", DOI = "doi:10.1007/978-3-540-79305-2_20", abstract = "3-D reconstruction in Nuclear Medicine imaging using complete Monte-Carlo simulation of trajectories usually requires high computing power. We are currently developing a Parisian Evolution Strategy in order to reduce the computing cost of reconstruction without degrading the quality of results. Our approach derives from the Fly algorithm which proved successful on real-time stereo image sequence processing. Flies are considered here as photon emitters. We developed the marginal fitness technique to calculate the fitness function, an approach usable in Parisian Evolution whenever each individual's fitness cannot be calculated independently of the rest of the population.", notes = "Published 2008?", } @InProceedings{Boutaib:2021:EuroGP, author = "Sofien Boutaib and Maha Elarbi and Slim Bechikh and Chih-Cheng Hung and Lamjed Ben Said", title = "Software Anti-patterns Detection Under Uncertainty Using A Possibilistic Evolutionary Approach", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "181--197", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, SBSE, code smells, code smells detection, Uncertain software class labels, PK-NN evolution, Possibility theory: Poster", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_12", abstract = "Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issue of class label uncertainty is usually omitted. Indeed, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Thus, this uncertainty should be taken into account and then processed by detection tools. Unfortunately, these latter usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. This practice could considerably degrade the detection/identification process effectiveness. Motivated by this observation and the interesting performance of the Possibilistic K-NN (PK-NN) classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory. ADIPOK is validated using a possibilistic base of smell examples that simulates the subjectivity of software engineers' opinions' uncertainty. The statistical analysis of the obtained results on a set of comparative experiments with respect to four state-of-the-art methods show the merits of our proposed method.", notes = "software maintenance, including bugfix, but original developers are not available. Detecting code smells. Class imbalance, noisy labels. ADIPOK. Detect each smell separately. IAC. DECOR, BLOP, PK-NN. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{conf/biostec/BoutorhG14, author = "Aicha Boutorh and Ahmed Guessoum", title = "Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction", bibdate = "2014-09-23", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/biostec/bioinformatics2014.html#BoutorhG14", booktitle = "{BIOINFORMATICS} 2014 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, {ESEO}, Angers, Loire Valley, France, 3-6 March, 2014", publisher = "SciTePress", year = "2014", editor = "Oscar Pastor and Christine Sinoquet and Guy Plantier and Tanja Schultz and Ana L. N. Fred and Hugo Gamboa", isbn13 = "978-989-758-012-3", pages = "253--258", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://dx.doi.org/10.5220/0004913702530258", } @Article{Bovermann:2014:CMJ, author = "T Bovermann and D Griffiths", journal = "Computer Music Journal", title = "Computation as Material in Live Coding", year = "2014", month = mar, volume = "38", number = "1", pages = "40--53", keywords = "genetic algorithms, genetic programming", abstract = "What does computation sound like, and how can computational processing be integrated into live-coding practice along with code? This article gives insights into three years of artistic research and performance practice with Betablocker, an imaginary central processing unit architecture, specifically designed and implemented for live-coding purposes. It covers the themes of algorithmic composition, sound generation, genetic programming, and autonomous coding in the light of self-manipulating code and artistic research practice.", DOI = "doi:10.1162/COMJ_a_00228", ISSN = "0148-9267", notes = "Also known as \cite{6818648}", } @PhdThesis{Boyd:thesis, author = "Thermodynamics of Correlations and Structure in Information Engines", title = "Alexander Blades Boyd", school = "Physics, University of California, Davis", year = "2018", address = "USA", URL = "https://www.proquest.com/pagepdf/2047668591", size = "223 pages", abstract = "Understanding structured information and computation in thermodynamics systems is crucial to progress in diverse fields, from biology at a molecular level to designed nano-scale information processors. Landauer's principle puts a bound on the energy cost of erasing a bit of information. This suggests that devices which exchange energy and information with the environment, which we call information engines, can use information as a thermodynamic fuel to extract work from a heat reservoir, or dissipate work to erase information. However, Landauer's Principle on its own neglects the detailed dynamics of physical information processing, the mechanics and structure between the start and end of a computation. Our work deepens our understanding of these nonequilibrium dynamics, leading to new principles of efficient thermodynamic control. We explore a particular type of information engine called an information ratchet, which processes a symbol string sequentially, transducing its input string to an output string. We derive a general energetic framework for these ratchets as they operate out of equilibrium, allowing us to exactly calculate work and heat production. We show that this very general form of computation must obey a Landauer-like bound, the Information Processing Second Law (IPSL), which shows that any form of temporal correlations are a potential thermodynamic fuel. We show that in order to leverage that fuel, the autonomous information ratchet must have internal states which match the predictive states of the information reservoir. This leads to a thermodynamic principle of requisite complexity, much like Ashby's law of requisite variety in cybernetics. This is a result of the modularity of information transducers. We derive the modularity dissipation, which is an energetic cost beyond Landauer's bound that predicts the structural energy costs of different implementations of the same computation. Applying the modularity dissipation to information ratchets establishes design principles for thermodynamically efficient autonomous information processors. They prescribe the ratchet's structure such that the computation saturates the bound set by the IPSL and, thus, achieves maximum thermodynamic efficiency.", notes = "not GP? ProQuest Number: 10689139 Supervisor: James P. Crutchfield", } @InCollection{bozarth:2000:PCVGP, author = "Bradley J. Bozarth", title = "Programmatic Compression of Video using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2000", year = "2000", editor = "John R. Koza", pages = "46--53", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:2000:gagp}", } @Article{bozek:2022:Sensors, author = "Andrzej Bozek", title = "Discovering {Stick-Slip-Resistant} Servo Control Algorithm Using Genetic Programming", journal = "Sensors", year = "2022", volume = "22", number = "1", keywords = "genetic algorithms, genetic programming", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/22/1/383", DOI = "doi:10.3390/s22010383", abstract = "The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analysed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important.", notes = "also known as \cite{s22010383}", } @InProceedings{Bozogullarindan:2020:ASYU, author = "Elif Bozogullarindan and Ceylan Bozogullarindan and Celal Ozturk", title = "Transfer Learning in Artificial Bee Colony Programming", booktitle = "2020 Innovations in Intelligent Systems and Applications Conference (ASYU)", year = "2020", month = "15-17 " # oct, address = "Istanbul, Turkey", keywords = "genetic algorithms, genetic programming, artificial bee colony algorithm, learning, artificial intelligence, regression analysis, symbolic regression problems, machine learning, transfer learning, ABCP-T", isb13 = "978-1-7281-9137-9", DOI = "doi:10.1109/ASYU50717.2020.9259801", abstract = "Artificial Bee Colony Programming (ABCP) is a machine learning method based on Artificial Bee Colony (ABC) algorithm used for parametric and structured optimization problems. It is used for the solution of symbolic regression problems as Genetic Programming (GP). On the other hand, transfer learning is the approach of using the knowledge of a system trained for a particular problem in another problem having a similar distribution. There are a number of research studies in the literature reporting the successful applications of the transfer learning to machine learning and GP. the transfer learning approach is applied to ABCP for the first time and all of the new methods created this way are named as ABCP-T. As a result of the experiments conducted for the symbolic regression problems in the literature, it is observed that ABCP-T gives better results than the standard ABCP.", notes = "Also known as \cite{9259801}", } @Article{Bozorg-Haddad:2017:JEE, author = "Omid Bozorg-Haddad and Shima Soleimani and Hugo A. Loaiciga", title = "Modeling Water-Quality Parameters Using Genetic Algorithm-Least Squares Support Vector Regression and Genetic Programming", journal = "Journal of Environmental Engineering", year = "2017", volume = "143", number = "7", month = jul, keywords = "genetic algorithms, genetic programming, Genetic algorithm-least squares support vector regression (GA-LSSVR) algorithm, Water quality, Modeling, Sensitivity analysis, Principal component analysis, PCA", publisher = "American Society of Civil Engineers", URL = "https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EE.1943-7870.0001217?src=recsys", DOI = "doi:https://doi.org/10.1061/(ASCE)EE.1943-7870.0001217", size = "10 pages", abstract = "The modeling and monitoring of water-quality parameters is necessary because of the ever increasing use of water resources and contamination caused by sewage disposal. This study employs two data-driven methods for modeling water-quality parameters. The methods are the least-squares support vector regression (LSSVR) and genetic programming (GP). Model inputs to the LSSVR algorithm and GP were determined using principal component analysis (PCA). The coefficients of the LSSVR were selected by sensitivity analysis employing statistical criteria. The results of the sensitivity analysis of the LSSVR showed that its accuracy depends strongly on the values of its coefficients. The value of the Nash-Sutcliffe (NS) statistic was negative for 60percent of the combinations of coefficients applied in the sensitivity analysis. That is, using the mean of a time series would produce a more accurate estimate of water-quality parameters than the LSSVR method in 60percent of the combinations of parameters tried. The genetic algorithm (GA) was combined with LSSVR to produce the GA-LSSVR algorithm with which to achieve improved accuracy in modeling water-quality parameters. The GA-LSSVR algorithm and the GP method were employed in modeling Na+, K+, Mg2+, SO2-4, Cl-, pH, electric conductivity (EC), and total dissolved solids (TDS) in the Sefidrood River, Iran. The results indicate that the GA-LSSVR algorithm has better accuracy for modeling water-quality parameters than GP judged by the coefficient of determination (R2) and the NS criterion. The NS static established, however, that the GA-LSSVR and GP methods have the capacity to model water-quality parameters accurately.", notes = "Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, Univ. of Tehran, Karaj, 31587-77871 Tehran, Iran", } @InProceedings{Bozorgtabar:2010:IST, author = "Behzad Bozorgtabar and Farzad Noorian and Gholam Ali Rezai Rad", title = "Comparison of different PCA based Face Recognition algorithms using Genetic Programming", booktitle = "5th International Symposium on Telecommunications (IST 2010)", year = "2010", month = dec, pages = "801--805", abstract = "Face Recognition plays a vital role in automation of security systems; therefore many algorithms have been invented with varying degrees of effectiveness. After successful try out of principal component analyses (PCA) in eigenfaces method, many different PCA based algorithms such as Two Dimensional PCA (2DPCA) and Multilinear PCA (MLPCA), combined with several classifying algorithms were studied. This paper uses Genetic Programming (GP) as a clustering tool, to classify features extracted by PCA, 2DPCA and MLPCA. Results of different algorithms are compared with each other and also previous studies and it is shown that Genetic Programming can be used in combination with PCA for face recognition problems.", keywords = "genetic algorithms, genetic programming, eigenfaces method, face recognition algorithms, multilinear PCA, principal component analyses, security systems automation, two dimensional PCA, eigenvalues and eigenfunctions, face recognition, principal component analysis", DOI = "doi:10.1109/ISTEL.2010.5734132", notes = "Also known as \cite{5734132}", } @InProceedings{Bozorgtabar:2011:GCC, author = "Behzad Bozorgtabar and Farzad Noorian and Rezai Rad {Gholam Ali}", title = "A Genetic Programming approach to face recognition", booktitle = "IEEE GCC Conference and Exhibition (GCC), 2011", year = "2011", pages = "194--197", address = "Dubai, United Arab Emirates", month = feb # " 19-22", publisher = "IEEE", size = "4 pages", abstract = "Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), an acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.", keywords = "genetic algorithms, genetic programming, data mining, face recognition technology, feature extraction, image group classification, pattern recognition, principal component analysis, relation discovery methodology, data mining, face recognition, feature extraction, image classification, principal component analysis", DOI = "doi:10.1109/IEEEGCC.2011.5752477", notes = "Iran University of Science and Technology Also known as \cite{5752477}", } @Article{Bozorgtabar:2011:JSIP, author = "Behzad Bozorgtabar and Gholam Ali Rezai Rad", title = "A Genetic Programming-{PCA} Hybrid Face Recognition Algorithm", journal = "Journal of Signal and Information Processing", year = "2011", volume = "2", number = "3", pages = "170--174", publisher = "Scientific Research Publishing", keywords = "genetic algorithms, genetic programming, face recognition, principal component analysis, leveraging algorithm", ISSN = "21594465", broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=21594465\&date=2011\&volume=02\&issue=03\&spage=170", DOI = "doi:10.4236/jsip.2011.23022", size = "5 pages", abstract = "Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.", bibsource = "OAI-PMH server at www.doaj.org", language = "eng", oai = "oai:doaj-articles:99426617eefcb9ce06c76152e081e501", } @InProceedings{brabazon:2001:AAANZ, author = "Tony Brabazon and M. O'Neill and C. Ryan and J. J. Collins", title = "Uncovering Technical Trading Rules Using Evolutionary Automatic Programming", booktitle = "Proceedings of 2001 AAANZ Conference (Accounting Association of Australia and NZ)", year = "2001", address = "Auckland, New Zealand", month = "1-3 " # jul, keywords = "genetic algorithms, genetic programming, grammatical evolution, financial prediction", } @InProceedings{brabazon:2002:EuroGP, title = "Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution", author = "Anthony Brabazon and Michael O'Neill and Conor Ryan and Robin Matthews", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "103--112", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, publisher = "Springer-Verlag", year = "2002", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "3-540-43378-3", DOI = "doi:10.1007/3-540-45984-7_10", abstract = "This study examines the potential of grammatical evolution to construct a linear classifier to predict whether a firm's corporate strategy will increase or decrease shareholder wealth. Shareholder wealth is measured using a relative fitness criterion, the change in a firm's market-value-added ranking in the Stern-Stewart Performance 1000 list, over a four year period, 1992-1996. Model inputs and structure are selected by means of grammatical evolution. The best classifier correctly categorised the direction of performance ranking change in 66.38percent of the firms in the training set and 65percent in the out-of-sample validation set providing support for a hypothesis that changes in corporate strategy are linked to changes in corporate performance.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @InProceedings{brabazon:2002:gecco, author = "Anthony Brabazon and Michael O'Neill and Robin Matthews and Conor Ryan", title = "Grammatical Evolution And Corporate Failure Prediction", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "1011--1018", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, real world applications, corporate failure prediction, genotype to phenotype mapping, grammars", ISBN = "1-55860-878-8", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA145.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/RWA145.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf", size = "8 pages", abstract = "This study examines the potential of Grammatical Evolution to uncover a series of useful rules which can assist in predicting corporate failure using information drawn from financial statements. A sample of 178 publically quoted, failed and non-failed Us firms, drawn from the period 1991 to 2000 are used to train and test the model. The preliminary findings indicate that the methodology has much potential.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", } @InProceedings{brabazon:2002:gecco:workshop, title = "Trading Foreign Exchange Markets Using Evolutionary Automatic Programming", author = "Tony Brabazon and Michael O'Neill", pages = "133--136", booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2002", month = "8 " # jul, publisher = "AAAI", address = "New York", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", keywords = "genetic algorithms, genetic programming, grammatical evolution", URL = "http://www.grammatical-evolution.org/gews2002/brabazon.ps", notes = "Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop", } @InProceedings{Brabazon:2003:ICAI, author = "Anthony Brabazon and Michael O'Neill", title = "A Grammar Model for Foreign-Exchange Trading", booktitle = "Proceedings of the International Conference on Artificial Intelligence", year = "2003", editor = "Hamid R. Arabnia and Rose Joshua and Youngsong Mun", volume = "II", pages = "492--498", month = "23-26 " # jun, publisher = "CSREA Press", address = "Las Vegas, USA", keywords = "genetic algorithms, genetic programming", ISBN = "1-932415-13-0", URL = "https://www.tib.eu/en/search/id/BLCP%3ACN050261220/A-Grammar-Model-for-Foreign-Exchange-Trading/", abstract = "This study examines the potential of Grammatical Evolution to uncover a series of useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved programs represents a market trading system and implicitly, a predictive model. The form of these programs is not specified ex-ante but emerges by means of an evolutionary process. Daily US Dollar-DM exchange rates for the period 9/3/93 to 13/10/97 are used to train and test the model. The preliminary findings suggest that the developed rules earn positive returns in hold-out sample test periods after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules.", notes = "IC-AI 2003. Also known as \cite{DBLP:conf/icai/BrabazonO03}", } @InProceedings{Brabazon:2004:BYB, author = "Anthony Brabazon and Robin Matthews and Michael O'Neill", title = "Grammars, Representations, Mental Maps and Corporate Strategy", booktitle = "Business Research Yearbook: Global Business Perspectives. Proceedings of the Fifteenth Annual International Conference of the International Academy of Business Disciplines", year = "2004", editor = "C. Gardner and J. Biberman and A. Alkhafaji", volume = "11", pages = "1054--1058", address = "San Antonio, USA", publisher_address = "Saline, Michigan, USA", month = mar # " 24-27", printer = "McNaughton and Gunn", keywords = "genetic algorithms, genetic programming, grammatical evolution", notes = "broken Feb 2014 http://academic.scranton.edu/faculty/BIBERMANG1/pres.htm", } @InProceedings{brabazon:evows04, author = "Anthony Brabazon and Michael O'Neill", title = "Bond-Issuer Credit Rating with Grammatical Evolution", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2004", month = "5-7 " # apr, editor = "Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3005", address = "Coimbra, Portugal", publisher = "Springer Verlag", publisher_address = "Berlin", pages = "270--279", keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation", ISBN = "3-540-21378-3", DOI = "doi:10.1007/978-3-540-24653-4_28", abstract = "This study examines the utility of Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard & Poor's issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment-grade and junk bond ratings with an average accuracy of 87.59 (84.92)percent across a five-fold cross validation. The results suggest that the two classifications of credit rating can be predicted with notable accuracy from a relatively limited subset of firm-specific financial data, using Grammatical Evolution.", notes = "EvoWorkshops2004", } @Article{brabazon:2005:GMTSP, author = "Anthony Brabazon and Katrina Meagher and Edward Carty and Michael O'Neill and Peter Keenan", title = "Grammar-mediated time-series prediction", journal = "Journal of Intelligent Systems", year = "2004", volume = "14", number = "2--3", pages = "123--143", month = aug, keywords = "genetic algorithms, genetic programming, grammatical evolution, time-series, high-frequency finance, intra-day stock trading", ISSN = "2191-026X", ISSN = "0334-1860", DOI = "doi:10.1515/JISYS.2005.14.2-3.123", size = "20 pages", abstract = "Grammatical Evolution is a data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study examines the potential of Grammatical Evolution to uncover useful technical trading rulesets for intra-day equity trading. The form of these rule-sets is not specified ex-ante but emerges by means of an evolutionary process. High-frequency price data drawn from United States stock markets is used to train and test the model. The findings suggest that the developed rules earn positive returns in holdout test periods, and that the sizes of these returns are critically impacted by the choice of position exit-strategy.", } @Article{BrabazonONeill:2004:IJAMCSDCSuGE, author = "Anthony Brabazon and Michael O'Neill", title = "Diagnosing Corporate Stability using Grammatical Evolution", journal = "International Journal of Applied Mathematics and Computer Science", year = "2004", volume = "14", number = "3", pages = "363--374", keywords = "genetic algorithms, genetic programming, Grammatical Evolution, corporate failure prediction", ISSN = "1641-876X", URL = "http://eudml.org/doc/207703", URL = "http://matwbn.icm.edu.pl/ksiazki/amc/amc14/amc1436.pdf", size = "12 pages", abstract = "Grammatical Evolution (GE) is a novel data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to construct a series of models for the prediction of bankruptcy, employing information drawn from financial statements. Unlike prior studies in this domain, the raw financial information is not preprocessed into pre-determined financial ratios. Instead, the ratios to be incorporated into the classification rule are evolved from the raw financial data. This allows the creation and subsequent evolution of alternative ratio-based representations of the financial data. A sample of 178 publicly quoted, US firms, drawn from the period 1991 to 2000 are used to train and test the model. The best evolved model correctly classified 86 (77)percent of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.", notes = "Also known as \cite{Brabazon2004}", } @Article{BrabazonONeill:2004:CMSETTRfSFEMuGE, author = "Anthony Brabazon and Michael O'Neill", title = "Evolving Technical Trading Rules for Spot Foreign-Exchange Markets Using Grammatical Evolution", journal = "Computational Management Science", year = "2004", volume = "1", number = "3-4", pages = "311--327", month = oct, keywords = "genetic algorithms, genetic programming, Grammatical evolution, Foreign exchange prediction, Technical trading rules", publisher = "Springer-Verlag", ISSN = "1619-697X", URL = "https://rdcu.be/dO4Fe", DOI = "doi:10.1007/s10287-004-0018-5", size = "17 pages", abstract = "Grammatical Evolution (GE) is a novel, data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and applies the methodology in an attempt to uncover useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved rules (programs) represents a market trading system. The form of these programs is not specified ex-ante, but emerges by means of an evolutionary process. Daily US-DM, US-Stg and US-Yen exchange rates for the period 1992 to 1997 are used to train and test the model. The findings suggest that the developed rules earn positive returns in hold-out sample test periods, after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules. It is also noted that this novel methodology has general utility for rule-induction, and data mining applications.", } @Article{Brabazon:2005:JIS, author = "A. Brabazon and K. Meagher and E. Carty and M. O'Neill and P. Keenan", title = "Grammar-Mediated Time-Series Prediction", journal = "Journal of Intelligent Systems", year = "2005", volume = "14", number = "2-3", pages = "123--143", note = "Special Issue", notes = "duplicate of \cite{brabazon:2005:GMTSP}", } @InProceedings{brabazon:2005:CRWpiGE, author = "Anthony Brabazon and Michael O'Neill", title = "Credit Rating with pi Grammatical Evolution", booktitle = "Proceedings of Computer Methods and Systems Conference", year = "2005", editor = "R. Tadeusiewicz and A. Ligeza and M. Szymkat", volume = "1", pages = "253--260", address = "Krakow, Poland", publisher_address = "Krakow", month = "14-16 " # nov, publisher = "Oprogramowanie Naukowo-Techniczne Tadeusiewicz", keywords = "genetic algorithms, genetic programming, grammatical evolution", ISBN = "83-916420-3-8", abstract = "This study examines the utility of pi Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard and Poor's issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of 86 (87)percent across a five-fold cross validation.", } @Book{Brabazon:2006:BIAS, author = "Anthony Brabazon and Michael O'Neill", title = "Biologically Inspired Algorithms for Financial Modelling", publisher = "Springer", year = "2006", series = "Natural Computing Series", keywords = "genetic algorithms, genetic programming, ant colony systems, artificial immune systems, biologically inspired algorithms (BIAs), computer trading, evolutionary methodologies, financial markets, financial trading, grammatical evolution, (GE), multilayer perceptrons, neural networks (NNs), particle swarm optimisation (PSO)", ISBN = "3-540-26252-0", DOI = "doi:10.1007/3-540-31307-9", notes = "reviewed by \cite{Kaboudan:2006:GPEM} also Brad G. Kyer, The Book Review Column 40(4), 2009, p11-17, William Gasarch, http://www.cs.umd.edu/~gasarch/bookrev/", size = "275 pages", } @Article{Brabazon:2006:I, author = "Anthony Brabazon and Michael O'Neill", title = "Credit Classification Using Grammatical Evolution", journal = "Informatica", year = "2006", volume = "30", number = "3", pages = "325--335", keywords = "genetic algorithms, genetic programming, grammatical evolution, Povzetek: Metoda gramaticne evolucije je uporabljena za klasificiranje kreditov.", ISSN = "0350-5596", URL = "http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf", size = "11 pages", abstract = "Grammatical Evolution (GE) is a novel data driven, model induction tool, inspired by the biological genetoprotein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to model the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data and the associated Standard & Poor's issuer credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of 87.59 (84.92)percent across a five-fold cross validation.", notes = "http://www.informatica.si/vol30.htm#No3", } @InCollection{Brabazon:2008:K-DC, author = "Anthony Brabazon and Michael O'Neill", title = "Bond Rating with {piGrammatical} Evolution", booktitle = "Knowledge Engineering and Intelligent Computations", publisher = "Springer", year = "2008", editor = "C. Cotta and S. Reich and R. Schaefer and A. Ligeza", volume = "102", series = "Studies in Computational Intelligence", chapter = "2", pages = "17--30", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-540-77474-7", DOI = "doi:10.1007/978-3-540-77475-4_2", abstract = "Most large firms use both share and debt capital to provide long-term finance for their operations. The debt capital may be raised from a bank loan, or may be obtained by selling bonds directly to investors. As an example of the scale of US bond markets, the value of new bonds issued in 2004 totaled $5.48 trillion, and the total value of outstanding marketable bond debt at 31 December 2004 was $23.6 trillion [1]. In comparison, the total global market capitalisation of all companies quoted on the New York Stock Exchange (NYSE) at 31/12/04 was $19.8 trillion [2]. Hence, although company stocks attract most attention in the business press, bond markets are actually substantially larger. When a company issues traded debt (e.g. bonds), it must obtain a credit rating for the issue from at least one recognised rating agency (Standard and Poor's (S&P), Moody's and Fitches'). The credit rating represents an agency's opinion, at a specific date, of the credit worthiness of a borrower in general (a bond-issuer credit-rating), or in respect of a specific debt issue (a bond credit rating). These ratings impact on the borrowing cost, and the marketability of issued bonds. Although several studies have examined the potential of both statistical and machine-learning methodologies for credit rating prediction [3-6], many of these studies used relatively small sample sizes, making it difficult to generalise strongly from their findings. This study by contrast, uses a large dataset of 791 firms, and introduces pi GE to this domain.", } @Book{Brabazon:2008:edbook, editor = "Anthony Brabazon and Michael O'Neill", title = "Natural Computing in Computational Finance", publisher = "Springer", year = "2008", volume = "100", series = "Studies in Computational Intelligence", month = apr, keywords = "genetic algorithms, genetic programming, computational finance, evolution strategies, differential evolution, bacterial foraging, quantum-inspired evolutionary algorithms", isbn13 = "9783540774761", URL = "http://www.springer.com/engineering/book/978-3-540-77476-1", abstract = "Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modelling in modern computational finance. Following an introductory chapter the book is organised into three sections. The first section deals with optimisation applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.", size = "approx 300 pages", } @Article{Brabazon:2008:IEEECIM, author = "Anthony Brabazon and Michael O'Neill and Ian Dempsey", title = "An Introduction to Evolutionary Computation in Finance", journal = "IEEE Computational Intelligence Magazine", year = "2008", volume = "3", number = "4", pages = "42--55", month = nov, URL = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2008&isnumber=4625777&Submit32=Go+To+Issue", DOI = "doi:10.1109/MCI.2008.929841", ISSN = "1556-603X", keywords = "genetic algorithms, genetic programming, grammatical evolution, finance, evolutionary computation, financial data processing computational intelligence methodologies, evolutionary computation approach, finance", abstract = "The world of finance is an exciting and challenging environment. Recent years have seen an explosion in the application of computational intelligence methodologies in finance. In this article we provide an overview of some of these applications concentrating on those employing an evolutionary computation approach.", notes = "Also known as \cite{4625793}", } @Book{Brabazon:2009:book, editor = "Anthony Brabazon and Michael O'Neill", title = "Natural Computing in Computational Finance (Volume 2)", publisher = "Springer", year = "2009", volume = "185", series = "Studies in Computational Intelligence", month = mar, keywords = "genetic algorithms, genetic programming, computational Finance, Computational Intelligence", isbn13 = "978-3-540-95973-1", URL = "http://www.springer.com/engineering/book/978-3-540-95973-1", abstract = "About this book Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modeling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems. This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer's Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007. Written for: Engineers, researchers, and graduate students in Computational Intelligence and Computer Finance", size = "approx 260 pages", } @Book{brabazon_oneill_maringer:2010:book, editor = "A. Brabazon and M. O'Neill and D. G. Maringer", title = "Natural Computing in Computational Finance (Volume 3)", publisher = "Springer", year = "2010", volume = "293", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming, natural computing, computational finance, computational intelligence", isbn13 = "978-3-642-13949-9", URL = "http://www.springer.com/engineering/book/978-3-642-13949-9", DOI = "doi:10.1007/978-3-642-13950-5", abstract = "This book consists of eleven chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. The inspiration for this book was due in part to the success of EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics. This book follows on from Natural Computing in Computational Finance Volumes I \cite{Brabazon:2008:edbook} and II \cite{Brabazon:2009:book}", size = "241 pages", } @Unpublished{abrabazon_moneill:ppsn2010, author = "A. Brabazon and M. O'Neill", howpublished = "PPSN 2010 11th International Conference on Parallel Problem Solving From Nature", address = "Krakow, Poland", month = "11-15 " # sep, note = "Tutorial", title = "Natural Computing and Finance", URL = "http://ncra.ucd.ie/papers/PPSN_tutorial_2010_published.pdf", year = "2010", keywords = "genetic algorithms, genetic programming, grammatical evolution, finance", size = "69 slides", } @InCollection{BrabazonDDOE:2012:HNCNCiFAR, author = "Anthony Brabazon and Jing Dang and Ian Dempsey and Michael O'Neill and David Edelman", title = "Natural Computing in Finance - A Review", booktitle = "Handbook of Natural Computing", publisher = "Springer", year = "2012", editor = "Grzegorz Rozenberg and Thomas Baeck and Joost N. Kok", volume = "2", chapter = "51", pages = "1707--1735", month = "19 " # aug, keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-540-92909-3", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-92911-6", DOI = "doi:10.1007/978-3-540-92910-9_51", abstract = "The field of natural computing (NC) has advanced rapidly over the past decade. One significant offshoot of this progress has been the application of NC methods in finance. This chapter provides an introduction to a wide range of financial problems to which NC methods have been usefully applied. The chapter also identifies open issues and suggests future directions for the application of NC methods in finance.", } @Book{Brabazon:book:NCA, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Natural Computing Algorithms", publisher = "Springer", year = "2015", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-662-43630-1", size = "401 pages", abstract = "The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.", notes = "Chapters on GP \cite{Brabazon:book:NCA.7} \cite{Brabazon:book:NCA.17} \cite{Brabazon:book:NCA.18} \cite{Brabazon:book:NCA.19} \cite{Brabazon:book:NCA.20}", } @InCollection{Brabazon:book:NCA.7, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Genetic Programming", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "7", pages = "95--114", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_7", abstract = "Genetic programming (GP) was initially developed to allow the automatic creation of a computer program from a high-level statement of a problem's requirements, by means of an evolutionary process. In GP, a computer program to solve a defined task is evolved from an initial population of random computer programs. An iterative evolutionary process is employed by GP, where better (fitter) programs for the task at hand are allowed to reproduce using recombination processes to recombine components of existing programs. The reproduction process is supplemented by incremental trial-and-error development, and both variety-generating mechanisms act to generate variants of existing good programs. Over time, the utility of the programs in the population improves as poorer solutions to the problem are replaced by better solutions. More generally, GP has been applied to evolve a wide range of structures (and their associated parameters) including electronic circuits, mathematical models, engineering designs, etc.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.17, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "An Introduction to Developmental and Grammatical Computing", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "17", pages = "335--343", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_17", abstract = "To say that the knowledge uncovered by developmental biologists has been under-exploited in natural computing is perhaps an understatement. Curiously, despite the relative lack of research attention that has been paid to these important biological processes, one of the fathers of Computer Science, Alan Turing, recognised the power of developmental systems and developed reaction-diffusion models to understand the mechanisms behind morphogenesis (the development of biological form) [638]. In recent years it is heartening to see researchers beginning to close this gap and start to explore the power of developmental processes such as genetic regulatory networks for problem solving, and the use of approaches such as self-modification of phenotypes and developmental evaluation. The surge in interest in developmental computing is illustrated by the creation of a new track dedicated to Generative and Developmental Systems which began in 2007 at the ACM Genetic and Evolutionary Computation Conference [72, 321, 347, 526, 586, 623] and which has run every year since. A special issue of the journal IEEE Transactions on Evolutionary Computation was also dedicated to this topic in 2011 [639]. In this chapter the concept of developmental computing is introduced with particular emphasis on grammatical computing, which uses grammars as a generative process in order to create structures of interest.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.18, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Grammar-Based and Developmental Genetic Programming", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "18", pages = "345--356", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_18", abstract = "The use of grammars in genetic programming (GP) has a long tradition, and there are many examples of different approaches in the literature representing linear, tree-based and more generally graph-based forms. McKay et al. \cite{McKay:2010:GPEM} presented a survey of grammar-based GP in the 10th Anniversary issue of the journal Genetic Programming and Evolvable Machines. In this and subsequent chapters, we highlight some of the more influential forms of grammar-based and developmental GP.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.19, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Grammatical Evolution", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "19", pages = "357--373", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-662-43630-1", ISSN = "1619-7127", DOI = "doi:10.1007/978-3-662-43631-8_19", abstract = "Grammatical Evolution (GE), a form of grammar-based genetic programming (Chap. 18 \cite{Brabazon:book:NCA.18} ), is an algorithm that can evolve computer programs, rulesets or, more generally, sentences in any language [150, 460, 470, 472, 547]. Rulesets could be as diverse as a regression model, a set of design instructions, or a trading system for a financial market. Rather than representing the programs as syntax trees, as in GP (Chap. 7 \cite{Brabazon:book:NCA.7} ) [340, 514], a linear genome representation is used in conjunction with a grammar.", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:book:NCA.20, author = "Anthony Brabazon and Michael O'Neill and Sean McGarraghy", title = "Tree-Adjoining Grammars and Genetic Programming", booktitle = "Natural Computing Algorithms", publisher = "Springer", year = "2015", series = "Natural Computing Series", chapter = "20", pages = "375--381", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-662-43630-1", DOI = "doi:10.1007/978-3-662-43631-8_20", notes = "Part of \cite{Brabazon:book:NCA}", } @InCollection{Brabazon:2018:hbge, author = "Anthony Brabazon", title = "Grammatical Evolution in Finance and Economics: A Survey", booktitle = "Handbook of Grammatical Evolution", publisher = "Springer", year = "2018", editor = "Conor Ryan and Michael O'Neill and J. J. Collins", chapter = "11", pages = "263--288", keywords = "genetic algorithms, genetic programming, Grammatical Evolution", isbn13 = "978-3-319-78716-9", DOI = "doi:10.1007/978-3-319-78717-6_11", abstract = "Finance was one of the earliest application domains for Grammatical Evolution (GE). Since the first such study in 2001, well in excess of 100 studies have been published employing GE for a diverse range of purposes encompassing financial trading, credit-risk modelling, supply chain management, detection of tax non-compliance, and corporate strategy modelling. This chapter surveys a sample of this work and in doing so, suggests some future directions for the application of GE in finance and economics.", notes = "Part of \cite{Ryan:2018:hbge}", } @Article{Brabazon:GPEM20, author = "Anthony Brabazon and Michael Kampouridis and Michael O'Neill", title = "Applications of genetic programming to finance and economics: past, present, future", journal = "Genetic Programming and Evolvable Machines", year = "2020", volume = "21", number = "1-2", pages = "33--53", month = jun, note = "Twentieth Anniversary Issue", keywords = "genetic algorithms, genetic programming, Finance, Economics, Quantitative trading", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-019-09359-z", size = "21 pages", abstract = "While the origins of genetic programming (GP) stretch back over 50 years, the field of GP was invigorated by John Koza popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the GP bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics.", } @InProceedings{BradburyJ10, author = "Jeremy S. Bradbury and Kevin Jalbert", title = "Automatic Repair of Concurrency Bugs", booktitle = "Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10)", year = "2010", editor = "Massimiliano {Di Penta} and Simon Poulding and Lionel Briand and John Clark", address = "Benevento, Italy", month = "7-9 " # sep, note = "Fast abstract", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE, concurrency, mutation :poster?", URL = "http://www.ssbse.org/2010/fastabstracts/ssbse2010_fastabstract_04.pdf", size = "2 pages", abstract = "Bugs in concurrent software are difficult to identify and fix since they may only exhibit abnormal behaviour on certain thread interleavings. We propose the use of genetic programming to incrementally create a solution that fixes a concurrency bug automatically. Bugs in a concurrent program are fixed by iteratively mutating the program and evaluating each mutation using a fitness function that compares the mutated program with the previous version. We propose three mutation operators that can fix concurrency bugs: synchronise an unprotected shared resource, expand synchronization regions to include unprotected source code, and interchange nested lock objects.", notes = "GenProg. Focus on deadlock and dead race bugs. Add synchronisation primitives around shared variables. Expand code region protected by existing synchronisation primitives (locks). Swap existing locks. Test based fitness. IBM ConTest. Hill climbing. Mutant chosen according to bug. Fast abstracts not in proceedings? broken http://www.ssbse.info/2010/program.php http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5633360", } @InCollection{braden:2002:AAPSPGA, author = "Katie Braden", title = "A simple Approach to Protein Structure Prediction using Genetic Algorithms", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 2002", year = "2002", editor = "John R. Koza", pages = "36--44", address = "Stanford, California, 94305-3079 USA", month = jun, publisher = "Stanford Bookstore", keywords = "genetic algorithms", URL = "http://www.genetic-programming.org/sp2002/Braden.pdf", notes = "part of \cite{koza:2002:gagp}", } @InProceedings{bradley:2010:evofin, author = "Robert Gregory Bradley and Anthony Brabazon and Michael O'Neill", title = "Evolving Trading Rule-Based Policies", booktitle = "EvoFIN", year = "2010", editor = "Cecilia {Di Chio} and Anthony Brabazon and Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and Michael O'Neill and Ernesto Tarantino and Neil Urquhart", volume = "6025", series = "LNCS", pages = "251--260", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, grammatical evolution", isbn13 = "978-3-642-12241-5", DOI = "doi:10.1007/978-3-642-12242-2_26", abstract = "Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.", notes = "EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010", } @InProceedings{bradley_etal:cec2010, author = "Robert Bradley and Anthony Brabazon and Michael O'Neill", title = "Objective Function Design in a Grammatical Evolutionary Trading System", booktitle = "2010 IEEE World Congress on Computational Intelligence", pages = "3487--3494", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", isbn13 = "978-1-4244-6910-9", keywords = "genetic algorithms, genetic programming, grammatical evolution", DOI = "doi:10.1109/CEC.2010.5586020", abstract = "Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution to the problem of trading model induction. A number of experiments were performed to assess the effect of objective function design on the trading characteristics of the evolved trading strategies. Empirical results suggest that the choice of objective function has a significant impact. The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work.", notes = "WCCI 2010. Also known as \cite{5586020}", } @Article{Brady:2014:acmTG, title = "{genBRDF}: discovering new analytic {BRDFs} with genetic programming", author = "Adam Brady and Jason Lawrence and Pieter Peers and Westley Weimer", journal = "ACM Transactions on Graphics", year = "2014", volume = "33", number = "4", pages = "114:1--114:11", month = jul, keywords = "genetic algorithms, genetic programming, GPU, BRDF, analytic, isotropic", publisher = "ACM", ISSN = "0730-0301", acmid = "2601193", URL = "https://web.eecs.umich.edu/~weimerw/p/brady_sig14.pdf", URL = "http://doi.acm.org/10.1145/2601097.2601193", DOI = "doi:10.1145/2601097.2601193", size = "11 pages", abstract = "We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modelling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterise complex high-dimensional reflectance functions through a large-scale optimisation. We present a number of analysis tools and data visualisation techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives.", notes = " p114:2 'we attempt to grow new' [mathematical expressions]. p114:5 'implemented the BRDF fitting procedure in CUDA and used a 24 node NVidia Tesla M2075 GPU cluster.' cited by \cite{Dorn:2015:Pacific-Graphics}", } @TechReport{brady:murphy, author = "Robert M. Brady and Ross J. Anderson and Robin C. Ball", title = "Murphy's law, the fitness of evolving species, and the limits of software reliability", institution = "Computer Laboratory, Cambridge", year = "1996?", email = "rja14@cl.cam.ac.uk", URL = "http://www.ftp.cl.cam.ac.uk/ftp/users/rja14/babtr.pdf", abstract = "We tackle two problems of interest to the software assurance community. Firstly, existing models of software development (such as the waterfall and spiral models) are oriented towards one-off software development projects, while the growth of mass market computing has led to a world in which most software consists of packages which follow an evolutionary development model. This leads us to ask whether anything interesting and useful may be said about evolutionary development. We answer in the affirmative. Secondly, existing reliability growth models emphasise the Poisson distribution of individual software bugs, while the empirically observed reliability growth for large systems is asymptotically slower than this. We provide a rigorous explanation of this phenomenon. Our reliability growth model is inspired by statistical thermodynamics, but also applies to biological evolution. It is in close agreement with experimental measurements of the fitness of an evolving species and the reliability of commercial software products. However, it shows that there are significant differences between the evolution of software and the evolution of species. In particular, we establish maximisation properties corresponding to Murphy?s law which work to the advantage of a biological species, but to the detriment of software reliability.", size = "11 pages", notes = "cf \cite{Bishop96} Takes huge liberties, dressing them in maths, {"}the number of defects which survive a selection process is maximised{"} {"}debugging removes the minimum possible number of bugs that must be removed in order to pass the test sequence{"}. {"}we have a dsitribution of deffects that e behaves statisically as if they were in thermal equilibrium at this{"} [1/t] {"}temperature{"}.", } @Article{DBLP:journals/npl/Braik21, author = "Malik Braik", title = "A Hybrid Multi-gene Genetic Programming with Capuchin Search Algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study", journal = "Neural Process. Lett.", volume = "53", number = "4", pages = "2873--2916", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1007/s11063-021-10530-w", DOI = "doi:10.1007/s11063-021-10530-w", timestamp = "Thu, 12 Aug 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/npl/Braik21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Book{Braitenberg:1984, author = "Valentino Braitenberg", year = "1984", keywords = "NEURAL MOBILE SIMULATION EVOLUTION MOTOR-SCHEMA REACTIVE MODULAR", institution = "Europe?", title = "Vehicles", publisher = "MIT Press", annote = "Braitenberg describes a set of thought experiments in which increasingly complex vehicles are built from simple mechanical and electronic components. Each of these imaginary vehicles in some way mimics intelligent behavior, and each one is given a name that corresponds to the behavior it imitates: {"}Fear,{"} {"}Love,{"} {"}Values,{"} {"}Logic,{"} etc. Braitenberg uses these thought experiments to explore psychological ideas and the nature of intelligence. Progressing through the book, the reader sees very intricate behaviors emerge from the interaction of simple component parts. In a sense, Braitenberg {"}constructs{"} intelligent behavior---a process he calls {"}synthetic psychology.{"} - from [Hogg Martin \& Resnick 91]", address = "Cambridge MA, USA", ISBN = "0-262-52112-1", notes = "amazon says 1986", } @InProceedings{Brajer:2012:MIPRO, author = "Iva Brajer and Domagoj Jakobovic", title = "Automated design of combinatorial logic circuits", booktitle = "Proceedings of the 35th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2012)", year = "2012", month = "21-25 " # may, pages = "823--828", address = "Opatija, Croatia", size = "6 pages", abstract = "This paper deals with automated design of combinatorial circuits with the use of Cartesian Genetic Programming (CGP). The synthesis is based on user specifications of network functionality, while the network structure may be predefined. The results show that CGP approach is able to match the desired functionality while preserving other performance criteria, such as latency and number of gates. Additionally, the evolution process may use Verilog network descriptions as input files, which facilitates the design for larger number of inputs and test patterns.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6240757", notes = "Also known as \cite{6240757}", } @InProceedings{Brajlih:2005:PMI, author = "Tomaz Brajlih and Igor Drstvensek and Miha Kovacic and Joze Balic", title = "Compensation of the size of the finished part for the PolyJet rapid prototyping procedure", booktitle = "Proceedings of the International Conference Polymers \& Moulds Innovations PMI 2005", year = "2005", address = "Gent, Belgium", month = apr # " 20-23", keywords = "genetic algorithms, genetic programming, hitra izdelava prototipov, PolyJet postopek, izravnalni faktor, prototipi, rapid prototyping, polyjet procedure, compensation factor", abstract = "The main accuracy problem of rapid prototyping procedures, which are using polymers as a building material is shrinking of a finished layer in the phase of polymerization. Different procedures are using different approaches to handle the problem but none of them can actually reach the accuracy that users of traditional cutting techniques are used to. To achieve better quality of the PolyJet procedure, which originally employs a method of size compensation to reach a desired accuracy, we decided to improve the procedure's performance by adjusting the compensation factor for every part separately. To this purpose some traditional methods of statistics were used, which were later combined with some newer, less traditional methods like genetic programming. The later enabled us to acquire a formula for compensation factor determination based upon the geometry of the actual part. It also showed the importance or unimportance of some influencing parameters respectively. The method resulted in a better compensation factor and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays.", notes = " broken Aug 2018 http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9636118", } @InProceedings{Brajlih:2006:DAAAM, author = "T. Brajlih and I. Drstvensek and B. Valentan and J. Balic", title = "Improving the Accuracy of Rapid Prototyping Procedures by Genetic Programming", booktitle = "Proceedings of the 5TH International conference of DAAAM Baltic -- Industrial Engineering", year = "2006", editor = "R. Kyeener", pages = "113--116", address = "Tallinn, Estonia", month = "20-22 " # apr, organisation = "BALTECH Consortium, Estonian Academy of Sciences, Federation of Estonian Engineering Industries, Association of Estonian Mechanical Engineers, Leonardo National Agency of Estonia, INNOMET", publisher = "DAAAM", keywords = "genetic algorithms, genetic programming", URL = "http://innomet.ttu.ee/daaam06/proceedings/Production%20Engineering/24brajilih.pdf", size = "4 pages", abstract = "To achieve better quality of the PolyJet Rapid Prototyping procedure, which originally employs a method of size compensation by scale factors to reach a desired accuracy, we decided to improve the procedure's performance by adjusting scale factors for every part separately. The main accuracy problem of rapid prototyping procedures that are using polymers as a building material is shrinking of a finished layer in the phase of polymerization. To this purpose we used genetic programming that enabled us to acquire a formula for scale factor's determination based upon the geometry of the actual part. The method resulted in optimized scale factors and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays.", } @Article{Brajlih:2006:AMME, author = "Tomaz Brajlih and Igor Drstvensek and Miha Kovacic and Joze Balic", title = "Optimizing scale factors of the PolyJet rapid prototyping procedure by genetic programming", journal = "Journal of achievements in materials and manufacturing engineering", year = "2006", volume = "16", number = "1-2", pages = "101--106", month = may # "-" # jun, note = "Special Issue of CAM3S'2005", keywords = "genetic algorithms, genetic programming, rapid prototyping, PolyJet", ISSN = "1734-8412", URL = "http://jamme.acmsse.h2.pl/index.php?id=69", URL = "http://157.158.19.167/papers_cams05/167.pdf", URL = "http://www.journalamme.org/papers_cams05/167.pdf", size = "6 pages", abstract = "The main problem of assuring a high dimensional accuracy of rapid prototyping procedures, that are using polymers as a building material, is shrinking of a finished layer during the phase of polymerisation. Therefore, the finished object is slightly smaller then the object's CAD three-dimensional model, that was used to build the prototype. Commonly used method to minimise this problem is to scale (enlarge) the original CAD model in order to compensate for the material's shrinkage during manufacturing. The scaling is usually done by the number factor (in percentages) that is recommended by the rapid prototyping machine's manufacturer. With a long-term use of the certain rapid prototyping machine the end-users can determine their own scale factor's values, which are more suited to their model's properties. This research has established a method that enables the user of a PolyJet RP machine to determine the optimal scale factor regardless of his previous experience. For that purpose the genetic programming methods were used to establish a mathematical model that enables the user to calculate optimal scale factor values for each axis (X,Y,Z) regarding a certain object's properties. This method was later tested on a series of prototypes that were scaled with factor values acquired with the established mathematical model.", notes = "http://www.journalamme.org/ broken 2022 http://157.158.19.167/index.php?id=69 Formerly Proceedings of Achievements in Mechanical and Materials Engineering. Faculty of mechanical engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia *Corresponding author. E-mail address: brajlih@yahoo.com [COBISS.SI-ID 10526486]", } @TechReport{oai:CiteSeerPSU:323834, title = "{SYSGP} -- {A} {C}++ library of different {GP} variants", author = "Markus Brameier and Wolfgang Kantschik and Peter Dittrich and Wolfgang Banzhaf", institution = "Collaborative Research Center 531, University of Dortmund", year = "1998", type = "Technical Report", number = "CI-98/48", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5345/2/ci4898_doc.pdf", URL = "http://citeseer.ist.psu.edu/323834.html", citeseer-isreferencedby = "oai:CiteSeerPSU:39667", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:323834", rights = "unrestricted", abstract = "In recent years different variants of genetic programming (GP) have emerged all following the basic idea of GP, the automatic evolution of computer programs. Today, three basic forms of representation for genetic programs are used, namely tree, graph and linear structures. We introduce a multi-representation system, SYSGP, that allows researchers to experiment with different representations with only a minimum implementation overhead. The system further offers the possibility to combine modules of different representation forms into one genetic program. SYSGP has been implemented as a C++ library using templates that operate with a generic data type.", size = "13 pages", } @InProceedings{brameier:1999:PMCGP, author = "Markus Brameier and Frank Hoffmann and Peter Nordin and Wolfgang Banzhaf and Frank Francone", title = "Parallel Machine Code Genetic Programming", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "2", pages = "1228", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, poster papers", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-439.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-439.ps", size = "1 page", abstract = "AIMGP is a very fast linear genetic programming approach that evolves machine code programs. We report on a parallelization of AIMGP for a parallel transputer system resulting in an almost linear speedup.", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @TechReport{oai:CiteSeerPSU:488546, title = "Effective Linear Genetic Programming", author = "Markus Brameier and Wolfgang Banzhaf", citeseer-isreferencedby = "oai:CiteSeerPSU:75403", citeseer-references = "oai:CiteSeerPSU:46478; oai:CiteSeerPSU:271953; oai:CiteSeerPSU:349518", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:488546", rights = "unrestricted", institution = "Department of Computer Science, University of Dortmund", year = "2001", number = "Reihe CI 108/01, SFB 531", address = "44221 Dortmund, Germany", month = "29 " # oct, keywords = "genetic algorithms, genetic programming", broken = "http://sfbci.uni-dortmund.de/index.php?option=com_wrapper&Itemid=180&lang=en", URL = "http://hdl.handle.net/2003/5407", URL = "http://sfbci.uni-dortmund.de/Publications/Reference/Downloads/BB09052001.pdf", URL = "http://citeseer.ist.psu.edu/488546.html", DOI = "doi:10.17877/DE290R-15250", size = "15 pages", abstract = "Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for creating code efficient solutions are presented.", } @TechReport{oai:CiteSeerPSU:324837, author = "Markus Brameier and Wolfgang Banzhaf", title = "A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis", institution = "Dortmund University", year = "1998", type = "Reihe", number = "CI 43/98, SFB 531", address = "Germany", keywords = "genetic algorithms, genetic programming", URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5344/2/ci4398_doc.pdf", URL = "http://citeseer.ist.psu.edu/324837.html", citeseer-isreferencedby = "oai:CiteSeerPSU:39821", citeseer-references = "oai:CiteSeerPSU:212034; oai:CiteSeerPSU:186821", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:324837", rights = "unrestricted", abstract = "We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks and argue that genetic programming is able to show similar performances in classification and generalization even when using a relatively small number of generations. Finally, an efficient algorithm for the elimination of introns in linear genetic programs is presented", size = "pages", } @Article{Brameier:2001:TEC, author = "Markus Brameier and Wolfgang Banzhaf", title = "A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining", journal = "IEEE Transactions on Evolutionary Computation", year = "2001", volume = "5", number = "1", pages = "17--26", month = feb, keywords = "genetic algorithms, genetic programming, Data mining, evolutionary computation, neural networks", URL = "http://web.cs.mun.ca/~banzhaf/papers/ieee_taec.pdf", size = "10 pages", abstract = "We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.", notes = "proben1/UCI LGP variable length string of C instruction. Branching. steady state tournament selection. two-point string crossover {"}high mutation rates have been experienced to produced better results{"} p19. Size<=256 {"}it is much easier for the GP system to implement structural introns [than semantic ones]{"} p20 {"}for all problems discussed, the performance of GP in generalization comes close to or even better then the results documented for NNs{"} (MLP, RPROP) p21 Ten demes of 500 connected in one direction circle. 5% mutation rate. {"}On average, the number of effective generations is reduced by a factor of three when using demes. Tests with and without conditionals. Runtime comparison. Intron removal (dead code) at run time.", } @Article{brameier:2001:GPEM, author = "Markus Brameier and Wolfgang Banzhaf", title = "Evolving Teams of Predictors with Linear Genetic Programming", journal = "Genetic Programming and Evolvable Machines", year = "2001", volume = "2", number = "4", pages = "381--407", month = dec, keywords = "genetic algorithms, genetic programming, evolution of teams, combination of multiple predictors, linear genetic programming", ISSN = "1389-2576", URL = "http://web.cs.mun.ca/~banzhaf/papers/teams.pdf", URL = "http://citeseer.ist.psu.edu/508652.html", URL = "http://citeseer.ist.psu.edu/411995.html", DOI = "doi:10.1023/A:1012978805372", size = "26 pages", abstract = "This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a real numbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP. In particular, the processing time of linear genetic programs is reduced significantly by removing intron code before program execution.", notes = "Article ID: 386363", } @TechReport{oai:CiteSeerPSU:552561, title = "Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming", author = "Markus Brameier and Wolfgang Banzhaf", year = "2002", month = feb # "~25", citeseer-isreferencedby = "oai:CiteSeerPSU:92442; oai:CiteSeerPSU:192628", citeseer-references = "oai:CiteSeerPSU:266665; oai:CiteSeerPSU:271953; oai:CiteSeerPSU:270103; oai:CiteSeerPSU:61421; oai:CiteSeerPSU:440305; oai:CiteSeerPSU:32228; oai:CiteSeerPSU:212034; oai:CiteSeerPSU:61877", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", language = "ENG", oai = "oai:eldorado:0x0004162d", oai = "oai:CiteSeerPSU:552561", rights = "unrestricted", URL = "http://eldorado.uni-dortmund.de/0x81d98002_0x0004162d", URL = "http://eldorado.uni-dortmund.de:8080/bitstream/2003/5419/1/123.pdf", URL = "http://citeseer.ist.psu.edu/552561.html", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.9067.pdf", institution = "Dortmund University", keywords = "genetic algorithms, genetic programming", abstract = "We investigate structural and semantic distance metrics for linear genetic programs. Causal connections between changes of the genotype and fitness changes form a necessary condition for analyzing structural differences between genetic programs and for the two major objectives of this paper: (i) Distance information betweenin-dividuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators - including an effective variant of the mutation operator that works closely with the used distance metric. Numerous experiments have been performed for a regression problem, a classification task, and a Boolean problem", notes = "see also \cite{brameier:2002:EuroGP} 123.pdf crashes SUSE 10.0 KDE Konqueror 3.4.2b, Nov 2006", size = "25 pages", } @InProceedings{brameier:2002:EuroGP, title = "Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming", author = "Markus Brameier and Wolfgang Banzhaf", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", publisher = "Springer-Verlag", volume = "2278", series = "LNCS", pages = "37--49", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://www.cs.mun.ca/~banzhaf/papers/eurogp02_dist.pdf", DOI = "doi:10.1007/3-540-45984-7_4", abstract = "We have investigated structural distance metrics for linear genetic programs. Causal connections between changes of the genotype and changes of the phenotype form a necessary condition for analyzing structural differences between genetic programs and for the two objectives of this paper: (i) The distance information between individuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators - including a mutation operator that works closely with the applied distance measure. Numerous experiments have been performed for three benchmark problems.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Best paper See also \cite{oai:CiteSeerPSU:552561}", } @InProceedings{brameier03, author = "Markus Brameier and Wolfgang Banzhaf", title = "Neutral Variations Cause Bloat in Linear GP", booktitle = "Genetic Programming, Proceedings of EuroGP'2003", year = "2003", editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa", volume = "2610", series = "LNCS", pages = "286--296", address = "Essex", publisher_address = "Berlin", month = "14-16 " # apr, organisation = "EvoNet", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming: Poster", ISBN = "3-540-00971-X", DOI = "doi:10.1007/3-540-36599-0_26", abstract = "In this contribution we investigate the influence of different variation effects on the growth of code. A mutation-based variant of linear GP is applied that operates with minimum structural step sizes. Results show that neutral variations are a direct cause for (and not only a result of) the emergence and the growth of intron code. The influence of non-neutral variations has been found to be considerably smaller. Neutral variations turned out to be beneficial by solving two classification problems more successfully.", notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003 Section 2.3 PerlGP 'In PerlGP \cite{maccallum03}, evolved code is expanded from a tree-based genotype into a string before being evaluated with Perl's eval() function. The trees of each individual are built (and later, mutated) according to a grammar and are strongly typed. In this application, we want the evolved code to look like the example given in Figure 3; that is to say, the solution should be some arithmetic expression containing constants and RE matches against a protein sequence. The matches() function feeds the number of separate RE matches into the arithmetic expression. If the result of the expression for a given sequence is greater than zero, it is predicted/classified as nuclear, otherwise it is non-nuclear.' ", } @PhdThesis{B2005OLGP, title = "On Linear Genetic Programming", author = "Markus Brameier", month = feb, year = "2004", school = "Fachbereich Informatik, Universit{\"a}t Dortmund", address = "Germany", keywords = "genetic algorithms, genetic programming, Evolutionary algorithms, Machine learning", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/20098/2/Brameierunt.pdf", URL = "https://eldorado.uni-dortmund.de/bitstream/2003/20098/1/Brameier.ps", DOI = "doi:10.17877/DE290R-253", size = "272 pages", abstract = "The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more common tree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand. The two major objectives of this work comprise(1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others. First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs.In doing so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP. Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations achieve the best performance in terms of solution quality.These mutations operate only on the (structurally) effective code and restrict the mutation step size to one instruction. One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient alternative we explicitly control the mutation step size on the effective code (effective step size).Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may be connected to or disconnected from the effective graph component. It is an interesting phenomenon that, to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that there are some serious arguments for using a linear representation.In a crossover-based comparison LGP has been found superior to TGP over a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffective code. The phenomenon of code growth is analysed for different linear genetic operators. When applying instruction mutations exclusively almost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear genetic programs may not grow if all neutral variation effects are rejected and if the variation step size is minimum.For the same reasons effective instruction mutations realize an implicit complexity control in linear GP which reduces a possible negative effect of code growth to a minimum.Another noteworthy result in this context is that program size is strongly increased by crossover while it is hardly influenced by mutation even if step sizes are not explicitly restricted. Finally, we investigate program teams as one possibility to increase the dimension of genetic programs. It will be demonstrated that much more powerful solutions may be found by teams than by individuals. Moreover, the complexity of team solutions remains surprisingly small compared to individual programs. Both is the result of specialisation and cooperation of team members.", notes = "Day of Submission: 2003-05-28, Committee: Wolfgang Banzhaf and Martin Riedmiller and Peter Nordin. \onlineAvailableT{https://eldorado.uni-dortmund.de/handle/2003/20098}{http://hdl.handle.net/2003/20098}{2007-08-17} LS11", } @Article{oai:biomedcentral.com:1471-2105-7-16, author = "Markus Brameier and Josien Haan and Andrea Krings and Robert M MacCallum", title = "Automatic discovery of cross-family sequence features associated with protein function", publisher = "BioMed Central Ltd.", year = "2006", month = jan # "~12", journal = "BMC bioinformatics [electronic resource]", volume = "7", number = "16", keywords = "genetic algorithms, genetic programming", ISSN = "1471-2105", bibsource = "OAI-PMH server at www.biomedcentral.com", language = "en", oai = "oai:biomedcentral.com:1471-2105-7-16", rights = "Copyright 2006 Brameier et al; licensee BioMed Central Ltd.", URL = "http://www.biomedcentral.com/content/pdf/1471-2105-7-16.pdf", URL = "http://www.biomedcentral.com/1471-2105/7/16", DOI = "doi:10.1186/1471-2105-7-16", size = "16 pages", abstract = "Background Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. Results We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the {"}transcription{"} function than to the general {"}nuclear{"} function/location. Conclusion We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription.", notes = "PMID: 16409628", } @Book{Brameier:2006:book, author = "Markus Brameier and Wolfgang Banzhaf", title = "Linear Genetic Programming", publisher = "Springer", year = "2007", number = "XVI", series = "Genetic and Evolutionary Computation", keywords = "genetic algorithms, genetic programming, Step Size Control, Syntax, algorithms, code growth, diversity control, evolutionary algorithm, genetic operators, learning, machine learning, neutral variations, optimisation, programming", ISSN = "1932-0167", ISBN = "0-387-31029-0", DOI = "doi:10.1007/978-0-387-31030-5", abstract = "Table of contents Preface, About the Authors, Acknowledgments, Introduction, I Fundamental Analyses: Basic Concepts, Representation Characteristics, A Comparison with Neural Networks, II Method Design: Segment Variations, Instruction Mutations, Analysis of Control Parameters, A Comparison with Tree-Based GP, III Advanced Techniques and Phenomena: Control of Diversity and Step Size, Code Growth and Neutral Variations, Evolution of Program Teams, References, Index.", size = "Approx. 320 pages", } @Article{NucPred-bioinformatics2007, author = "Markus Brameier and Andrea Krings and Robert M. MacCallum", title = "{NucPred} Predicting nuclear localization of proteins", journal = "Bioinformatics", year = "2007", volume = "23", number = "9", pages = "1159--1160", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1093/bioinformatics/btm066", size = "2 pages", abstract = "NucPred analyses patterns in eukaryotic protein sequences and predicts if a protein spends at least some time in the nucleus or no time at all. Subcellular location of proteins represents functional information, which is important for understanding protein interactions, for the diagnosis of human diseases and for drug discovery. NucPred is a novel web tool based on regular expression matching and multiple program classifiers induced by genetic programming. A likelihood score is derived from the programs for each input sequence and each residue position. Different forms of visualisation are provided to assist the detection of nuclear localisation signals (NLSs). The NucPred server also provides access to additional sources of biological information (real and predicted) for a better validation and interpretation of results. Availability: The web interface to the NucPred tool is provided at http://www.sbc.su.se/~maccallr/nucpred. In addition, the Perl code is made freely available under the GNU Public Licence (GPL) for simple incorporation into other tools and web servers.", notes = "PMID: 17332022 [PubMed - indexed for MEDLINE] A few lines in Mutlu Dogruel, Doctor of Philosophy, January 2008 ftp://ftp.sanger.ac.uk/pub/resources/theses/dogruel/chapter4.pdf", } @Article{Brameier:2007:BMCbinf, author = "Markus Brameier and Carsten Wiuf", title = "Ab initio identification of human {microRNAs} based on structure motifs", journal = "BMC Bioinformatics", year = "2007", volume = "8", pages = "478", month = "18 " # dec, keywords = "genetic algorithms, genetic programming, linear genetic programming", URL = "http://www.biomedcentral.com/content/pdf/1471-2105-8-478.pdf", DOI = "doi:10.1186/1471-2105-8-478", size = "11 pages", abstract = "BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio approaches are able to discover species-specific miRNAs without known sequence homology. RESULTS: MiRPred is a novel method for ab initio prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns. CONCLUSION: Our motif finding method is at least competitive to state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.", notes = "PMID: 18088431 [PubMed - indexed for MEDLINE]", } @Article{Bramerdorfer:2014:ieeeIE, author = "Gerd Bramerdorfer and Stephan M. Winkler and Michael Kommenda and Guenther Weidenholzer and Siegfried Silber and Gabriel Kronberger and Michael Affenzeller and Wolfgang Amrhein", title = "Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs", journal = "IEEE Transactions on Industrial Electronics", year = "2014", month = nov, volume = "61", number = "11", pages = "6454--6462", keywords = "genetic algorithms, genetic programming, brushless machine, permanent magnet, cogging torque, torque ripple, modelling, field-oriented control, symbolic regression, artificial neural network, random forests", DOI = "doi:10.1109/TIE.2014.2303785", ISSN = "0278-0046", size = "9 pages", abstract = "This article investigates the modelling of brushless permanent magnet synchronous machines (PMSMs). The focus is on deriving an automatable process for obtaining dynamic motor models that take nonlinear effects, such as saturation, into account. The modelling is based on finite element (FE) simulations for different current vectors in the dq plane over a full electrical period. The parameters obtained are the stator flux in terms of the direct and quadrature component and the air gap torque, both modelled as functions of the rotor angle and the current vector. The data is preprocessed according to theoretical results on potential harmonics in the targets as functions of the rotor angle. A variety of modelling strategies were explored: linear regression, support vector machines, symbolic regression using genetic programming, random forests, and artificial neural networks. The motor models were optimised for each training technique and their accuracy was then compared on the basis of the initially available FE data and further FE simulations for additional current vectors. Artificial neural networks and symbolic regression using genetic programming achieved the highest accuracy especially with additional test data.", notes = "Also known as \cite{6729026}", } @InProceedings{Bramerdorfer:2014:IECON, author = "Gerd Bramerdorfer and Wolfgang Amrhein and Stephan M. Winkler and Michael Affenzeller", booktitle = "40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014", title = "Identification of a nonlinear PMSM model using symbolic regression and its application to current optimization scenarios", year = "2014", month = oct, pages = "628--633", abstract = "This article presents the nonlinear modelling of the torque of brushless PMSMs by using symbolic regression. It is still popular to characterise the operational behaviour of electrical machines by employing linear models. However, nowadays most PMSMs are highly used and thus a linear motor model does not give an adequate accuracy for subsequently derived analyses, e.g., for the calculation of the maximum torque per ampere (MTPA) trajectory. This article focuses on modelling PMSMs by nonlinear white-box models derived by symbolic regression methods. An optimised algebraic equation for modelling the machine behaviour is derived using genetic programming. By using a Fourier series representation of the motor torque a simple to handle model with high accuracy can be derived. A case study is provided for a given motor design and the motor model obtained is used for deriving the MTPA-trajectory for sinusoidal phase currents. The model is further applied for determining optimised phase current waveforms ensuring zero torque ripple.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/IECON.2014.7048566", notes = "Also known as \cite{7048566}", } @InProceedings{Brandejsky:2012:ICCC, author = "Thomas Brandejsky", title = "Nonlinear system identification by GPA-ES", booktitle = "13th International Carpathian Control Conference (ICCC 2012)", year = "2012", month = may, pages = "58--62", size = "5 pages", abstract = "The paper discusses application of Genetic Programming Algorithm - Evolutionary Strategy (GPA-ES) algorithm to symbolic regression of chaotic systems. In the paper, Van der Pol oscillator and especially Rabinovich-Fabrikant equations are analysed and regressed. On the base of these experiments, novel improvements of GPA-ES algorithm are suggested.", keywords = "genetic algorithms, genetic programming, GPA-ES algorithm, Rabinovich-Fabrikant equations, Van der Pol oscillator, chaotic systems, genetic programming algorithm-evolutionary strategy algorithm, nonlinear system identification, regression analysis, symbolic regression, chaos, identification, nonlinear systems, oscillators, regression analysis", DOI = "doi:10.1109/CarpathianCC.2012.6228616", notes = "Also known as \cite{6228616}", } @Article{Brandejsky:2013:CMA, author = "Tomas Brandejsky", title = "Specific modification of a GPA-ES evolutionary system suitable for deterministic chaos regression", journal = "Computer \& Mathematics with Applications", year = "2013", volume = "66", number = "2", pages = "106--112", note = "Nostradamus 2012", ISSN = "0898-1221", DOI = "doi:10.1016/j.camwa.2013.01.011", URL = "http://www.sciencedirect.com/science/article/pii/S089812211300028X", keywords = "genetic algorithms, genetic programming, Evolutionary strategy, Optimisation, Symbolic regression, Deterministic chaos", abstract = "The paper deals with symbolic regression of deterministic chaos systems using a GPA-ES system. A Lorenz attractor, Roessler attractor, Rabinovich-Fabrikant equations and a van der Pol oscillator are used as examples of deterministic chaos systems to demonstrate significant differences in the efficiency of the symbolic regression of systems described by equations of similar complexity. Within the paper, the source of this behaviour is identified in presence of structures which are hard to be discovered during the evolutionary process due to the low probability of their occurrence in the initial population and by the low chance to produce them by standard evolutionary operators given by small probability to form them in a single step and low fitness function magnitudes of inter-steps when GPA tries to form them in more steps. This low magnitude of fitness function for particular solutions tends to eliminate them, thus increasing the number of needed evolutionary steps. As the solution of identified problems, modification of terminals and related crossover and mutation operators are suggested.", } @InCollection{Brandejsky:2013:HBO, author = "Tomas Brandejsky", title = "The Use of Local Models Optimized by Genetic Programming Algorithms in Biomedical-Signal Analysis", booktitle = "Handbook of Optimization", publisher = "Springer", year = "2013", editor = "Ivan Zelinka and Vaclav Snasel and Ajith Abraham", volume = "38", series = "Intelligent Systems Reference Library", chapter = "28", pages = "697--716", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-30503-0", URL = "http://dx.doi.org/10.1007/978-3-642-30504-7_28", DOI = "doi:10.1007/978-3-642-30504-7_28", bibdate = "2013-09-25", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/isrl/isrl38.html#Brandejsky13", URL = "http://dx.doi.org/10.1007/978-3-642-30504-7", abstract = "Today researchers need to solve vague defined problems working with huge data sets describing signals close to chaotic ones. Common feature of such signals is missing algebraic model explaining their nature. Genetic Algorithms and Evolutionary Strategies are suitable to optimise such models and Genetic Programming Algorithms to develop them. Hierarchical GPA-ES algorithm presented herein is used to build compact models of difficult signals including signals representing deterministic chaos. Efficiency of GPA-ES is presented in the paper. Specific group of non-linearly composed functions similar to real biomedical signals is studied in the paper. On the base of these prerequisites, models applicable in complex biomedical signals like EEG modelling are formed and studied within the contribution.", notes = "CTU in Prague,", } @InProceedings{Brandejsky:2018:ICCAIRO, author = "Tomas Brandejsky", booktitle = "2018 International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO)", title = "Influence of Two Different Crossover Operators Use Onto {GPA} Efficiency", year = "2018", pages = "127--132", abstract = "Increasing capabilities of today computers, especially size of memory and computational power open new application areas to Genetic Programming Algorithms [1]. Unfortunately, efficiency of these algorithms is not big and decreases with solved problem complexity. Thus, its increase is extremely important for opening of new application domains. There exists three main areas that should potentially influence GPA efficiency. They are algorithms, pseudo-random number generator behaviours and evolutionary operators. Genetic programming algorithms use two basic evolutionary operators - mutation and crossover in the sense of Darwinian evolution. Non-looking to the fact, that it is possible to define additional operators like e.g. application defined operators [2], there are many different implementations of both basic evolutionary operators [3] and each of them is sometimes useful in artificial evolutionary process. Thus, the main question solved in this paper is that it might bring some advance to use two randomly executed different crossover operators in GPA. The study is focused to symbolic regression problem and as GPA is used GPA-ES, because it is capable to eliminate influence of solution parameters (constants) identification and thus to produce more clear results.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCAIRO.2018.00029", month = may, notes = "Also known as \cite{8698424}", } @InProceedings{brandejsky:2019:CSMMMIS, author = "Tomas Brandejsky", title = "Floating Data Window Movement Influence to Genetic Programming Algorithm Efficiency", booktitle = "Computational Statistics and Mathematical Modeling Methods in Intelligent Systems", year = "2019", publisher = "Springer", keywords = "genetic algorithms, genetic programming", URL = "http://link.springer.com/chapter/10.1007/978-3-030-31362-3_4", DOI = "doi:10.1007/978-3-030-31362-3_4", } @InProceedings{Brandstetter:2012:CIG, author = "Matthias F. Brandstetter and Samad Ahmadi", booktitle = "Computational Intelligence and Games (CIG), 2012 IEEE Conference on", title = "Reactive control of {Ms. Pac Man} using Information Retrieval based on Genetic Programming", year = "2012", pages = "250--256", DOI = "doi:10.1109/CIG.2012.6374163", abstract = "During the last years the well-known Ms. Pac Man video game has been - and still is - an interesting test bed for the research on various concepts from the broad area of Artificial Intelligence (AI). Among these concepts is the use of Genetic Programming (GP) to control the game from a human player's perspective. Several GP-based approaches have been examined so far, where traditionally they define two types of GP terminals: one type for information retrieval, the second type for issuing actions (commands) to the game world. However, by using these action terminals the controller has to manage actions issued to the game during their runtime and to monitor their outcome. In order to avoid the need for active task management this paper presents a novel approach for the design of a GP-based Ms. Pac Man controller: the proposed approach solely relies on information retrieval terminals in order to rate all possible directions of movement at every time step during a running game. Based on these rating values the controller can move the agent through the mazes of the the game world of Ms. Pac Man. With this design, which forms the main contribution of our work, we decrease the overall GP solution complexity by removing all action control management tasks from the system. It is demonstrated that by following the proposed approach such a system can successfully control an autonomous agent in a computer game environment on the level of an amateur human player.", keywords = "genetic algorithms, genetic programming, computer games, information retrieval, AI, GP solution complexity, GP-based approaches, Ms. Pac Man video game, action control management task removal, action terminals, artificial intelligence, autonomous agent control, game mazes, human player perspective, information retrieval, rating values, reactive control, task management, Abstracts, Computational intelligence, Games, Humans, Information retrieval, Sociology, Statistics", notes = "Also known as \cite{6374163}", } @InProceedings{branke:1999:RGDSSEA, author = "Jurgen Branke and Massimo Cutaia and Heinrich Dold", title = "Reducing Genetic Drift in Steady State Evolutionary Algorithms", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "68--74", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Branke:EEC:gecco2004, author = "Juergen Branke and Pablo Funes and Frederik Thiele", title = "Evolving En-Route Caching Strategies for the Internet", booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II", year = "2004", editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell", series = "Lecture Notes in Computer Science", pages = "434--446", address = "Seattle, WA, USA", publisher_address = "Heidelberg", month = "26-30 " # jun, organisation = "ISGEC", publisher = "Springer-Verlag", volume = "3103", ISBN = "3-540-22343-6", ISSN = "0302-9743", DOI = "doi:10.1007/b98645", DOI = "doi:10.1007/978-3-540-24855-2_55", size = "13", keywords = "genetic algorithms, genetic programming", abstract = "Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packetized data over multiple intermediate relays. As hubs in the network become overladen, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [18]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods.", notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)", affiliation = "Institute AIFB, University of Karlsruhe, 76128 Karlsruhe Germany", } @Article{Branke:2006:ASC, author = "Jurgen Branke and Pablo Funes and Frederik Thiele", title = "Evolutionary design of en-route caching strategies", journal = "Applied Soft Computing", year = "2006", volume = "7", number = "3", pages = "890--898", month = jun, keywords = "genetic algorithms, genetic programming, En-route caching, Robustness", DOI = "doi:10.1016/j.asoc.2006.04.003", size = "9 pages", abstract = "Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packeted data over multiple intermediate relays. As hubs in the network become over used, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [X. Tang, S.T. Chanson, Coordinated en-route web caching, IEEE Transact. Comput. 51 6 (2002) 595-607]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods.", notes = "cites \cite{Paterson:1997:ecacGP}, \cite{oneill:1999:AGCA}, \cite{Branke:EEC:gecco2004}. Caching Internet documents. Caching as deletion strategy. GP evolves priority (of cached www documents) strategy removes low priority doc from cache until space is available for new doc. Pop 60, 100 gens. Network simulation on cluster of 6 linux workstations. Linear network, GP almost optimal. Appealing GP solution RUDF Priority = last time accessed*(distance+access count). p896 GPfinal+RUDF 'significantly outperformed all the other strategies tested, with a slight advantage of RUDF over GPfinal'.", } @Proceedings{Branke:2010:GECCO, title = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation", year = "2010", editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt", address = "Portland, OR, USA", publisher_address = "New York, NY, USA", month = jul # " 07-11", organisation = "ACM SIGEVO", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Ant Colony Optimisation and Swarm Intelligence, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, Bioinformatics and Computational Biology, Combinatorial Optimisation and Metaheuristics, Estimation of Distribution Algorithms, Evolution Strategies and Evolutionary Programming, Evolutionary Multiobjective, Optimisation, Generative and Developmental Systems, Genetics-Based Machine Learning, Parallel Evolutionary Systems, Real World Application, Search Based Software Engineering, Theory", isbn13 = "978-1-4503-0072-8", URL = "http://portal.acm.org/citation.cfm?id=1830483&coll=DL&dl=ACM&CFID=12039329&CFTOKEN=58660565", abstract = "These proceedings contain the papers presented at the 12th Annual Genetic and Evolutionary Computation Conference (GECCO-2010), held in Portland, USA, July 7-11, 2010. This year, we received 373 submissions, of which 168 were accepted as full eight-page publication with 25 minute presentation during the conference. This corresponds to an acceptance rate of 45percent. In addition, 110 submissions (29percent) have been accepted for poster presentation with two-page abstracts in the proceedings. GECCO works according to the motto one conference, many mini-conferences. This year, there were 15 separate tracks that operated independently from each other. Each track had its own track chair(s) and individual program committee. To ensure an unbiased reviewing process, all reviews were conducted double blind; no authors' names were revealed to the reviewers. About 560 researchers participated in the reviewing process. We want to thank them for all their work, which is highly appreciated and absolutely vital to ensure the high quality of the conference. In addition to the presentation of the papers contained in these proceedings, GECCO-2010 also included free tutorials, workshops, a series of sessions on Evolutionary Computation in Practice, various competitions, and late-breaking papers.", notes = "GECCO-2019 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010). ACM Order Number 910102.", } @Article{Branke:2015:EC, author = "Juergen Branke and Torsten Hildebrandt and Bernd Scholz-Reiter", title = "Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations", journal = "Evolutionary Computation", year = "2015", volume = "23", number = "2", pages = "249--277", month = "Summer", keywords = "genetic algorithms, genetic programming, Job Shop Scheduling, Dispatching Rule, Representation, CMA-ES, Artificial Neural Network", ISSN = "1063-6560", DOI = "doi:10.1162/EVCO_a_00131", size = "29 pages", abstract = "Dispatching rules are frequently used for real-time, on-line scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighbourhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on Artificial Neural Networks, and a tree representation. Using appropriate Evolutionary Algorithms (CMA-ES for the Neural Network and linear representations, Genetic Programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualise what the rules do in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of Genetic Programming gives the best results if many candidate rules can be evaluated, closely followed by the Neural Network representation that leads to good results already for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.", notes = "Warwick Business School, University of Warwick, CV4 7AL Coventry, UK", } @Article{Branke:2015:ieeeTEC, author = "Juergen Branke and Su Nguyen and Christoph Pickardt and Mengjie Zhang", title = "Automated Design of Production Scheduling Heuristics: A Review", journal = "IEEE Transactions on Evolutionary Computation", year = "2016", volume = "20", number = "1", pages = "110--124", month = feb, keywords = "genetic algorithms, genetic programming, Evolutionary design, hyper-heuristic, scheduling.", ISSN = "1089-778X", URL = "http://wrap.warwick.ac.uk/88212/1/WRAP-automated-design-production-scheduling-heuristics-Branke-2015.pdf", URL = "http://wrap.warwick.ac.uk/88212/", DOI = "doi:10.1109/TEVC.2015.2429314", size = "15 pages", abstract = "Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyperheuristics have been developed and shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasised in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This review strives to fill this gap by summarising the state of the art, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.", notes = "This paper is a review mainly on GP methods for scheduling. Also known as \cite{7101236}", } @InProceedings{Branke:2016:WSC, author = "Juergen Branke and Matthew J. Groves and Torsten Hildebrandt", booktitle = "2016 Winter Simulation Conference (WSC)", title = "Evolving control rules for a dual-constrained job scheduling scenario", year = "2016", pages = "2568--2579", abstract = "Dispatching rules are often used for scheduling in semiconductor manufacturing due to the complexity and stochasticity of the problem. In the past, simulation-based Genetic Programming has been shown to be a powerful tool to automate the time-consuming and expensive process of designing such rules. However, the scheduling problems considered were usually only constrained by the capacity of the machines. In this paper, we extend this idea to dual-constrained flow shop scheduling, with machines and operators for loading and unloading to be scheduled simultaneously. We show empirically on a small test problem with parallel workstations, re-entrant flows and dynamic stochastic job arrival that the approach is able to generate dispatching rules that perform significantly better than benchmark rules from the literature.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/WSC.2016.7822295", month = dec, notes = "Also known as \cite{7822295}", } @InProceedings{Branke:2019:GECCOcomp, author = "Juergen Branke", title = "Simulation optimisation: tutorial", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", note = "Tutorial", isbn13 = "978-1-4503-6748-6", pages = "862--889", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3323385", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3323385} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{branquinho:2023:NEWK, author = "Henrique Branquinho and Nuno Lourenco and Ernesto Costa", title = "{SPENSER:} Towards a {NeuroEvolutionary} Approach for Convolutional Spiking Neural Networks", booktitle = "Neuroevolution at work", year = "2023", editor = "Ernesto Tarantino and Edgar Galvan and Ivanoe {De Falco} and Antonio {Della Cioppa} and Scafuri Umberto and Mengjie Zhang", pages = "2115--2122", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, grammatical evolution, neuroevolution, computer vision, spiking neural networks, DENSER", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3596399", size = "8 pages", abstract = "Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. However, the performance of SNNs still lags behind traditional Artificial Neural Networks (ANNs), as there is no consensus on the best learning algorithm for SNNs. Best-performing SNNs are based on ANN to SNN conversion or learning with spike-based backpropagation through surrogate gradients. The focus of recent research has been on developing and testing different learning strategies, with hand-tailored architectures and parameter tuning. Neuroevolution (NE), has proven successful as a way to automatically design ANNs and tune parameters, but its applications to SNNs are still at an early stage. DENSER is a NE framework for the automatic design and parametrization of ANNs, based on the principles of Genetic Algorithms (GA) and Structured Grammatical Evolution (SGE). In this paper, we propose SPENSER, a NE framework for SNN generation based on DENSER, for image classification on the MNIST and Fashion-MNIST datasets. SPENSER generates competitive performing networks with a test accuracy of 99.42\% and 91.65\% respectively.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{Brar:2007:WCE, author = "Gursewak S Brar and Yadwinder S Brar and Yaduvir Singh", title = "A Fuzzy Entropy Algorithm For Data Extrapolation In Multi-Compressor System", booktitle = "Proceedings of the World Congress on Engineering, WCE 2007", year = "2007", volume = "I", pages = "105--110", address = "London", month = jul # " 2-4", keywords = "genetic algorithms, genetic programming, fuzzy entropy, incomplete data, classification, knowledge discovery, multi-compressor system", isbn13 = "978-988-98671-5-7", URL = "http://www.iaeng.org/publication/WCE2007/WCE2007_pp105-110.pdf", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2111", bibsource = "OAI-PMH server at citeseerx.ist.psu.edu", contributor = "CiteSeerX", language = "en", oai = "oai:CiteSeerXPSU:10.1.1.149.2111", abstract = "In this paper incomplete quantitative data has been dealt by using the concept of fuzzy entropy. Fuzzy entropy has been used to extrapolate the data pertaining to the compressor current. Certain attributes related to the compressor current have been considered. Test data of compressor current used in this knowledge discovery algorithm knows the entire attribute clearly. The developed algorithm is very effective and can be used in the various application related to knowledge discovery and machine learning. The developed knowledge discovery algorithm using fuzzy entropy has been tested on a multi-compressor system for incomplete compressor current data and it is found that the error level is merely 4.40percent, which is far better than other available knowledge discovery algorithms", } @Article{DBLP:journals/jifs/BrasSW21, author = "Glender Bras and Alisson {Marques Silva} and Elizabeth Fialho Wanner", title = "Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks", journal = "J. Intell. Fuzzy Syst.", volume = "41", number = "1", pages = "499--516", year = "2021", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.3233/JIFS-202146", DOI = "doi:10.3233/JIFS-202146", timestamp = "Fri, 10 Sep 2021 01:00:00 +0200", biburl = "https://dblp.org/rec/journals/jifs/BrasSW21.bib", bibsource = "dblp computer science bibliography, https://dblp.org", } @Article{Bras:2024:GPEM, author = "Glender Bras and Alisson {Marques Silva} and Elizabeth F. Wanner", title = "A genetic algorithm for rule extraction in fuzzy adaptive learning control networks", journal = "Genetic Programming and Evolvable Machines", year = "2024", volume = "25", pages = "Article no 11", note = "Online first", keywords = "genetic algorithms, Fuzzy systems, Rule extraction, Forecasting", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-024-09486-2", abstract = "Falcon-GA, for rule extraction in a Fuzzy Adaptive Learning Control Network (FALCON) using a Genetic Algorithm (GA). The FALCON-GA combines multiple techniques to establish the relationships and connections among fuzzy rules, including the use of a GA for rule extraction and a Gradient-based method for fine-tuning the membership function parameters. The learning algorithm of FALCON-GA incorporates three key components: the ART (Adaptive Resonance Theory) clustering algorithm for initial membership function identification, the Genetic Algorithm for rule extraction, and the Gradient method for adjusting membership function parameters. Moreover, FALCON-GA offers flexibility by allowing the incorporation of different rule types within the FALCON architecture, making it flexible and expansible. The proposed model has been evaluated in various forecasting problems reported in the literature and compared to alternative models. Computational experiments demonstrate the effectiveness of FALCON-GA in forecasting tasks and reveal significant performance improvements compared to the original FALCON. These results indicate that Genetic Algorithms efficiently extract rules for Fuzzy Adaptive Learning Control Networks.", notes = "Is this GP? Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Av. Amazonas, 7675, Belo Horizonte, MG, 30510-000, Brazil", } @Article{BRAUNE:2022:IJPE, author = "Roland Braune and Frank Benda and Karl F. Doerner and Richard F. Hartl", title = "A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems", journal = "International Journal of Production Economics", volume = "243", pages = "108342", year = "2022", ISSN = "0925-5273", DOI = "doi:10.1016/j.ijpe.2021.108342", URL = "https://www.sciencedirect.com/science/article/pii/S0925527321003182", keywords = "genetic algorithms, genetic programming, Flexible shop scheduling, Machine learning, Iterative dispatching rule, Multi-tree representation", abstract = "This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP. These priority rules were tested on benchmark problem settings, such as those of Brandimarte and Lawrence, in a static and dynamic environment. The GP approaches were then applied to a special case based on the problem setting of an industrial partner. The goal of these approaches was to minimize the maximum completion time of all jobs, which is known as the makespan. To reach this goal, we considered job assignment and machine sequencing decisions simultaneously in a single-tree representation and compared this single tree with a multi-tree approach, where the terminal sets (job- and machine-related) were strictly separated. This resulted in two parallel GP populations; they were used to first decide which job to choose and then which machine it should be assigned to. Furthermore, for both tree approaches, we implemented an iterative variant that stores recorded information of past schedules to achieve further improvements. Computational experiments revealed a consistent advantage compared to the existing advanced priority rules from the literature with considerably increased performance under the presence of unrelated parallel machines and larger instances in general", } @InCollection{brave:1994:recursive, author = "Scott Brave", title = "Evolution of Planning: Using recursive techniques in Genetic Planning", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "1--10", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @InProceedings{brave:1994:recursiveGW, author = "Scott Brave", title = "Using Genetic Programming to Evolve Recursive Programs for Tree Search", booktitle = "Fourth Golden West Conference on Intelligent Systems", year = "1995", editor = "Sushil J. Louis", pages = "60--65", publisher_address = "San Francisco, California, USA", month = "12-14 " # jun, publisher = "International Society for Computers and their Applications - ISCA", email = "isca@ipass.net", keywords = "genetic algorithms, genetic programming", ISBN = "1-880843-12-9", notes = "GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm", } @InProceedings{brave:1994:mmGW, author = "Scott Brave", title = "Using Genetic Programming to Evolve Mental Models", booktitle = "Fourth Golden West Conference on Intelligent Systems", year = "1995", editor = "Sushil J. Louis", pages = "91--96", publisher_address = "San Francisco, California, USA", month = "12-14 " # jun, publisher = "International Society for Computers and their Applications - ISCA", email = "isca@ipass.net", keywords = "genetic algorithms, genetic programming, memory", ISBN = "1-880843-12-9", notes = "GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm", } @InCollection{brave:1996:aigp2, author = "Scott Brave", title = "Evolving Recursive Programs for Tree Search", booktitle = "Advances in Genetic Programming 2", publisher = "MIT Press", year = "1996", editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}", pages = "203--220", chapter = "10", address = "Cambridge, MA, USA", keywords = "genetic algorithms, genetic programming", ISBN = "0-262-01158-1", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.3005", URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.3005&rep=rep1&type=pdf", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277538", DOI = "doi:10.7551/mitpress/1109.003.0015", abstract = "This article compares basic genetic programming, genetic programming with automatically defined functions (ADFs), and genetic programming with ADFs using a restricted form of recursion on a planning problem involving tree search. The results show that evolution of a recursive program is possible and further that, of the three techniques explored, genetic programming with recursive ADFs performs the best for the tree search problem. Additionally, genetic programming using ADFs (recursive and non-recursive) outperforms genetic programming without ADFs. The scalability of these techniques is also investigated. The computational effort required to reach a solution using ADFs with recursion is shown to remain essentially constant with world size, while genetic programming with non-recursive ADFs scales linearly at best, and basic genetic programming scales exponentially. Finally, many solutions were found using genetic programming with recursive ADFs which generalised to any world size.", notes = "Recursive ADFs, non-recursive ADFs and non-ADF GP compared on a tree searching problem. Tree depths 2-7 (ie up to 127 leaf nodes) containing one goal node. Problem arranged so can only be solved (by luck?) or by using memory. READ+WRITE update a single memory cell per tree node, ie no index, just access current cell. WRITE not as Teller but returns its argument. ADF1 and ADF2 syntax set up so one can search tree and one can move within it, cf. Andre. Recursive ADFs much better than ADFs much better than non-ADFs, difference increase as tree size increases. {"}random{"}? program search can find recursive ADF programs which solve problem. DGPC", size = "17 pages", } @InProceedings{brave:1996:dface, author = "Scott Brave", title = "Evolving Deterministic Finite Automata Using Cellular Encoding", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming", pages = "39--44", address = "Stanford University, CA, USA", publisher = "MIT Press", size = "6 pages", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzautomata.pdf/brave96evolving.pdf", URL = "http://citeseer.ist.psu.edu/brave96evolving.html", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap5.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", abstract = "his paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify actions for the incremental growth of a deterministic finite automata from an initial single-state zygote. The results show that, given a test bed of positive and negative samples, the proposed method is successful at inducing automata to recognise several different languages. 1. Introduction The automatic creation of finite...", notes = "GP-96 DGPC {"}inremental growth of finite automata from an initial single-state zygote{"}, {"}Induced automata to recognise several different (formal) languages{"} eg Tomita {"}applies cellular encoding to the evolution of determistic finite (state) automata.{"}", } @InProceedings{brave:1996:emmmGP, author = "Scott Brave", title = "The Evolution of Memory and Mental Models Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, memory", pages = "261--266", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzmodels.pdf/brave96evolution.pdf", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap32.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "his paper applies genetic programming to the evolution of intelligent agents that gradually build internal representations of their surroundings for later use in planning. The method used allows for the creation of dynamically determined representations that are not pre-designed by the human creator of the system. In an illustrative path-planning problem, evolved programs learn a model of their world and use this internal representation to plan their successive actions. The results show that...", notes = "GP-96. cf. \cite{brave:1994:mmGW} ", } @Proceedings{brave:1999:gecco99lb, editor = "Scott Brave and Annie S. Wu", title = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary Programming, fuzzy rules", size = "311 pages", notes = "GECCO-99LB", } @InProceedings{Bravi:2017:evoApplications, author = "Ivan Bravi and Ahmed Khalifa and Christoffer Holmgard and Julian Togelius", title = "Evolving Game-Specific UCB Alternatives for General Video Game Playing", booktitle = "20th European Conference on the Applications of Evolutionary Computation", year = "2017", editor = "Giovanni Squillero", series = "LNCS", volume = "10199", publisher = "Springer", pages = "393--406", address = "Amsterdam", month = "19-21 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, General AI, MTCS, Monte-Carlo Tree Search", DOI = "doi:10.1007/978-3-319-55849-3_26", abstract = "At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behaviour of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general.", notes = "Dipartimento di Elettronica, Informatica e BioingegneriaPolitecnico di MilanoMilanoItaly EvoApplications2017 held in conjunction with EuroGP'2017, EvoCOP2017 and EvoMusArt2017 http://www.evostar.org/2017/cfp_evoapps.php.", } @InProceedings{DBLP:conf/ideal/BrazierRW04, author = "Karl J. Brazier and Graeme Richards and Wenjia Wang", title = "Implicit Fitness Sharing Speciation and Emergent Diversity in Tree Classifier Ensembles", booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings", year = "2004", pages = "333--338", editor = "Zheng Rong Yang and Richard M. Everson and Hujun Yin", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3177", ISBN = "3-540-22881-0", bibsource = "DBLP, http://dblp.uni-trier.de", address = "Exeter, UK", month = aug # " 25-27", organisation = "IEEE", keywords = "genetic algorithms, genetic programming, gene expression programming", DOI = "doi:10.1007/b99975", abstract = "Implicit fitness sharing is an approach to the stimulation of speciation in evolutionary computation for problems where the fitness of an individual is determined as its success rate over a number trials against a collection of succeed/fail tests. By fixing the reward available for each test, individuals succeeding in a particular test are caused to depress the size of one another's fitness gain and hence implicitly co-operate with those succeeding in other tests. An important class of problems of this form is that of attribute-value learning of classifiers. Here, it is recognised that the combination of diverse classifiers has the potential to enhance performance in comparison with the use of the best obtainable individual classifiers. However, proposed prescriptive measures of the diversity required have inherent limitations from which we would expect the diversity emergent from the self-organisation of speciating evolutionary simulation to be free. The approach was tested on a number of the popularly used real-world data sets and produced encouraging results in terms of accuracy and stability.", notes = "http://www.dcs.ex.ac.uk/ideal04/ a) Cleveland heart data b) Thyroid data c) Pima Indians diabetes data d) E. coli data ", } @InProceedings{Bredeche:2009:EA, author = "N. Bredeche and E. Haasdijk and A. E. Eiben", title = "On-Line, On-Board Evolution of Robot Controllers", booktitle = "9th International Conference, Evolution Artificielle, EA 2009", year = "2009", editor = "Pierre Collet and Nicolas Monmarche and Pierrick Legrand and Marc Schoenauer and Evelyne Lutton", volume = "5975", series = "Lecture Notes in Computer Science", pages = "110--121", address = "Strasbourg, France", month = oct # " 26-28", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-14155-3", URL = "http://www.cs.vu.nl/~gusz/papers/2009-bredeche09ea_final2-LNCS.pdf", DOI = "doi:10.1007/978-3-642-14156-0_10", size = "12 pages", abstract = "This paper reports on a feasibility study into the evolution of robot controllers during the actual operation of robots (on-line), using only the computational resources within the robots themselves (on-board). We identify the main challenges that these restrictions imply and propose mechanisms to handle them. The resulting algorithm is evaluated in a hybrid system, using the actual robots' processors interfaced with a simulator that represents the environment. The results show that the proposed algorithm is indeed feasible and the particular problems we encountered during this study give hints for further research.", notes = "EA'09 Published 2010", } @InProceedings{breeden:1999:UJE, author = "Joseph L. Breeden and Todd W. Allen", title = "Using an optimization toolkit for Java to evolve market strategies for European seeds", booktitle = "Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference", year = "1999", editor = "Scott Brave and Annie S. Wu", pages = "57--64", address = "Orlando, Florida, USA", month = "13 " # jul, keywords = "Genetic Algorithms", notes = "GECCO-99LB", } @InProceedings{conf/ijcci/BreenO16, author = "Aidan Breen and Colm O'Riordan", title = "Evolving Art using Aesthetic Analogies - Evolutionary Supervised Learning to Generate Art with Grammatical Evolution", booktitle = "Proceedings of the 8th International Joint Conference on Computational Intelligence, {IJCCI} 2016", year = "2016", editor = "Juan Julian Merelo Guervos and Fernando Melicio and Jose Manuel Cadenas and Antonio Dourado and Kurosh Madani and Antonio E. Ruano and Joaquim Filipe", pages = "59--68", address = "Porto, Portugal", month = nov # " 9-11", publisher = "SciTePress", keywords = "genetic algorithms, genetic programming", isbn13 = "978-989-758-201-1", bibdate = "2017-05-24", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2016ecta.html#BreenO16", DOI = "doi:10.5220/0006048400590068", notes = "Volume 1: ECTA", } @InProceedings{RibeiroZV07a, author = "Jose Carlos Ribeiro and Mario Zenha-Rela and Francisco {Fernandez de Vega}", title = "An Evolutionary Approach for Performing Structural Unit-Testing on Third-Party Object-Oriented Java Software", booktitle = "Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)", year = "2007", pages = "379--388", editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta", volume = "129", series = "Studies in Computational Intelligence", address = "Acireale, Italy", month = "8-10 " # nov, publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html", isbn13 = "978-3-540-78986-4", URL = "http://jcbribeiro.googlepages.com/NICSO2007-053.pdf", DOI = "doi:10.1007/978-3-540-78987-1_34", abstract = "Evolutionary Testing is an emerging methodology for automatically generating high quality test data. The focus of this paper is on presenting an approach for generating test cases for the unit-testing of object-oriented programs, with basis on the information provided by the structural analysis and interpretation of Java bytecode and on the dynamic execution of the instrumented test object. The rationale for working at the bytecode level is that even when the source code is unavailable, insight can still be obtained and used to guide the search-based test case generation process. Test cases are represented using the Strongly Typed Genetic Programming paradigm, which effectively mimics the polymorphic relationships, inheritance dependences and method argument constraints of object-oriented programs.", notes = "http://www.dmi.unict.it/nicso2007/ http://www.dmi.unict.it/nicso2007/NICSO2007-program.pdf", } @InProceedings{Bregieiro-Ribeiro:2008:JAEM, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "{eCrash:} a framework for performing evolutionary testing on third-party Java components", booktitle = "I Jornadas sobre Algoritmos Evolutivos y Metaheuristicas (JAEM 2007)", year = "2007", editor = "Enrique Alba and Francisco Herrera", pages = "137--144", address = "Zaragoza, Spain", month = "11-14 " # sep, keywords = "genetic algorithms, genetic programming, SBSE, STGP", isbn13 = "978-84-9732-593-6", URL = "http://jcbribeiro.googlepages.com/jribeiro_jaem07.pdf", size = "8 pages", abstract = "The focus of this paper is on presenting a tool for generating test data by employing evolutionary search techniques, with basis on the information provided by the structural analysis and interpretation of the Java bytecode of third-party Java components, and on the dynamic execution of the instrumented test object. The main objective of this approach is that of evolving a set of test cases that yields full structural code coverage of the test object. Such a test set can be used for effectively performing the testing activity, providing confidence in the quality and robustness of the test object. The rationale of working at the bytecode level is that even when the source code is unavailable structural testing requirements can still be derived, and used to assess the quality of a test set and to guide the evolutionary search towards reaching specific test goals.", notes = "http://neo.lcc.uma.es/jaem07/ With CEDI 2007", } @InProceedings{Bregieiro-Ribeiro:2008:AST, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object-oriented software", booktitle = "AST '08: Proceedings of the 3rd international workshop on Automation of software test", year = "2008", pages = "85--92", address = "Leipzig, Germany", publisher_address = "New York, NY, USA", publisher = "ACM", keywords = "genetic algorithms, genetic programming, SBSE, Search-Based Test Case Generation, Evolutionary Testing, Object-Orientation, Strongly-Typed Genetic Programming, Software Engineering, Testing and Debugging| Testing tools, Verification", isbn13 = "978-1-60558-030-2", URL = "http://jcbribeiro.googlepages.com/ast12-ribeiro.pdf", DOI = "doi:10.1145/1370042.1370061", size = "8 pages", abstract = "Evolutionary Testing is an emerging methodology for automatically producing high quality test data. The focus of our on-going work is precisely on generating test data for the structural unit-testing of object-oriented Java programs. The primary objective is that of efficiently guiding the search process towards the definition of a test set that achieves full structural coverage of the test object. However, the state problem of object-oriented programs requires specifying carefully ne-tuned methodologies that promote the traversal of problematic structures and difficult controlflow paths - which often involves the generation of complex and intricate test cases, that dene elaborate state scenarios. This paper proposes a methodology for evaluating the quality of both feasible and unfeasible test cases - i.e., those that are effectively completed and terminate with a call to the method under test, and those that abort prematurely because a runtime exception is thrown during test case execution. With our approach, unfeasible test cases are considered at certain stages of the evolutionary search, promoting diversity and enhancing the possibility of achieving full coverage.", notes = "also known as \cite{1370061}", } @InProceedings{Bregieiro-Ribeiro:2008:gecco, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Strongly-typed genetic programming and purity analysis: input domain reduction for evolutionary testing problems", booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation", year = "2008", editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener", isbn13 = "978-1-60558-130-9", pages = "1783--1784", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1783.pdf", DOI = "doi:10.1145/1389095.1389439", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, Input domain reduction, search-based test case generation, strongly-Typed genetic programming, Search-based software engineering: Poster, Testing, Debugging, Testing tools, data generators, coverage testing, stack, bitset, STGP, EMCDG, IDR", abstract = "Search-based test case generation for object-oriented software is hindered by the size of the search space, which encompasses the arguments to the implicit and explicit parameters of the test object's public methods. The performance of this type of search problems can be enhanced by the definition of adequate Input Domain Reduction strategies. The focus of our on-going work is on employing evolutionary algorithms for generating test data for the structural unit-testing of Java programs. Test cases are represented and evolved using the Strongly-Typed Genetic Programming paradigm; Purity Analysis is particularly useful in this situation because it provides a means to automatically identify and remove Function Set entries that do not contribute to the definition of interesting test scenarios. Categories and Subject Descriptors", notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008). ACM Order Number 910081. Also known as \cite{1389439}", } @InProceedings{Bregieiro-Ribeiro:2008:geccocomp, author = "Jose Carlos {Bregieiro Ribeiro}", title = "Search-based test case generation for object-oriented java software using strongly-typed genetic programming", year = "2008", editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry", isbn13 = "978-1-60558-131-6", booktitle = "GECCO-2008 Graduate Student Workshops", pages = "1819--1822", address = "Atlanta, GA, USA", URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1819.pdf", DOI = "doi:10.1145/1388969.1388979", publisher = "ACM", publisher_address = "New York, NY, USA", month = "12-16 " # jul, keywords = "genetic algorithms, genetic programming, dynect-orientation, evolutionary testing, search-based test case generation, strongly-Typed genetic programming", notes = "Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite{1388979}", } @Article{BregieiroRibeiro2009, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Test Case Evaluation and Input Domain Reduction Strategies for the Evolutionary Testing of Object-Oriented Software", journal = "Information and Software Technology", year = "2009", volume = "51", number = "11", pages = "1534--1548", month = nov, keywords = "genetic algorithms, genetic programming, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2009.06.009", URL = "http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2", size = "15 pages", abstract = "In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of object-oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and input domain reduction; the strategies proposed were empirically evaluated with encouraging results.Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space.{"}", notes = "Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008)", } @InProceedings{DBLP:conf/gecco/RibeiroRV09, author = "Jose Carlos {Bregieiro Ribeiro} and Mario {Zenha Rela} and Francisco {Fernandez de Vega}", title = "An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing", booktitle = "GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation", year = "2009", editor = "Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano {van Hemert} and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba", pages = "1949--1950", address = "Montreal", publisher = "ACM", publisher_address = "New York, NY, USA", month = "8-12 " # jul, organisation = "SigEvo", keywords = "genetic algorithms, genetic programming, Poster", isbn13 = "978-1-60558-325-9", bibsource = "DBLP, http://dblp.uni-trier.de", DOI = "doi:10.1145/1569901.1570253", abstract = "This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.", notes = "GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.", } @Article{Ribeiro20091534, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software", journal = "Information and Software Technology", volume = "51", number = "11", pages = "1534--1548", year = "2009", note = "Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008)", ISSN = "0950-5849", DOI = "doi:10.1016/j.infsof.2009.06.009", URL = "http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2", keywords = "genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction", abstract = "In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the structural unit-testing of Object-Oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and Input Domain Reduction; the strategies proposed were empirically evaluated with encouraging results. Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic structures. Purity Analysis is employed as a systematic strategy for reducing the search space.", } @InProceedings{Ribeiro:2010:EuroGP, author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}", title = "Enabling Object Reuse on Genetic Programming-based Approaches to Object-Oriented Evolutionary Testing", booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010", year = "2010", editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar", volume = "6021", series = "LNCS", pages = "220--231", address = "Istanbul", month = "7-9 " # apr, organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming, SBSE", isbn13 = "978-3-642-12147-0", DOI = "doi:10.1007/978-3-642-12148-7_19", abstract = "Recent research on search-based test data generation for Object-Oriented software has relied heavily on typed Genetic Programming for representing and evolving test data. However, standard typed Genetic Programming approaches do not allow Object Reuse; this paper proposes a novel methodology to overcome this limitation. Object Reuse means that one instance can be passed to multiple methods as an argument, or multiple times to the same method as arguments. In the context of Object-Oriented Evolutionary Testing, it enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise. Additionally, the experimental studies performed show that the proposed methodology is able to effectively increase the performance of the test data generation process.", notes = "AT-nodes P-nodes \cite{lopez:2004:eurogp}. Java Red-Black tree and vector classes. pop=25. Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010", } @PhdThesis{Bregieiro-Ribeiro:thesis, author = "Jose Carlos {Bregieiro Ribeiro}", title = "Contributions for Improving Genetic Programming-Based Approaches to the Evolutionary Testing of Object-Oriented Software", title_es = "Contribuciones a la Mejora de las Tecnicas para Test de Software Orientado a Objetos Mediante Programacion Genetica", school = "Departamento de Tecnologa de los Computadores y de las Comunicaciones, Universidad de Extremadura", year = "2010", address = "Merida, Spain", month = "12 " # nov, keywords = "genetic algorithms, genetic programming, SBSE", URL = "https://sites.google.com/site/jcbribeiro/jose.ribeiro_phdthesis_final.pdf", URL = "https://sites.google.com/site/jcbribeiro/", size = "188 pages", notes = "eCrash Examination Board: Prof. Xin Yao, Prof. Ernesto Costa, Prof. Carlos Cotta, Prof. Ignacio Hidalgo, Prof. Miguel Macias. Supervisors: Francisco Fernandez de Vega (Universidad de Extremadura, Spain) Mario Alberto Zenha-Rela (Universidade de Coimbra, Portugal)", } @InCollection{Bregieiro-Ribeiro:2015:hbgpa, author = "Jose Carlos {Bregieiro Ribeiro} and Ana Filipa Nogueira and Francisco {Fernandez de Vega} and Mario Alberto Zenha-Rela", title = "eCrash: a Genetic Programming-Based Testing Tool for Object-Oriented Software", booktitle = "Handbook of Genetic Programming Applications", publisher = "Springer", year = "2015", editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan", chapter = "23", pages = "575--593", keywords = "genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Object-Orientation, Search-Based Software Engineering, Unit Testing", isbn13 = "978-3-319-20882-4", DOI = "doi:10.1007/978-3-319-20883-1_23", abstract = "This paper describes the methodology, architecture and features of the eCrash framework, a Java-based tool which employs Strongly-Typed Genetic Programming to automate the generation of test data for the structural unit testing of Object-Oriented programs. The application of Evolutionary Algorithms to Test Data generation is often referred to as Evolutionary Testing. eCrash implements an Evolutionary Testing strategy developed with three major purposes: improving the level of performance and automation of the Software Testing process; minimising the interference of the tool's users on the Test Object analysis to a minimum; and mitigating the impact of users decisions in the Test Data generation process.", } @InProceedings{Bremer:2021:CSIS, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Towards Evolutionary Emergence", booktitle = "Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems", year = "2021", editor = "Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik Slezak", pages = "55--60", address = "Online", month = "2-5 " # sep, organisation = "Polish Information Processing Society, Polskie Towarzystwo Informatyczne, Warszawa, Poland", publisher = "FedCSIS", keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, PSO", isbn13 = "78-83-962423-0-3", ISSN = "2300-5963", URL = "https://annals-csis.org/Volume_26/pliks/position.pdf", URL = "https://annals-csis.org/proceedings/2021/drp/pdf/111.pdf", DOI = "doi:10.15439/2021F111", size = "6 pages", abstract = "With the upcoming era of large-scale, complex cyber-physical systems, also the demand for decentralized and self-organising algorithms for coordination rises. Often such algorithms rely on emergent behavior; local observations and decisions aggregate to some global behavior without any apparent, explicitly programmed rule. Systematically designing these algorithms targeted for a new orchestration or optimisation task is, at best, tedious and error prone. Suitable and widely applicable design patterns are scarce so far. We opt for a machine learning based approach that learns the necessary mechanisms for targeted emergent behavior automatically. To achieve this,we use Cartesian genetic programming. As an example that demonstrates the general applicability of this idea, we trained a swarm-based optimization heuristics and present first results showing that the learned swarm behavior is significantly better than just random search. We also discuss the encountered pitfalls and remaining challenges on the research agenda.", abstract = "Cyber-physical systems demand self-organizing algorithms that rely on emergent behaviour; local observations and decision aggregate to global behavior without explicitly programmed rules. Designing these algorithms is error prone. Widely applicable design patterns are scarce. We opt for a machine learning approach that learns mechanisms for targeted emergent behavior automatically. We use Cartesian genetic programming. As an example, that demonstrates the general applicability of this idea, we trained a swarm based heuristics and present first results showing that the learned swarm behavior is significantly better than just random search. We also discuss the encountered pitfalls and remaining challenges on the research agenda.", notes = "Also {"}Annals of Computer Science and Information Systems{"}, ACSIS, Volume 26, 55-60 (2021) Sofia, Bulgaria secretariat@fedcsis.org http://annals-csis.org/", } @InProceedings{bremer:2022:PAAMS, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Fully Distributed Cartesian Genetic Programming", booktitle = "Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection", year = "2022", editor = "Frank Dignum and Philippe Mathieu and Juan Manuel Corchado and Fernando {De La Prieta}", volume = "13616", series = "LNAI", pages = "36--49", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, parallel computing, Multi-agent system, COHDA, Distributed optimization", isbn13 = "978-3-031-18192-4", URL = "https://rdcu.be/c7nZL", URL = "http://link.springer.com/chapter/10.1007/978-3-031-18192-4_4", DOI = "doi:10.1007/978-3-031-18192-4_4", size = "14 pages", abstract = "Cartesian genetic programming is a popular version of genetic programming and has meanwhile proven its performance in many use cases. This paper introduces an algorithmic level decomposition of program evolution that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem solving is adapted to evolve programs. The applicability of the approach and the effectiveness of the multi-agent approach as well as of the evolved genetic programs are demonstrated using symbolic regression, n-parity, and classification problems.", } @Article{bremer:2023:Systems, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by {CMA-ES}", journal = "Systems", year = "2023", volume = "11", number = "4", pages = "Article No. 177", keywords = "genetic algorithms, genetic programming, cartesian genetic programming", ISSN = "2079-8954", URL = "https://www.mdpi.com/2079-8954/11/4/177", DOI = "doi:10.3390/systems11040177", abstract = "Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimisation approach. Recently, an algorithmic level decomposition of program evolution has been introduced that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem-solving was adapted to evolve these programs. The applicability of the approach and the effectiveness of the used multi-agent protocol as well as of the evolved genetic programs for the case of full enumeration in local agent decisions has already been successfully demonstrated. Symbolic regression, n-parity, and classification problems were used for this purpose. As is typical of decentralized systems, agents have to solve local sub-problems for decision-making and for determining the best local contribution to solving program evolution. So far, only a full enumeration of the solution candidates has been used, which is not sufficient for larger problem sizes. We extend this approach by using CMA-ES as an algorithm for local decisions. The superior performance of CMA-ES is demonstrated using Koza’s computational effort statistic when compared with the original approach. In addition, the distributed modality of the local optimisation is scrutinized by a fitness landscape analysis.", notes = "also known as \cite{systems11040177}", } @InProceedings{bremer:2023:UKCI, author = "Joerg Bremer and Sebastian Lehnhoff", title = "Hybridizing Levy Flights and Cartesian Genetic Programming for Learning {Swarm-Based} Optimization", booktitle = "UK Workshop on Computational Intelligence", year = "2023", pages = "299--310", address = "Birmingham", month = "6-8 " # sep, publisher = "Springer", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", URL = "http://link.springer.com/chapter/10.1007/978-3-031-47508-5_24", DOI = "doi:10.1007/978-3-031-47508-5_24", notes = "Published in 2024", } @InProceedings{Bremner:2010:ICES, author = "Paul Bremner and Mohammad Samie and Gabriel Dragffy and Tony Pipe and James Alfred Walker and Andy M. Tyrrell", title = "Evolving Digital Circuits Using Complex Building Blocks", booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010", year = "2010", editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller", series = "Lecture Notes in Computer Science", volume = "6274", pages = "37--48", address = "York", month = sep # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-15322-8", DOI = "doi:10.1007/978-3-642-15323-5_4", size = "12 pages", abstract = "This work is a study of the viability of using complex building blocks (termed molecules) within the evolutionary computation paradigm of CGP; extending it to MolCGP. Increasing the complexity of the building blocks increases the design space that is to be explored to find a solution; thus, experiments were undertaken to find out whether this change affects the optimum parameter settings required. It was observed that the same degree of neutrality and (greedy) 1+4 evolution strategy gave optimum performance. The Computational Effort used to solve a series of benchmark problems was calculated, and compared with that used for the standard implementation of CGP. Significantly less Computational Effort was exerted by MolCGP in 3 out of 4 of the benchmark problems tested. Additionally, one of the evolved solutions to the 2-bit multiplier problem was examined, and it was observed that functionality present in the molecules, was exploited by evolution in a way that would be highly unlikely if using standard design techniques.", affiliation = "Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY", } @InProceedings{Bremner:2011:EuroGP, author = "Paul Bremner and Mohammad Samie and Anthony G. Pipe and Gabriel Dragffy and Yang Liu", title = "Evolving Cell Array Configurations Using CGP", booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011", year = "2011", month = "27-29 " # apr, editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado", series = "LNCS", volume = "6621", publisher = "Springer Verlag", address = "Turin, Italy", pages = "73--84", organisation = "EvoStar", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-20406-7", DOI = "doi:10.1007/978-3-642-20407-4_7", abstract = "A cell array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells. In this paper we have presented a means by which CGP might be adapted to evolve configurations of a proposed cell array. As part of doing so, we have suggested an additional genetic operator that exploits modularity by copying sections of the genome within a solution, and investigated its efficacy. Additionally, we have investigated applying selection pressure for parsimony during functional evolution, rather than in a subsequent stage as proposed in other work. Our results show that solutions to benchmark problems can be evolved with a good degree of efficiency, and that compact solutions can be found with no significant impact on the required number of circuit evaluations.", notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011", } @InProceedings{Bremner:2011:MOoCCE, title = "Multi-Objective Optimisation of Cell-Array Circuit Evolution", author = "Paul Bremner and Mohammad Samie and Anthony Pipe and Andy Tyrrell", pages = "440--446", booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation", year = "2011", editor = "Alice E. Smith", month = "5-8 " # jun, address = "New Orleans, USA", organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "0-7803-8515-2", keywords = "genetic algorithms, genetic programming, cartesian genetic programming, cell-array circuit evolution, circuit decomposition technique, custom FPGA, digital circuit, interconnected configurable cell, multiobjective optimisation, field programmable gate arrays", DOI = "doi:10.1109/CEC.2011.5949651", abstract = "In this paper we have investigated the efficacy of applying multi-objective optimisation to Cartesian genetic programming (CGP) when used for evolution of cell-array configurations. A cell-array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells; thus, the CGP nodes are more complex than in its standard implementation. We have described modifications to a previously described optimisation algorithm that has led to significant improvements in performance; circuits close to a hand designed equivalent have been found, in terms of the optimised objectives. Additionally we have investigated the effect of circuit decomposition techniques on evolutionary performance. We found that using a hybrid of input and output decomposition techniques substantial reductions in evolution time were observed. Further, while the number of circuit inputs is the key factor for functional evolution time, the number of circuit outputs is the key factor for optimisation time.", notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.", } @Article{bressane:2023:Pollutants, author = "Adriano Bressane and Anna Isabel Silva Loureiro and Raissa Caroline Gomes and Admilson Irio Ribeiro and Regina Marcia Longo and Rogerio Galante Negri", title = "Spatiotemporal Effect of Land Use on Water Quality in a Peri-Urban Watershed in a Brazilian Metropolitan Region: An Approach Considering {GEP-Based} Artificial Intelligence", journal = "Pollutants", year = "2023", volume = "3", number = "1", pages = "1--11", keywords = "genetic algorithms, genetic programming, gene expression programming", ISSN = "2673-4672", URL = "https://www.mdpi.com/2673-4672/3/1/1", DOI = "doi:10.3390/pollutants3010001", abstract = "The suppression of natural spaces due to urban sprawl and increases in built and agricultural environments has affected water resource quality, especially in areas with high population densities. Considering the advances in the Brazilian environmental legal framework, the present study aimed to verify whether land use has still affected water quality through a case study of a peri-urban watershed in a Brazilian metropolitan region. Analyses of physical–chemical indicators, collected at several sample points with various land-use parameters at different seasons of the year, were carried out based on an approach combining variance analysis and genetic programming. As a result, some statistically significant spatiotemporal effects on water quality associated with the land use, such as urban areas and thermotolerant coliform (R = −0.82, p < 0.01), mixed vegetation and dissolved oxygen (R = 0.80, p < 0.001), agriculture/pasture and biochemical oxygen demand (R = 0.40, p < 0.001), and sugarcane and turbidity (R = 0.65, p < 0.001), were verified. In turn, gene expression programming allowed for the computing of the importance of land-use typologies based on their capability to explain the variances of the water quality parameter. In conclusion, in spite of the advances in the Brazilian law, land use has still significantly affected water quality. Public policies and decisions are required to ensure effective compliance with legal guidelines.", notes = "also known as \cite{pollutants3010001}", } @InCollection{breunig:1995:LIPRGP, author = "Markus M. Breunig", title = "Location Independent Pattern Recognition using Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1995", year = "1995", editor = "John R. Koza", pages = "29--38", address = "Stanford, California, 94305-3079 USA", month = "11 " # dec, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming, ADF", ISBN = "0-18-195720-5", URL = "http://www.dbs.informatik.uni-muenchen.de/~breunig/HomepageResearch/Papers/PatternRecog.pdf", size = "10 pages", abstract = "This paper describes an application of genetic programming. Programs able of recognising a pattern independent of its location are evolved. Usually the evolution of programs is controlled primarily by the fitness evaluation function. This paper demonstrates how genetic programming can be encouraged to evolve programs with properties not being explicitly considered in the fitness measure like location independence. The measurements taken include the use of automatically defined functions allowing the problem to be decomposed into sub-functions, a special implementation of iteration and carefully chosen function and terminal sets. A main purpose was to minimise the restrictions imposed on the solution, i.e. giving the genetic programming as much freedom as possible while still encouraging the desired properties.", notes = "part of \cite{koza:1995:gagp}", } @InProceedings{Brezocnik:1997:DAAAM, author = "Miran Brezocnik and Joze Balic", title = "System for discovering and optimizung mathematical models using genetic programming and genetic algorithms", booktitle = "Proceedings of the 8th International DAAAM Symposium", year = "1997", editor = "Branko Katalinic", pages = "37--38", month = "23-25 " # oct, publisher = "DAAAM International", email = "mbrezocnik@uni-mb.si", ISBN = "3-901509-04-6", address = "Dubrovnik, Croatia", publisher_address = "Vienna", keywords = "genetic algorithms, genetic programming, adaptive systems, evolutionary computation", abstract = "In this paper, we propose a system for discovering and optimising of various mathematical models. The system consists of two parts. In the first part, we discover unknown mathematical models on the basis of empirical given data (learning data). In the second part, we optimise parameters of the discovered mathematical models. Genetic programming (GP) and genetic algorithm (GA) are used for discovering and optimizing of models, respectively. GP and GA are evolutionary optimization methods based on the Darwinian natural selection and survival of the fittest.", notes = "http://www.daaam.com/daaam/Past_Activities/DAAAM_International_Activities_1990-2005.pdf", } @InProceedings{Brezocnik:1997:ICDMMI, author = "Miran Brezocnik and Joze Balic", title = "Comparison of genetic programming with genetic algorithm", booktitle = "3rd International Conference Design to Manufacture in Modern Industry: Design to manufacture in modern industry", year = "1997", editor = "Anton Jezernik and Bojan Dolsak", pages = "150--156", month = sep, publisher = "University of Maribor, Faculty of Mechanical Engineering", publisher_address = "Slovenia", email = "mbrezocnik@uni-mb.si", ISBN = "86-435-0192-1", keywords = "genetic algorithms, genetic programming", abstract = "The paper is concerned about the conventional genetic algorithm (GA) and, particularly, the recently proposed paradigm: genetic programming (GP). The well-known basic knowledge of the conventional GA is briefly presented, but only for comparison with GP. On the contrary, the GP method is discussed in detail. The GP is an evolutionary process, where the fittest computer program in the space of possible computer programs is searched for. The fittest computer program represents a solution in the observed problem domain. One of the most powerful abilities of the GP that allows inclusion of rich information into computer programs is clearly presented and emphasised. Our personal views concerning the complementary nature of the conventional GA and GP are discussed. Finally, we briefly presented the implementation of the GP on the wide variety of different problems.", } @InProceedings{Brezocnik:1998:IAD, author = "Miran Brezocnik and Joze Balic", title = "A genetic programming approach for modelling of self-organizing assembly systems", booktitle = "Intelligent assembly and disassembly - IAD'98: A proceedings volume from the IFAC Workshop", year = "1998", editor = "Peter Kopacek and Dragica Noe", pages = "47--52", address = "Bled, Slovenia", publisher_address = "Oxford, UK", month = "21-23 " # may, organisation = "IFAC", publisher = "Pergamon", keywords = "genetic algorithms, genetic programming, self-organising systems, intelligent manufacturing systems, assembly, simulation", ISBN = "0-08-043042-2", URL = "http://www.amazon.co.uk/Intelligent-Assembly-Disassembly-IAD-Proceedings/dp/0080430422", URL = "http://trove.nla.gov.au/version/44951526", abstract = "The paper proposes a genetic programming approach to the modelling of the assembly of the basic components (cells) into an integrated (whole) organism. The concept is based on the simulation of self-organising uniting of live cells into tissues, organs and individuals. The assembly is treated as the basic and general principle, therefore, the basic cells can be very different. Assembling takes place on the basis of the genetic content in the basic components and is influenced by the environmental conditions. The genetic content can be topological, geometrical, technological, ecological, economical, etc. The simulation of the self-organizing genetic assembly of the variant product consisting of many basic components is given. The basic feature of genetic assembly is that it takes place in a distributed, nondeterministical, bottom-up, and self-organising manner.", } @PhdThesis{Brezocnik:thesis, author = "Miran Brezocnik", title = "MODELING OF TECHNOLOGICAL SYSTEMS BY THE USE OF GENETIC METHODS", school = "University of Maribor, Faculty of Mechanical Engineering", year = "1998", type = "phdthesis", address = "Smetanova ulica 17, SI-2000 Maribor, Slovenia", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, intelligent manufacturing systems, technological system, forming, assembly, robots, self-organisation, genetic methods, modelling, optimisation", size = "129 pages", abstract = "In this work we propose modelling of different technological systems by a general approach. The research starts with searching for common characteristics of the technological systems. After they have been found out, they are synthesised into uniform principle serving for conceiving a general method for their modeling. The method imitates associating of living cells into tissues, organs and organisms. The disturbances resulting from limited human knowledge, unpredictability of technological systems, and unexpected events in production environment are automatically eliminated during the evolutionary process. More and more intelligent behaviour of the individual technological system, which is expressed as an increasingly successful synchronisation of the material, energy and information, is obtained gradually with self-organization and without centralised instruction. In order to support the theoretical researches a system for genetic programming is developed. It is successfully used for genetic modeling of: 1. forming efficiency, 2. assembly and classification, and 3. trajectories of robots in the production environment. The results of modeling of forming efficiency show excellent correspondence between analytically obtained models, experimental results, and genetically developed models. In case of genetic modeling of assembly the basic components are integrated into the final product in a self-organising manner. Genetic modelling the trajectory of the robot, striving to arrive at the aim through a dynamic production environment, discovers the intelligent robot navigation formed during the evolutionary process.", } @Article{Brezocnik:2000:JTP, author = "Miran Brezocnik and Joze Balic and Leo Gusel", title = "Artificial intelligence approach to determination of flow curve", journal = "Journal for technology of plasticity", year = "2000", volume = "25", number = "1-2", pages = "1--7", keywords = "genetic algorithms, genetic programming, forming, flow curve, artificial intelligence", ISSN = "0350-2368", ISSN = "0354-3870", abstract = "For the control of the forming process it is necessary to know as precisely as possible the flow curve of the material formed. The paper presents the determination of the equation for the flow curve with the artificial intelligence approach. The genetic programming method (GP) was used. It is an evolutionary optimisation technique based on the Darwinian natural selection and the survival of the fittest organisms. The comparison between the experimental results, the analytical solution and the solution obtained genetically clearly shows that the genetic programming method is a very promising approach.", notes = "Novi Sad : Faculty of Technical Sciences, Institute for Production Engineering broken http://www.scindeks.nbs.bg.ac.yu/arhiva.php?issn=0350-2368&je=en", } @Book{Brezocnik:book, author = "Miran Brezocnik", title_en = "Using genetic programming in intelligent manufacturing systems", title = "Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih", publisher = "University of Maribor, Faculty of mechanical engineering", year = "2000", address = "Maribor, Slovenia", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, manufacturing, intelligent manufacturing systems, modelling, assembly, metal forming, autonomous robot, evolutionary algorithms", ISBN = "86-435-0306-1", broken = "http://maja.uni-mb.si/slo/Knjige/2000-03-mon/index.htm", URL = "http://www.isbns.net/isbn/9788643503065/", URL = "http://www.worldcat.org/title/uporaba-genetskega-programiranja-v-inteligentnih-proizvodnih-sistemih/oclc/444489491", size = "160 pages", note = "In Slovenian", } @Article{Brezocnik:2001:MPT, author = "Miran Brezocnik and Joze Balic and Zlatko Kampus", title = "Modeling of forming efficiency using genetic programming", journal = "Journal of Materials Processing Technology", volume = "109", pages = "20--29", year = "2001", number = "1-2", month = "1 " # feb, email = "joze.balic@uni-mb.si", keywords = "genetic algorithms, genetic programming, Metal-forming, Yield stress, Forming efficiency, Modeling, Adaptation, Artificial intelligence", ISSN = "0924-0136", URL = "http://www.sciencedirect.com/science/article/B6TGJ-423HM9M-5/1/bcc93a13fbb04521236d3a8e16f8850b", DOI = "doi:10.1016/S0924-0136(00)00783-4", abstract = "This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry. [COBISS.SI-ID 5979414]", notes = "Journal of Materials Processing Technology http://www.elsevier.com/wps/find/journaldescription.cws_home/505656/description#description", } @Article{Brezocnik:2001:RCIM, author = "Miran Brezocnik and Joze Balic", title = "A genetic-based approach to simulation of self-organizing assembly", journal = "Robotics and Computer-Integrated Manufacturing", volume = "17", pages = "113--120", year = "2001", number = "1-2", month = feb, keywords = "genetic algorithms, genetic programming, Intelligent manufacturing systems, Self-organizing assembly, Evolution", ISSN = "0736-5845", DOI = "doi:10.1016/S0736-5845(00)00044-2", URL = "http://www.sciencedirect.com/science/article/B6V4P-42DP1Y1-J/1/175033beb3ddb787b75c22253e5534c2", abstract = "The paper proposes a new and innovative biologically oriented idea in conceiving intelligent systems in modern factories of the future. The intelligent system is treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the production environment and the environment in a wider sense. Simulation of self-organizing assembly of mechanical parts (basic components) into the product is presented as an example of the intelligent system. The genetic programming method is used. The genetic-based assembly takes place on the basis of the genetic content in the basic components and the influence of the environment. The evolution of solutions happens in a distributed way, nondeterministically, bottom-up, and in a self-organizing manner. The paper is also a contribution to the international research and development program intelligent manufacturing systems, which is one of the biggest projects ever introduced.", notes = "Robotics and Computer-Integrated Manufacturing http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description", } @InProceedings{Brezocnik:2001:RIM, author = "Miran Brezocnik and Miha Kovacic", title = "Survey of the evolutionary computation and its application in manufacturing systems", booktitle = "3rd International Conference on Revitalization and Modernization of Production RIM 2001", year = "2001", editor = "Milan Jurkovic and Isak Karabegovic", pages = "501--508", address = "University of Bihac, Bihacu, Bosnia and Herzegovina", month = sep, organisation = "Bihac, Tehnieki fakultet", keywords = "genetic algorithms, genetic programming", ISBN = "9958-624-10-9", } @Article{Brezocnik:2002:JIM, author = "Miran Brezocnik and Joze Balic and Karl Kuzman", title = "Genetic programming approach to determining of metal materials properties", journal = "Journal of Intelligent Manufacturing", year = "2002", volume = "13", number = "1", pages = "5--17", month = feb, email = "joze.balic@uni-mb.si", keywords = "genetic algorithms, genetic programming, materials properties, metal forming, modeling, self-organisation", ISSN = "0956-5515", DOI = "doi:10.1023/A:1013693828052", abstract = "The paper deals with determining metal materials properties by use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebel's expression was genetically developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically.", notes = "Journal of Intelligent Manufacturing http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40528-70-35668245-0,00.html", } @InProceedings{Brezocnik:2002:AMME, author = "Miran Brezocnik and Miha Kovacic", title = "Prediction of surface roughness with genetic programming", booktitle = "Proceedings of the 11th International Scientific Conference Achievements in Mechanical and Materials Engineering, AMME'2002", year = "2002", editor = "Leszek A. Dobrzanski", pages = "23--26", keywords = "genetic algorithms, genetic programming", ISBN = "83-914458-7-9", notes = "Broken Dec 2012 http://www.wamme.org/index.php?id=37&PHPSESSID=8b9ce9355f0dbdaebee40f5d6ddec320 See also \cite{Brezocnik:2004:JMPT}", } @InCollection{Brezocnik:2002:DAAAM, author = "Miran Brezocnik", title = "On intelligent learning systems for next-generation manufacturing", booktitle = "DAAAM International Scientific Book 2002", chapter = "6", pages = "39--48", publisher = "DAAAM International", year = "2002", volume = "1", address = "Vienna", month = oct, editor = "Branko Katalinic", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, manufacturing systems, artificial intelligence, learning, evolutionary computation, emergence", ISBN = "3-901509-30-5", URL = "http://www.daaam.com/", abstract = "In the first part of the paper we analyse the basic scientific and philosophical facts, as well as social circumstances, that have a great impact on manufacturing concepts. Then we propose a shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organisational principles towards intelligent systems in next-generation manufacturing involving phenomena such as non-determination, emergence, learning, complexity, self-organization, bottom-up organisation, and co-existence with natural environment. In the second part we give two examples from metal forming industry and autonomous intelligent vehicles. Both systems are based on learning and imitate some excellent properties of living systems. The stable global order (i.e. the solution) of each presented system gradually emerges as a result of interactions between basic entities of which the system consists and the environment.", notes = "http://www.daaam.com/daaam/Publications/Publications.htm", } @InProceedings{Brezocnik:TMT2002, author = "Miran Brezocnik and Miha Kovacic", title = "Integrated evolutionary computation environment for optimizing and modeling of manufacturing processes", booktitle = "6th International Research/Expert Conference {"}Trends in the development of Machinery and Associated Technology{"}", year = "2002", editor = "Safet Brdarevia and Sabahudin Ekinovia and Ramon {Compamys Pascual} and Joan {Calvet Vivancos}", pages = "TMT02--073", address = "Neum, Bosnia and Herzegovina", month = "18-22 " # sep, organisation = "FACULTY OF MECHANICAL ENGINEERING IN ZENICA, UNIVERSITY OF SARAJEVO, BOSNIA AND HERZEGOVINA. UNIVERSITAT POLITECNICA DE CATALUNYA BARCELONA, DEP. D'ENGINYERIA MECANICA (SPAIN)", keywords = "genetic algorithms, genetic programming, Poster", ISBN = "9958-617-11-0", URL = "http://www.mf.unze.ba/tmt2002/tmt2002-1.htm", notes = "http://www.mf.unze.ba/tmt2002/", } @Article{Brezocnik:2003:RCIM, author = "Miran Brezocnik and Joze Balic and Zmago Brezocnik", title = "Emergence of intelligence in next-generation manufacturing systems", journal = "Robotics and Computer-Integrated Manufacturing", year = "2003", volume = "19", pages = "55--63", number = "1-2", abstract = "In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organisation, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analysed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment.", owner = "wlangdon", URL = "http://www.sciencedirect.com/science/article/B6V4P-47XW4VG-1/2/f88aada395a16da3031d89d272dae207", month = feb # "-" # apr, keywords = "genetic algorithms, genetic programming, Intelligent manufacturing systems, Emergence, Learning", DOI = "doi:10.1016/S0736-5845(02)00062-5", } @InCollection{Brezocnik:2003:DAAAM, author = "Miran Brezocnik and Miha Kovacic", title = "Modelling of intelligent mobility for next-generation manufacturing systems", volume = "2", booktitle = "DAAAM International Scientific Book 2003", publisher = "DAAAM International Vienna", year = "2003", editor = "B. Katalinic", pages = "95--102", address = "Vienna", month = jul, keywords = "genetic algorithms, genetic programming", ISBN = "3-901509-30-5", abstract = "We present the modelling of the intelligent mobility for next-generation manufacturing systems. The modelling took place in the simplified dynamic manufacturing environment with several loads, obstacles and one robot placed in it. Each agent is freely movable on the floor. The aim of the robot is to pick up all loads and to come to the goal point. For optimisation of the robot path between loads and for planning of the robot travel the genetic algorithm and the genetic programming were used, respectively. The research showed that intelligent behaviour of the robot results from the interactions of the robot with the dynamic environment.", notes = "publication@daaam.com broken Jan 2013 http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm", } @InProceedings{Brezocnik:2003:tmt, author = "Miran Brezocnik and Miha Kovacic and Mirko Ficko", title = "Genetic-based approach to predict surface roughness in end milling", booktitle = "7th International Research/Expert Conference {"}Trends in the Development Machinery and Associated Technology{"}", year = "2003", pages = "529--532", address = "Barcelona, Spain", month = "15-16 " # sep, organisation = "UNIVERSITAT POLITECNICA DE CATALUNYA UNIVERSITY OF SARAJEVO ESCOLA TECNICA SUPERIOR D'ENGINYERIA INDUSTRIAL DE BARCELONA FACULTY OF MECHANICAL ENGINEERING IN ZENICA (Bosnia and Herzegovina) DEPARTAMENT D'ENGINYERIA MECANICA (Spain)", keywords = "genetic algorithms, genetic programming", ISBN = "9958-617-18-8", notes = "http://www.mf.unze.ba/tmt2003/papers.htm", } @Article{Brezocnik:2004:AJME, author = "Miran Brezocnik and Miha Kovacic and Mirko Ficko", title = "Intelligent systems for next-generation manufacturing", journal = "Academic Journal of Manufacturing Engineering", year = "2004", volume = "2", number = "1", pages = "34--37", keywords = "genetic algorithms, genetic programming", ISSN = "1583-7904", URL = "http://www.worldcat.org/title/intelligent-systems-for-next-generation-manufacturing/oclc/440013859", abstract = "In this paper we propose a fundamental shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organizational principles towards intelligent systems in next-generation manufacturing involving phenomena such as non-determinism, emergence, learning, complexity, self-organization, and bottom-up organization. The problem types and different intelligent systems for solving of problems were studied. Two examples of the intelligent systems from the areas of metal forming industry and autonomous intelligent vehicles are given. Both systems are based on learning and imitate some excellent properties of the living systems. Genetic programming and genetic algorithms were used. The stable global order (i.e., solution) of each presented system gradually emerges as a result of interactions between entities of which the system consists and the environment", notes = "http://reviste.ulbsibiu.ro/ajme/ http://eng.upt.ro/auif/journal_vol_2_2004_no_1.html#8 Feb 2014 journal_vol_2_2004_no_1.html gives a number of alternative titles... ", } @Article{Brezocnik:2004:IJAMT, author = "Miran Brezocnik and Leo Gusel", title = "Predicting stress distribution in cold-formed material with genetic programming", journal = "International journal of advanced manufacturing technology", year = "2004", volume = "23", number = "7-8", pages = "467--474", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, metal forming, stress distribution, modelling", ISSN = "0268-3768", DOI = "doi:10.1007/s00170-003-1649-3", abstract = "In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.", } @InProceedings{Brezocnik:2004:TMT, author = "Miran Brezocnik and Mirko Ficko and Miha Kovacic", title = "Genetic based approach to predict surface roughness", booktitle = "8th International Research/Expert Conference Trends in the Development Machinery and Associated Technology", year = "2004", pages = "91--94", address = "Neum, Bosnia and Herzegovina", month = "15-19 " # sep, keywords = "genetic algorithms, genetic programming, celno frezanje, povrsinska hrapavost, napoved hrapavosti, genetsko programiranje, end milling, surface roughness, prediction of surface roughness", ISBN = "9958-617-21-8", URL = "http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9009686", URL = "https://plus.cobiss.si/opac7/bib/9009686", abstract = "In this paper we propose genetic programming to predict surface roughness in end milling. Two independent data sets were obtained from measurements: the training data set and the testing data set. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables (parameters), while surface roughness was the output variable. Different surface roughness models were obtained with the training data set and genetic programming. The testing data set was used to prove the accuracy of the best model. The conclusion is that surface roughness is most influenced by the feed rate, while vibrations increase the prediction accuracy.", jaezik = "angleski", notes = "Paper number TMT04-237 http://www.mf.unze.ba/tmt2004/submittedabstracts2.htm (broken) COBISS.SI-ID: 9009686", } @Article{Brezocnik:2004:JMPT, author = "M. Brezocnik and M. Kovacic and M. Ficko", title = "Prediction of surface roughness with genetic programming", journal = "Journal of Materials Processing Technology", year = "2004", volume = "157-158", pages = "28--36", month = "20 " # dec # " 2004", keywords = "genetic algorithms, genetic programming, Manufacturing systems, Surface roughness; Milling, Evolutionary algorithms", ISSN = "0924-0136", DOI = "doi:10.1016/j.jmatprotec.2004.09.004", abstract = "In this paper, we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate, depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.", notes = "Originally in AMME 2000-2002 conference \cite{Brezocnik:2002:AMME}. Achievements in Mechanical and Materials Engineering Conference. Selected for publication as full paper in the Special Issue of the Journal of Materials Processing Technology (Elsevier, the Netherlands)", } @InProceedings{Mechatronics2004_Abstract_026, author = "Miha Kovacic and Miran Brezocnik and Joze Balic", title = "Genetic Programming Approach for Autonomous Vehicles", booktitle = "Mechatronics 2004 9th Mechatronics Forum International Conference", year = "2004", address = "METU, Ankara, Turkey", month = "30 " # aug # "-1 " # sep, organisation = "Atilim University", keywords = "genetic algorithms, genetic programming", URL = "http://mechatronics.atilim.edu.tr/mechatronics2004/papers/Mechatronics2004_Abstract_026.pdf", size = "1 page", abstract = "GP was used for intelligent path planning of an autonomous vehicle in 2D production environment. Robot had to find loads, to avoid all the obstacles and to reach the target point. The production environment (robot, loads and obstacles) are represented as free 2D shapes. The robot discretely rotates for 30 degrees left and right and moves forward by two different steps. Step decreases if the sensor detects the load or obstacle. The GP system tries to find gradually optimal program for robot navigation through production environment as a consequence of interactions between the robot and detected environment. Program for navigation can be randomly constructed of logical operators (IFLOAD, IF-OBSTACLE), basic commands (MOVE, RIGHT, LEFT), and connection functions (CF2, CF3). Each program is run several times until 100 time units for the robot's task are used or the target point is reached. The system for genetic programming was run 50-times. Robot travelled safely with all collected loads to the target point 2-times, which means that the probability of the finding successful navigation program is 4 percent. In future the researches will be oriented particularly towards conceiving an improved GP system with the possibility of use 3D models of the production environment. Preliminary results of the concept are encouraging.", notes = "University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia", } @Article{brezocnik_2004_AJME, author = "Miran Brezocnik and Miha Kovacic and Joze Balic and Bogdan Sovilj", title = "Programming {CNC} measuring machines by genetic algorithms", journal = "Academic Journal of Manufacturing Engineering", year = "2004", volume = "2", number = "4", pages = "15--20", keywords = "genetic algorithms, genetic programming, optimisation, coordinate measuring machines, computer aided quality control, evolutionary computation", ISSN = "1583-7904", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/brezocnik_2004_AJME.pdf", size = "6 pages", abstract = "The need for efficient and reliable tools for programming of CNC coordinate measuring machine is rapidly increasing in modern production. The proposed concept based on genetic algorithms assures generation and optimization of NC programs for measuring machine. Therefore the structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the tactile probe which performs simple elementary motions in the discretized measuring area. During the simulated evolution the probe movement becomes more and more optimized and intelligent solutions emerge gradually as a result of the low level interaction between the simple probe movements and the measuring environment. Example of CNC programming of measuring machine is given. Results show universality and inventiveness of the approach", notes = "http://www.eng.utt.ro/auif/ http://www.eng.utt.ro/auif/rev/issue/no-08/no-08.html#C2", } @Article{Brezocnik:2003:MMP, author = "Miran Brezocnik and Miha Kovacic", title = "Integrated genetic programming and genetic algorithm approach to predict surface roughness", journal = "Materials and Manufacturing Processes", year = "2003", volume = "18", number = "3", pages = "475--491", month = may, keywords = "genetic algorithms, genetic programming, Manufacturing systems, Surface roughness, Milling", DOI = "doi:10.1081/AMP-120022023", abstract = "we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimised by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modelling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.", } @InProceedings{Brezocnik:2005:RIM, author = "Miran Brezocnik and Bostjan Vaupotic and Janez Fridrih and Ivo Pahole", title = "Cost estimation for punch dies by genetic programming", booktitle = "RIM 2005 / 5th International scientific conference on Production engineering", year = "2005", editor = "Milan Jurkovic and Vlatko Dolecek", pages = "167--172", month = "14-17 " # sep, publisher = "Faculty of Technical Engineering, Bihac, Bosnia and Hercegovina", email = "mbrezocnik@uni-mb.si", keywords = "genetic algorithms, genetic programming, punch dies, cost estimation", ISBN = "9958-9262-0-2", abstract = "The paper presents a new approach for cost estimation of punch dies used in metal-forming industry. In the modern business world fast and accurate information is the principal advantage in securing orders and establishing the company's renowned. Often, the offer for the manufacturing and supply of the tool must be sent within a short time. However, precise preparation of the offer requires much work. The paper presents an approach ensuring fast determination of the relatively precise cost estimate of the punch dies on the basis of the tool input parameters (e.g., outside dimensions, number of blades, number of directions of cutting). The proposed approach is based on the evolutionary searching for the adequate general equation describing the influence of the tool input parameters on punch die manufacturing cost. Evolutionary development of the equation was performed by the genetic programming and the base of the punch dies already made.", } @Article{brezocnik:2005:MMP, author = "Miran Brezocnik and Miha Kovacic and Leo Gusel", title = "Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution", journal = "Materials and Manufacturing Processes", year = "2005", volume = "20", number = "3", pages = "497--508", month = may, keywords = "genetic algorithms, genetic programming, Metal forming, Stress distribution, System modelling", ISSN = "1042-6914", URL = "http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497", DOI = "doi:10.1081/AMP-200053541", abstract = "We compare genetic algorithm (GA) and genetic programming (GP) for system modelling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analysed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.", notes = "A1 Laboratory for Intelligent Manufacturing Systems, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia A2 Laboratory for Material Forming, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia", } @Article{Brezocnik:2006:AMME, author = "Miran Brezocnik and Miha Kovacic and Matej Psenicnik", title = "Prediction of steel machinability by genetic programming", journal = "Journal of achievements in materials and manufacturing engineering", year = "2006", volume = "16", number = "1-2", pages = "107--113", month = may # "-" # jun, note = "Special Issue of CAM3S'2005", keywords = "genetic algorithms, genetic programming, Steel machinability, Extra machinability, Modelling", issn_ = "Y505-3994 invalid checksum", URL = "http://jamme.acmsse.h2.pl/index.php?id=69", URL = "http://157.158.19.167/papers_cams05/1123.pdf", size = "7 pages", abstract = "The steels with extra machinability are made according to a special technological process. Such steels can be machined at high cutting speeds. In addition, the resistance of the tools used for machining, is higher than in the case of ordinary steels. It depends on several parameters, particularly on the steel chemical composition, whether the steel will meet the criterion of extra machinability. Special tests for each batch separately show whether the steel has extra machinability or not. In our research, the prediction of machinability of steels, depending on input parameters, was performed by genetic programming and data on the batches of steel already made. The model developed during the simulated evolution was tested also with the testing data set. The results show that the proposed concept can be successfully used in practice.", notes = "http://www.journalamme.org/ Formerly Proceedings of Achievements in Mechanical and Materials Engineering. (1.123) Intelligent Manufacturing Systems Laboratory, University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, SI-2000 Maribor, Slovenia", } @Article{brezocnik:2021:Metals, author = "Miran Brezocnik and Uros Zuperl", title = "Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic {Programming--An} Industrial Study", journal = "Metals", year = "2021", volume = "11", number = "6", keywords = "genetic algorithms, genetic programming", ISSN = "2075-4701", URL = "https://www.mdpi.com/2075-4701/11/6/972", DOI = "doi:10.3390/met11060972", abstract = "Store Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (t-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model's analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same.", notes = "also known as \cite{met11060972}", } @Article{Briand:2006:GPEM, author = "Lionel C. Briand and Yvan Labiche and Marwa Shousha", title = "Using genetic algorithms for early schedulability analysis and stress testing in real-time systems", journal = "Genetic Programming and Evolvable Machines", year = "2006", volume = "7", number = "2", pages = "145--170", month = aug, note = "Special Issue: Best of GECCO 2005", keywords = "genetic algorithms, Software verification and validation, Schedulability theory", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-006-9003-9", abstract = "Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the designers of such systems to analyse the schedulability of tasks during the design process, as well as to test the system's response time to events in an effective manner once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on the system task architecture, the derivation of test cases that maximise the chances of critical deadline misses within the system; we refer to this testing activity as stress testing. A second objective is to enable an early but realistic analysis of tasks' schedulability at design time. We have developed a specific solution based on genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but that, nevertheless, exhibit deadline misses.", } @InProceedings{briesch:2023:GECCOcomp, author = "Martin Briesch and Dominik Sobania and Franz Rothlauf", title = "On the {Trade-Off} between Population Size and Number of Generations in {GP} for Program Synthesis", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "535--538", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, program synthesis, crossover, population size, generations, mutation: Poster", isbn13 = "9798400701191", DOI = "doi:10.1145/3583133.3590681", size = "4 pages", abstract = "When using genetic programming for program synthesis, we are usually constrained by a computational budget measured in program executions during evolution. The computational budget is influenced by the choice of population size and number of generations per run leading to a trade-off between both possibilities. To better understand this trade-off, we analyze the effects of different combinations of population sizes and number of generations on performance. Further, we analyze how the use of different variation operators affects this trade-off. We conduct experiments on a range of common program synthesis benchmarks and find that using larger population sizes lead to a better search performance. Additionally, we find that using high probabilities for crossover and mutation lead to higher success rates. Focusing on only crossover or using only mutation usually leads to lower search performance. In summary, we find that large populations combined with high mutation and crossover rates yield highest GP performance for program synthesis approaches.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @InProceedings{briesch:2024:GECCOcomp, author = "Martin Briesch and Ryan Boldi and Dominik Sobania and Alexander Lalejini and Thomas Helmuth and Franz Rothlauf and Charles Ofria and Lee Spector", title = "Improving Lexicase Selection with Informed {Down-Sampling}", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Marcus Gallagher", pages = "25--26", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, lexicase selection, informed down-sampling", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664068", size = "2 pages", abstract = "This short paper presents the main findings of our work titled Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving, which was recently published in the Evolutionary Computation Journal. In this work, we introduce informed down-sampled lexicase selection to dynamically build diverse subsets of training cases during evolution using population statistics. We evaluate our method on a set of program synthesis problems in two genetic programming systems and find that informed down-sampling improves performance in both systems compared to random down-sampling when using lexicase selection. Additionally, we investigate the constructed down-samples and find that informed down-sampling can identify important training cases and does so across different evolutionary runs and systems.", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Briggs:2006:ASPGP, title = "Functional genetic programming with combinators", author = "Forrest Briggs and Melissa O'Neill", booktitle = "Proceedings of the Third Asian-Pacific workshop on Genetic Programming", year = "2006", editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen", pages = "110--127", ISSN = "18590209", address = "Military Technical Academy, Hanoi, VietNam", keywords = "genetic algorithms, genetic programming", URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/forrest/fsb-meo-combs.pdf", URL = "http://gpbib.cs.ucl.ac.uk/aspgp06/fsb-meo-combs.pdf", size = "18 pages", abstract = "Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve combinator-expression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic programming with combinator expressions compares favourably to prior approaches, namely the works of Yu [37], Kirshenbaum [18], Agapitos and Lucas [1], Wong and Leung [35], Koza [20], Langdon [21], and Katayama [17].", notes = "broken march 2020 http://www.aspgp.org", } @Article{Briggs:2008:IJKBIES, author = "Forrest Briggs and Melissa O'Neill", title = "Functional Genetic Programming and Exhaustive Program Search with Combinator Expressions", journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems", year = "2008", volume = "12", number = "1", pages = "47--68", keywords = "genetic algorithms, genetic programming, Lambda-expressions", ISSN = "1327-2314", publisher = "IOS Press", URL = "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00140", DOI = "doi:10.3233/KES-2008-12105", size = "22 page", abstract = "Using a strongly typed functional programming language for genetic programming has many advantages, but evolving functional programs with variables requires complex genetic operators with special cases to avoid creating ill-formed programs. We introduce combinator expressions as an alternative program representation for genetic programming, providing the same expressive power as strongly typed functional programs, but in a simpler format that avoids variables and other syntactic clutter. We outline a complete genetic-programming system based on combinator expressions, including a novel generalised genetic operator, and also show how it is possible to exhaustively enumerate all well-typed combinator expressions up to a given size. Our experimental evidence shows that combinator expressions compare favourably with prior representations for functional genetic programming and also offers insight into situations where exhaustive enumeration outperforms genetic programming and vice versa.", notes = "Standard ML and Haskell mentioned a few times. even-N-parity, stacks and queues. KES", } @InProceedings{briney+karpinski:2003:gecco:workshop, title = "An Interdisciplinary Investigation of the Evolution and Maintenance of Conditional Strategies in Chthamalus anisopoma, using Genetic Programming and a Quantitative Genetic Model", author = "Kristin Briney and Tod Karpinski", pages = "258--261", booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference", editor = "Alwyn M. Barry", year = "2003", month = "11 " # jul, publisher = "AAAI", address = "Chigaco", publisher_address = "445 Burgess Drive, Menlo Park, CA 94025", notes = "Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming Conference (GP-2003) part of barry:2003:GECCO:workshop", keywords = "genetic algorithms, genetic programming", } @InProceedings{Brinster:2012:APSURSI, author = "Irina Brinster and Philippe {De Wagter} and Jason Lohn", booktitle = "Antennas and Propagation Society International Symposium (APSURSI), 2012 IEEE", title = "Evaluation of stochastic algorithm performance on antenna optimization benchmarks", year = "2012", isbn13 = "978-1-4673-0461-0", address = "Chicago, IL, USA", size = "2 pages", abstract = "This paper evaluates performance of ten stochastic search algorithms on a benchmark suite of four antenna optimisation problems. Hill climbers (HC) serve as baseline algorithms. We implement several variants of genetic algorithms, evolution strategies, and genetic programming as examples of competitive strategy for achieving optimal solution. Ant colony and particle-swarm optimisation represent cooperative strategy. Static performance is measured in terms of success rates and mean hit time, while dynamic performance is evaluated from the development of the mean solution quality. Among the evaluated algorithms, steady-state GA provides the best trade-off between efficiency and effectiveness. PSO is recommended for noisy problems, while ACO and GP should be avoided for antenna optimisations because of their low efficiencies.", keywords = "genetic algorithms, genetic programming, ant colony optimisation, antennas, particle swarm optimisation, search problems, stochastic processes, Hill climbers, ant colony optimisation, antenna optimisation benchmark, cooperative strategy, evolution strategies, particle swarm optimisation, steady-state GA, stochastic algorithm performance, stochastic search algorithm, Antennas, Arrays, Benchmark testing, Electromagnetics, Heuristic algorithms, Optimisation", DOI = "doi:10.1109/APS.2012.6348758", ISSN = "1522-3965", notes = "Also known as \cite{6348758}", } @InProceedings{brizuela:1999:ADSGAJSSP, author = "Carlos A. Brizuela and Nobuo Sannomiya", title = "A Diversity Study in Genetic Algorithms for Job Shop Scheduling Problems", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "75--82", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-333.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-333.ps", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InCollection{brock:1994:ers, author = "Oliver Brock", title = "Evolving Reusable Subroutines for Genetic Programming", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "11--19", address = "Stanford, California, 94305-3079 USA", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", ISBN = "0-18-182105-2", URL = "http://robotics.stanford.edu/users/oli/PAPERS/a-life.ps", URL = "http://citeseer.ist.psu.edu/156902.html", abstract = "Although automatically defined functions (ADFs) are able to significantly reduce the computational effort required in genetic programming, reasonably di{\AE}cult problems still require large amounts of computation time. However, every time genetic programming evolves a program to solve a problem those ADFs have to be rediscovered from scratch. If the ADFs of a correct program contain partial solutions that are generally useful, they can be used to solve similar problems. This paper proposes a technique to make the information of successful ADFs accessible to genetic programming in order to reduce the computational costs of solving related problems with less computational effort and demonstrates its utility using the example of the even n-parity function.", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html ADFS previously evolved may be used by subsequent GP runs. Ie become part of fitness set for rpb and adf of later runs.", } @InCollection{Broersma:2017:miller, author = "Hajo Broersma", title = "Evolution in Nanomaterio: The {NASCENCE} Project", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "4", pages = "87--111", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_4", abstract = "This chapter describes some of the work carried out by members of the NASCENCE project, an FP7 project sponsored by the European Community. After some historical notes and background material, the chapter explains how nanoscale material systems have been configured to perform computational tasks by finding appropriate configuration signals using artificial evolution. Most of this exposition is centred around the work that has been carried out at the MESA+ Institute for Nanotechnology at the University of Twente using disordered networks of nanoparticles. The interested reader will also find many pointers to references that contain more details on work that has been carried out by other members of the NASCENCE consortium on composite materials based on single-walled carbon nanotubes.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Misc{journals/corr/abs-2005-13110, author = "Clifford Broni-Bediako and Yuki Murata and Luiz Henrique Mormille and Masayasu Atsumi", title = "Evolutionary {NAS} with Gene Expression Programming of Cellular Encoding", howpublished = "arXiv", year = "2020", volume = "abs/2005.13110", keywords = "genetic algorithms, genetic programming, gene expression programming", bibdate = "2020-05-28", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-13110", URL = "https://arxiv.org/abs/2005.13110", } @InProceedings{Broni-Bediako:2020:SSCI, author = "Cliford Broni-Bediako and Yuki Murata and Luiz H. B. Mormille and Masayasu Atsumi", title = "Evolutionary NAS with Gene Expression Programming of Cellular Encoding", booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)", year = "2020", pages = "2670--2676", abstract = "The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme-symbolic linear generative encoding (SLGE)-simple, yet a powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via an evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using fewer GPU resources.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI47803.2020.9308346", month = dec, notes = "Also known as \cite{9308346}", } @InProceedings{Brookhouse:2014:GECCOcomp, author = "James Brookhouse and Fernando E. B. Otero and Michael Kampouridis", title = "Working with {OpenCL} to speed up a genetic programming financial forecasting algorithm: initial results", booktitle = "GECCO 2014 Workshop on Evolutionary Computation Software Systems (EvoSoft)", year = "2014", editor = "Stefan Wagner and Michael Affenzeller", isbn13 = "978-1-4503-2881-4", keywords = "genetic algorithms, genetic programming, GPU", pages = "1117--1124", month = "12-16 " # jul, organisation = "SIGEVO", address = "Vancouver, BC, Canada", URL = "https://kar.kent.ac.uk/42144/", URL = "http://doi.acm.org/10.1145/2598394.2605689", DOI = "doi:10.1145/2598394.2605689", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.", notes = "Also known as \cite{2605689} Distributed at GECCO-2014.", } @InProceedings{Brooks92RR9, title = "Artificial Life and Real Robots", year = "1992", pages = "3--10", author = "Rodney A. Brooks", booktitle = "Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life", editor = "Francisco J. Varela and Paul Bourgine", address = "Cambridge, MA, USA", publisher = "MIT Press", keywords = "genetic algorithms, genetic programming", URL = "http://people.csail.mit.edu/brooks/papers/real-robots.pdf", size = "9 pages", abstract = "The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behaviour-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.", } @InProceedings{Brotto-Rebuli:2021:EuroGP, author = "Karina {Brotto Rebuli} and Leonardo Vanneschi", title = "Progressive Insular Cooperative {GP}", booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming", year = "2021", editor = "Ting Hu and Nuno Lourenco and Eric Medvet", series = "LNCS", volume = "12691", publisher = "Springer Verlag", address = "Virtual Event", pages = "19--35", month = "7-9 " # apr, organisation = "EvoStar, Species", keywords = "genetic algorithms, genetic programming, Multiclass classification, Cooperative evolution", isbn13 = "978-3-030-72811-3", DOI = "doi:10.1007/978-3-030-72812-0_2", abstract = "This work presents a novel genetic programming system for multi-class classification, called progressively insular cooperative genetic programming (PIC GP). Based on the idea that effective multiclass classification can be obtained by appropriately joining classifiers that are highly specialized on the single classes, PIC GP evolves, at the same time, two populations. The first population contains individuals called specialists, and each specialist is optimized on one specific target class. The second population contains higher-level individuals, called teams, that join specialists to obtain the final algorithm prediction. By means of three simple parameters, PIC GP can tune the amount of cooperation between specialists of different classes. The first part of the paper is dedicated to a study of the influence of these parameters on the evolution dynamics. The obtained results indicate that PIC GP achieves the best performance when the evolution begins with a high level of cooperation between specialists of different classes, and then this type of cooperation is progressively decreased, until only specialists of the same class can cooperate between each other. The last part of the work is dedicated to an experimental comparison between PIC GP and a set of state-of-the-art classification algorithms. The presented results indicate that PIC GP outperforms the majority of its competitors on the studied test problems.", notes = "See also http://hdl.handle.net/10362/107369 Multi-class classification, team GP, multi-branch PIC GP, root is single non-evolving softmax node. Two parent selection. Non interacting island populations (insular). 3 class Iris. CIR=Cooperation Intensity Rate. http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021", } @InProceedings{rebuli:2022:GECCOcomp, author = "Karina {Brotto Rebuli} and Mario Giacobini and Niccolo Tallone and Leonardo Vanneschi", title = "A preliminary study of Prediction Interval Methods with Genetic Programming", booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion", year = "2022", editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson", pages = "530--533", address = "Boston, USA", series = "GECCO '22", month = "9-13 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, modelling uncertainty, crisp prediction, prediction interval", isbn13 = "978-1-4503-9268-6/22/07", DOI = "doi:10.1145/3520304.3528806", abstract = "This article presents an exploratory study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. A PI is the range of values in which the real target value is expected to fall into. It should combine two contrasting properties: to be as narrow as possible and to include as many data observations as possible. One proposed GP method, called CWC-GP, evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure that combines the width and the probability coverage of the PI. The other proposed GP method, called LUBE-GP, evolves independently the boundaries of the PI with a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with Direct and Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the presented preliminary results pave the way to further investigations. The most promising results were observed with the Sequential CWC-GP.", notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{Brotto-Rebuli:2022:WIVACE, author = "Karina {Brotto Rebuli} and Mario Giacobini and Niccolo Tallone and Leonardo Vanneschi", title = "Single and Multi-objective Genetic Programming Methods for Prediction Intervals", booktitle = "WIVACE 2022, XVI International Workshop on Artificial Life and Evolutionary Computation", year = "2022", editor = "Claudio {De Stefano} and Francesco Fontanella and Leonardo Vanneschi", volume = "1780", series = "Computer and Information Science", pages = "205--218", address = "Gaeta (LT), Italy", month = sep # " 14-16", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-031-31183-3", DOI = "doi:10.1007/978-3-031-31183-3_17", abstract = "A PI is the range of values in which the real target value of a supervised learning task is expected to fall into, and it should combine two contrasting properties: to be as narrow as possible, and to include as many data observations as possible. This article presents an study on modeling Prediction Intervals (PI) with two Genetic Programming(GP) methods. The first proposed GP method is called CWC-GP, and it evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure. This measure is the Coverage Width-based Criterion (CWC), which combines the width and the probability coverage of the PI. The second proposed GP method is called LUBE-GP, and it evolves independently the lower and upper boundaries of the PI. This method applies a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied both with the Direct and the Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the results pave the way to further investigations.", notes = "Published after the workshop. Use new author order. Supplementary material available at https://bit.ly/3zsRfGP http://wivace2022.unicas.it/files/programWIVACE2022.pdf", } @InProceedings{brotto-rebuli:2023:GECCOcomp, author = "Karina {Brotto Rebuli} and Mario Giacobini and Sara Silva and Leonardo Vanneschi", title = "A Comparison of Structural Complexity Metrics for Explainable Genetic Programming", booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year = "2023", editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen", pages = "539--542", address = "Lisbon, Portugal", series = "GECCO '23", month = "15-19 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, complexity metrics, explainable AI, XAI, interpretable models: Poster", isbn13 = "9798400701191", URL = "https://novaresearch.unl.pt/en/publications/a-comparison-of-structural-complexity-metrics-for-explainable-gen", URL = "https://novaresearch.unl.pt/files/67865641/Comparison_Structural_Complexity_Metrics_for_Explainable_Genetic_Programming.pdf", DOI = "doi:10.1145/3583133.3590595", size = "4 pages", abstract = "Genetic Programming (GP) has the potential to generate intrinsically explainable models. Despite that, in practice, this potential is not fully achieved because the solutions usually grow too much during the evolution. The excessive growth together with the functional and structural complexity of the solutions increase the computational cost and the risk of overfitting. Thus, many approaches have been developed to prevent the solutions to grow excessively in GP. However, it is still an open question how these approaches can be used for improving the interpretability of the models. This article presents an empirical study of eight structural complexity metrics that have been used as evaluation criteria in multi-objective optimisation. Tree depth, size, visitation length, number of unique features, a proxy for human interpretability, number of operators, number of non-linear operators and number of consecutive nonlinear operators were tested. The results show that potentially the best approach for generating good interpretable GP models is to use the combination of more than one structural complexity metric.", notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)", } @TechReport{broughton:1998:e3DwlsGPwww, author = "T. Broughton and P. Coates and H. Jackson", title = "Exploring {3D} design worlds using {Lindenmayer} systems and Genetic Programming", institution = "University of East London", year = "1998", keywords = "genetic algorithms, genetic programming", broken = "http://homepages.uel.ac.uk/0483p/chapter12.html", notes = "www info only", } @InCollection{broughton:1999:e3DwlsGPwww, author = "T. Broughton and Paul S. Coates and Helen Jackson", title = "Exploring Three-dimensional design worlds using {Lindenmeyer} Systems and Genetic Programming", booktitle = "Evolutionary Design Using Computers", publisher = "Academic press", year = "1999", editor = "Peter Bentley", chapter = "14", pages = "323--341", address = "London, UK", keywords = "genetic algorithms, genetic programming", ISBN = "0-12-089070-4", URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html", URL = "http://hdl.handle.net/10552/856", abstract = "The raw Lindenmeyer-system (L-system) generates random branching structures in the isospatial grid. Using a three dimensional L-system, early experiments (reported CAAD Futures 97, \cite{coates:1997:GPx3dw} ) showed that globally defined useful form (the flytrap) can evolve quite quickly using one fitness function This paper will describe further experiments undertaken using an improved L-system and multigoal evolution to evolve space/enclosure systems that satisfy both the requirements of space use and those of enclosure. This is implemented as symbiotic coevolution between: 1) L-system branching tree system whose goal is to surround the largest volume of empty space (defined as space which is 'invisible' to an outside observer). 2) Circulation system using walking three dimensional turtles to measure the spatial property of the enclosed space. The resulting enclosure phenotypes can be realised using the occupied isospatial grid points as nodes of a nurbs surface. The chapter covers: 1.0 Introduction to Genetic Programming, L-Systems and the Isospatial Grid 2.0 Three dimensional L-systems, production rules and s-expressions 3.0 Evolutionary Experiments in Simple Environments 4.0 Symbiotic Coevolution", notes = " ", } @Article{brown:1997:GPsoccer, author = "Janelle Brown", title = "AI, Teamwork is Goal of Robot Soccer Tourney", journal = "Wired News", year = "1997", volume = "5", number = "10", month = "3:04pm PDT 26 " # aug, keywords = "genetic algorithms, genetic programming", broken = "http://www.wired.com/culture/lifestyle/news/1997/08/6388", size = "1 page", abstract = "It's got all the excitement of real soccer, but without the bad haircuts and big egos. This week the Robot Soccer World Cup debuts at the International Joint Conferences on Artificial Intelligence in Japan. Matching robot against robot, RoboCup is making breakthroughs in artificial life and multi-agent collaboration, while providing a few kicks in the process.", notes = "Report on RoboCup robot competition (held at IJCAI 1997 in Nagoya, Japan) http://www.robocup.org/RoboCup/ see also \cite{luke:1997:csstcGP} and http://www.cs.umd.edu/users/seanl/soccerbots/", } @InProceedings{Brown:2010:ANNIE, author = "Joseph A. Brown and Daniel Ashlock", title = "Using Evolvable Regressors to Partition Data", booktitle = "ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks", year = "2010", editor = "Cihan H. Dagli", volume = "20", pages = "187--194", address = "St. Louis, Mo, USA", month = nov # " 1-3", organisation = "Smart Engineering Systems Laboratory, Systems Engineering Graduate Programs, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409 USA", publisher = "ASME", keywords = "genetic algorithms, genetic programming", isbn13 = "9780791859599", URL = "http://www.uoguelph.ca/~jbrown16/EvolRegress.pdf", URL = "https://asmedigitalcollection.asme.org/ebooks/book/149/chapter-abstract/30383/Using-Evolvable-Regressors-to-Partition-Data", DOI = "doi:10.1115/1.859599.paper24", abstract = "This manuscript examines permitting multiple populations of evolvable regressors to compete to be the best model for the largest number of data points. Competition between populations enables a natural process of specialisation that implicitly partitions the data. This partitioning technique uses function-stack based regressors and has the ability to discover the natural number of clusters in a data set via a process of sub-population collapse.", notes = "ASME Order Number: 859599", } @PhdThesis{Brown:thesis, author = "Joseph Alexander Brown", title = "Regression and Classification from Extinction", school = "School of Computer Science, The University of Guelph", year = "2014", address = "Canada", month = "10 " # jan, keywords = "genetic algorithms, genetic programming Bioinformatics", URL = "http://hdl.handle.net/10214/7793", URL = "https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793", URL = "https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/7793/Brown_Joseph_201401_PhD.pdf", URL = "http://genealogy.math.ndsu.nodak.edu/id.php?id=188278", size = "176 pages", abstract = "Evolutionary Algorithms use the principles of natural selection and biological evolution to act as search and optimisation tools. Two novel Spatially Structured Evolutionary Algorithms: the Multiple Worlds Model (MWM) and Multiple Agent Genetic Networks (MAGnet) are presented. These evolutionary algorithms create evolved unsupervised classifiers for data. Both have a property of subpopulation collapse, where a population/node receives little or no fitness implying the number of classes is too large. This property has the best biological analog of extinction. MWM has a number of evolving populations of candidate solutions. The novel fitness function selects one member from each population, and fitness is divided between. Each of these populations meets with the biological definition of a separate species; each is a group of organisms which produces offspring within their type, but not outside of it. This fitness function creates an unsupervised classification by partitioning the data, based on which population is of highest fitness, and creates an evolved classifier for that partition. MAGnet involves a number of evolving agents spread about a graph, the nodes of which contain individual data members or problem instances. The agents will in turn test their fitness on each of the neighbouring nodes in the graph, moving to the one where they have the highest fitness. During this move they may choose to take one of these problem instances with them. The agent then undergoes evolutionary operations based on which neighbours are on the node. The locations of the problem instances over time are sorted by the evolving agents, and the agents on a node act as a classifier", notes = "Advisor: Ashlock, Daniel", } @Article{Brown:2017:GP_Diablo, author = "Joseph Alexander Brown and Valtchan Valtchanov", title = "Tile Based Genetic Programming Generation for Diablo-like games", journal = "Seeds", year = "2017", volume = "2", pages = "89--93", keywords = "genetic algorithms, genetic programming, game", URL = "http://www.procjam.com/seeds/issues/2.pdf", abstract = "Diablo's initial pitch document highlights the use of Procedural Content as a prominent feature of the game: The heart of Diablo is the randomly created dungeon. A new dungeon level is generated...", notes = "http://www.procjam.com/seeds/ Seeds is a zine made by you, the PROCJAM community, ... Kickstarter", } @Article{Brown:2010:JCP, author = "W. Michael Brown and Aidan P. Thompson and Peter A. Schultz", title = "Efficient hybrid evolutionary optimization of interatomic potential models", journal = "Journal of Chemical Physics", year = "2010", volume = "132", number = "2", pages = "024108", keywords = "genetic algorithms, genetic programming, potential energy functions, search problems", ISSN = "1089-7690", DOI = "doi:10.1063/1.3294562", size = "13 pages", abstract = "The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimisation strategy intended for the automated development of material specific inter atomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorisation can help to address this issue. Finally, we show that with the use of this method, we are able to 'rediscover' the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.", notes = "34.20.Cf Department of Multiscale Dynamic Material Modeling, Sandia National Laboratories, Albuquerque, New Mexico 87185-1322, USA", } @InProceedings{browncribbs:1996:nand, author = "H. {Brown Cribbs III} and Robert E. Smith", title = "Classifier System Renaissance: New Analogies, New Directions", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "Classifier Systems, Genetic Algorithms", pages = "547--552", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap89.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", notes = "GP-96 Classifier paper", } @PhdThesis{CameronBrowne:thesis, author = "Cameron Browne", title = "Automatic Generation and Evaluation of Recombination Games", school = "Faculty of Information Technology, Queensland University of Technology", year = "2008", address = "Australia", month = feb, keywords = "genetic algorithms, genetic programming, Combinatorial, Games, Design, Aesthetics, Evolutionary, Search, Yavalath", URL = "http://www.cameronius.com/cv/publications/thesis-2.47.zip", size = "251 pages", abstract = "Many new board games are designed each year, ranging from the unplayable to the truly exceptional. For each successful design there are untold numbers of failures; game design is something of an art. Players generally agree on some basic properties that indicate the quality and viability of a game, however these properties have remained subjective and open to interpretation. The aims of this thesis are to determine whether such quality criteria may be precisely defined and automatically measured through self-play in order to estimate the likelihood that a given game will be of interest to human players, and whether this information may be used to direct an automated search for new games of high quality. Combinatorial games provide an excellent test bed for this purpose as they are typically deep yet described by simple well defined rule sets. To test these ideas, a game description language was devised to express such games and a general game system implemented to play, measure and explore them. Key features of the system include modules for measuring statistical aspects of self-play and synthesising new games through the evolution of existing rule sets. Experiments were conducted to determine whether automated game measurements correlate with rankings of games by human players, and whether such correlations could be used to inform the automated search for new high quality games. The results support both hypotheses and demonstrate the emergence of interesting new rule combinations.", notes = "Reviewed by \cite{Althoefer:2010:ICGA}", } @Book{CameronBrowne:book, author = "Cameron Browne", title = "Evolutionary Game Design", publisher = "Springer", year = "2011", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4471-2178-7", URL = "http://www.springer.com/computer/ai/book/978-1-4471-2178-7", DOI = "doi:10.1007/978-1-4471-2179-4", size = "122 pages", abstract = "This book tells the story of Yavalath, the first computer-generated board game to be commercially released... Table of contents Introduction Games in General The Ludi System Measuring Games Evolving Games Viable Games Yavalath Conclusion", notes = "Softcover. Reviewed in \cite{Boumaza:2012:GPEM}. Winner 2012 HUMIES GECCO 2012 ", } @Article{Browne:2012:ICGA.Yavalath, author = "Cameron Browne", title = "Yavalath: Sample chapter from Evolutionary Game Design", journal = "ICGA Journal", year = "2012", volume = "35", number = "1", keywords = "genetic algorithms, genetic programming", URL = "https://chessprogramming.wikispaces.com/ICGA+Journal", notes = "Winner 2012 HUMIES GECCO 2012 http://www.genetic-programming.org/hc2012/Browne-Paper-1-Preface.pdf https://www.imperial.ac.uk/news/112191/doc-ra-wins-2012-prestigious-humies/", } @Article{Browne_2012_sigevolution, author = "Cameron Browne", title = "Evolutionary Game Design: Automated Game Design Comes of Age", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2012", volume = "6", number = "2", pages = "3--15", keywords = "genetic algorithms, genetic programming, LUDI, game description language GDL", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf", DOI = "doi:10.1145/2597453.2597454", size = "13 pages", abstract = "The HUMIES awards are an annual competition held in conjunction with the Genetic and Evolutionary Computation Conference (GECCO), in which cash prizes totalling 10,000 USA dollars are awarded to the most human-competitive results produced by any form of evolutionary computation published in the previous year. This article describes the gold medal-winning entry from the 2012 Humies competition, based on the LUDI system for playing, evaluating and creating new board games. LUDI was able to demonstrate human competitive results in evolving novel board games that have gone on to be commercially published, one of which, Yavalath, has been ranked in the top 2.5percent of abstract board games ever invented. Further evidence of human-competitiveness was demonstrated in the evolved games implicitly capturing several principles of good game design, outperforming human designers in at least one case, and going on to inspire a new subgenre of games.", notes = "18 Feb 2014. Nestorgames, 2 and 3 player board hex game. Ndengrod=Pentalath 2009, no ko.", } @Misc{browne:1996:bsc, author = "David Browne", title = "Vision-Based Obstacle Avoidance: A Coevolutionary Approach", school = "Department of Software Development, Monash University", year = "1996", type = "Bachelor of Computing with Honours", address = "Australia", month = oct, keywords = "genetic algorithms, genetic programming", URL = "http://www.csse.monash.edu.au/hons/projects/1996/David.Browne/", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/browne/browne_thesis.ps.gz", size = "147 pages", abstract = "This thesis investigates the design of robust obstacle avoidance strategies. Specifically, simulated coevolution is used to breed steering agents and obstacle courses in a `computational arms race'. Both steering agent strategies and obstacle courses are represented by computer programs, and are coevolved according to the genetic programming paradigm. Previous research has found it difficult to evolve robust vision based obstacle avoidance agents. By independently evolving obstacle avoidance agents against a competing evolving species (ie the obstacle courses), it is hypothesised that the robustness of the agents will be increased. The simon system, an existing genetic programming tool, is modified and used to evolve both the obstacle avoidance agents and the obstacle courses. A comparison is made between the robustness of coevolved obstacle avoidance agents and traditionally evolved (non-coevolved) agents. Robustness is measured by average performance in a series of randomly generated obstacle courses. Experimental results show that the average robustness of the coevolved oa agents is greater than that of the traditionally evolved, and statistically it is shown that this data is representative of all cases. It is therefore concluded that coevolution is applicable to oa type problems, and can be used to evolve more robust, general purpose Vision-Based Obstacle Avoidance agents.", } @Article{Browne:2010:ACISC, author = "Nigel P. A. Browne and Marcus V. {dos Santos}", title = "Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming", journal = "Applied Computational Intelligence and Soft Computing", year = "2010", volume = "2010", pages = "Article ID 409045", keywords = "genetic algorithms, genetic programming, gene expression programming", URL = "http://downloads.hindawi.com/journals/acisc/2010/409045.pdf", DOI = "doi:10.1155/2010/409045", size = "19 pages", abstract = "Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.", } @InProceedings{Browne:2016:GECCOcomp, author = "Will N. Browne", title = "Code Fragments: Past and Future use in Transfer Learning", booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation", year = "2016", editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing", isbn13 = "978-1-4503-4323-7", pages = "1405--1405", address = "Denver, Colorado, USA", month = "20-24 " # jul, keywords = "genetic algorithms, genetic programming", organisation = "SIGEVO", DOI = "doi:10.1145/2908961.2931737", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Code Fragments (CFs) have existed as an extension to Evolutionary Computation, specifically Learning Classifiers Systems (LCSs), for half a decade. Through the scaling, abstraction and reuse of both knowledge and functionality that CFs enable, interesting problems have been solved beyond the capability of any other technique. This paper traces the development of the different CF-based systems and outlines future research directions that will form the basis for advanced Transfer Learning in LCSs.", notes = "Distributed at GECCO-2016.", } @Article{Brownlee:2017:ieeeETCI, author = "Alexander Edward Ian Brownlee and Nathan Burles and Jerry Swan", title = "Search-Based Energy Optimization of Some Ubiquitous Algorithms", journal = "IEEE Transactions on Emerging Topics in Computational Intelligence", year = "2017", volume = "1", number = "3", pages = "188--201", month = jun, keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Energy, Java", ISSN = "2471-285X", URL = "http://eprints.whiterose.ac.uk/117916/1/07935484_1.pdf", DOI = "doi:10.1109/TETCI.2017.2699193", size = "14 pages", abstract = "Reducing computational energy consumption is of growing importance, particularly at the extremes (i.e., mobile devices and datacentres). Despite the ubiquity of the Java virtual machine (JVM), very little work has been done to apply search-based software engineering (SBSE) to minimize the energy consumption of programs that run on it. We describe OPACITOR, a tool for measuring the energy consumption of JVM programs using a bytecode level model of energy cost. This has several advantages over time-based energy approximations or hardware measurements. It is 1) deterministic, 2) unaffected by the rest of the computational environment, 3) able to detect small changes in execution profile, making it highly amenable to metaheuristic search, which requires locality of representation. We show how generic SBSE approaches coupled with OPACITOR achieve substantial energy savings for three widely used software components. Multilayer perceptron implementations minimizing both energy and error were found, and energy reductions of up to 70percent and 39.85percent were obtained over the original code for Quicksort and object-oriented container classes, respectively. These highlight three important considerations for automatically reducing computational energy: tuning software to particular distributions of data; trading off energy use against functional properties; and handling internal dependencies that can exist within software that render simple sweeps over program variants sub-optimal. Against these, global search greatly simplifies the developer's job, freeing development time for other tasks.", notes = "Also known as \cite{7935484}", } @InProceedings{Brownlee:2018:GECCOcomp, author = "Alexander E. I. Brownlee and John R. Woodward and Nadarajen Veerapen", title = "Relating training instances to automatic design of algorithms for bin packing via features", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2018", editor = "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco {Chavez de la O} and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar", isbn13 = "978-1-4503-5764-7", pages = "135--136", address = "Kyoto, Japan", DOI = "doi:10.1145/3205651.3205748", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, automatic design of algorithms, bin packing, features", acmid = "3205748", size = "2 pages", abstract = "Automatic Design of Algorithms (ADA) treats algorithm choice and design as a machine learning problem, with problem instances as training data. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply genetic programming ADA for bin packing to several new and existing benchmark sets. Using sets with narrowly-distributed features for training results in highly specialised algorithms, whereas those with well-spread features result in very general algorithms. Variance in certain features has a strong correlation with the generality of the trained policies.", notes = "Also known as \cite{3205748} Also known as \cite{Brownlee:2018:RTI:3205651.3205748} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Brownlee:2019:GECCO, author = "Alexander E. I. Brownlee and Justyna Petke and Brad Alexander and Earl T. Barr and Markus Wagner and David R. White", title = "{Gin}: genetic improvement research made easy", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2019", editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre", isbn13 = "978-1-4503-6111-8", pages = "985--993", address = "Prague, Czech Republic", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based Software Engineering, SBSE, Software engineering, Software notations and tools, GI", URL = "https://cs.adelaide.edu.au/users/markus/pub/2019gecco-gintool.pdf", DOI = "doi:10.1145/3321707.3321841", size = "9 pages", abstract = "Genetic improvement (GI) is a young field of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modifiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code profiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI.", notes = "https://github.com/gintool/gin Slides: http://crest.cs.ucl.ac.uk/fileadmin/crest/COWphotos/Other/Petke.pdf Computing Science and Mathematics, University of Stirling, Stirling, UK. Also known as \cite{3321841} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Brownlee:2020:CEC, author = "Alexander Brownlee and Justyna Petke and Anna F. Rasburn", title = "Injecting Shortcuts for Faster Running {Java} Code", booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)", year = "2020", month = jul # " 19-24", editor = "Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Justyna Petke and John R. Woodward", publisher = "IEEE", address = "Internet", note = "Special Session on Genetic Improvement", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, SBSE, GIN", isbn13 = "978-1-7281-6929-3", URL = "http://geneticimprovementofsoftware.com/paper_pdfs/E-24667.pdf", URL = "https://dspace.stir.ac.uk/bitstream/1893/30963/1/InjectingShortcutsCEC2020.pdf", URL = "http://hdl.handle.net/1893/30963", DOI = "doi:10.1109/CEC48606.2020.9185708", size = "8 pages", abstract = "Genetic Improvement of software applies search methods to existing software to improve the target program in some way. Impressive results have been achieved, including substantial speedups, using simple operations that replace, swap and delete lines or statements within the code. Often this is achieved by specialising code, removing parts that are unnecessary for particular use-cases. Previous work has shown that there is a great deal of potential in targeting more specialised operations that modify the code to achieve the same functionality in a different way. We propose six new edit types for Genetic Improvement of Java software, based on the insertion of break, continue and return statements. The idea is to add shortcuts that allow parts of the program to be skipped in order to speed it up. 10000 randomly-generated instances of each edit were applied to three open-source applications taken from GitHub. The key findings are: (1) compilation rates for inserted statements without surrounding if statements are 1.5 to 18.3percent; (2) edits where the insert statement is embedded within an if have compilation rates of 3.2 to 55.8percent; (3) of those that compiled, all 6 edits have a high rate of passing tests (Neutral Variant Rate), >60percent in all but one case, and so have the potential to be performance improving edits. Finally, a preliminary experiment based on local search shows how these edits might be used in practice.", notes = "jCodec, spark, spatial4j. Profile wtih hprof to find hot methods and their unit tests. Target plastic code in hot methods with local search. memory caching? NVR = Neutral Variant Rate. Speedup. https://github.com/gintool/gin http://geneticimprovementofsoftware.com/events/wcci2020 WCCI2020", } @InProceedings{Brownlee:2021:GI, author = "Alexander E. I. Brownlee and Jason Adair and Saemundur O. Haraldsson and John Jabbo", title = "Exploring the Accuracy -- Energy Trade-off in Machine Learning", booktitle = "GI @ ICSE 2021", year = "2021", month = "30 " # may, editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon", publisher = "IEEE", address = "internet", pages = "11--18", keywords = "genetic algorithms, genetic programming, genetic improvement, AI, ML, PyRAPL, NSGA-II jMetalPy", isbn13 = "978-1-6654-4466-8/21", URL = "https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/brownlee_gi-icse_2021.pdf", video_url = "https://www.youtube.com/watch?v=bap72BF9vZw&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=7", video_url = "https://www.youtube.com/watch?v=tgRV-AsVYko&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=8", video_url = "https://www.youtube.com/watch?v=tB3IWHT4FT4&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=4", DOI = "doi:10.1109/GI52543.2021.00011", size = "8 pages", abstract = "Machine learning accounts for considerable global electricity demand and resulting environmental impact, as training a large deep-learning model produces 284000 kgs of the greenhouse gas carbon dioxide. In recent years, search-based approaches have begun to explore improving software to consume less energy. Machine learning is a particularly strong candidate for this because it is possible to trade off functionality (accuracy) against energy consumption, whereas with many programs functionality is simply a pass-or-fail constraint. We use a grid search to explore hyperparameter configurations for a multilayer perceptron on five classification data sets, considering trade-offs of classification accuracy against training or inference energy. On one data set, we show that 77 percent of energy consumption for inference can saved by reducing accuracy from 94.3 percent to 93.2 percent. Energy for training can also be reduced by 30-50 percent with minimal loss of accuracy. We also find that structural parameters like hidden layer size is a major driver of the energy-accuracy trade-off, but there is some evidence that non-structural hyperparameters influence the trade-off too. We also show that a search-based approach has the potential to identify these trade-offs more efficiently than the grid search.", notes = "data etc at http://hdl.handle.net/11667/173 Energy cost versus accuracy tradeoff, eg for tanh, relu, logistic regression. Hidden layer size (only on glass dataset). Solver (high impact). Model size, esp for online inference (eg for IoT). Video tB3IWHT4FT4 Alexander E.I. Brownlee. 15:14 Discussion chair: Yu Huang Q: Alexandre Bergel, A: Alexander E.I. Brownlee, pyRAPL easy. 16:01 Q: Westley Weimer, A: learning algorithm 14:34 W. B. Langdon A: pyRAPL v. nanoseconds v. Markus Wagner energy measurements in Joules. 19:00 Approximate computing 19:37 Yu Huang, A: python (SciKit Learn) v. Weka. 20:35 Yu Huang problems, A: noise. SE for AI. part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html", } @Article{Brownlee:2021:SEN, author = "Alexander E. I. Brownlee", title = "{Genetic Improvement @ ICSE 2021}: Personal reflection of a Workshop Participant", journal = "SIGSOFT Software Engineering Notes", year = "2021", volume = "46", number = "4", month = oct, pages = "28--30", keywords = "genetic algorithms, genetic programming, genetic improvement", publisher = "Association for Computing Machinery", ISSN = "0163-5948", URL = "https://doi.org/10.1145/3485952.3485960", DOI = "doi:10.1145/3485952.3485960", size = "3 pages", abstract = "Following Dr. Stephanie Forrest of Arizona State University keynote \cite{Forrest:2021:GI} presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021) \cite{Petke:2021:ICSEworkshop}. Topics included a growing range of target systems and applications, algorithmic improvements, wide-ranging questions about how other fields (especially evolutionary computation) can inform advances in GI, and about how GI is branded to other disciplines. We give a personal perspective on the workshop proceedings, the discussions that took place, and resulting prospective directions for future research.", notes = "GI-2021 ACM SIGSOFT Software Engineering Notes https://dl.acm.org/newsletter/sigsoft/ University of Stirling, UK", } @InProceedings{Brownlee:2023:SSBSE, author = "Alexander E. I. Brownlee and James Callan and Karine Even-Mendoza and Alina Geiger and Carol Hanna and Justyna Petke and Federica Sarro and Dominik Sobania", title = "Enhancing Genetic Improvement Mutations Using Large Language Models", booktitle = "SSBSE 2023: Challenge Track", year = "2023", editor = "Paolo Arcaini and Tao Yue and Erik Fredericks", organisers = "Erik Fredericks and Paolo Arcaini and Tao Yue and Rebecca Moussa and Thomas Vogel and Gregory Gay and Max Hort and Bobby R. Bruce and Jose Miguel Rojas and Vali Tawosi", volume = "14415", series = "LNCS", pages = "153--159", address = "San Francisco, USA", month = "8 " # dec, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, GI, GIN, SBSE, LLM, AI, ANN, OpenAI, Langchain4J, Java, jCodec", isbn13 = "978-3-031-48795-8", URL = "https://arxiv.org/pdf/2310.19813.pdf", URL = "https://kclpure.kcl.ac.uk/portal/en/publications/enhancing-genetic-improvement-mutations-using-large-language-mode", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Brownlee_2023_SSBSEchallenge.pdf", code_url = "https://doi.org/10.5281/zenodo.8304433", code_url = "http://github.com/gintool/gin", DOI = "doi:10.1007/978-3-031-48796-5_13", size = "7 pages", abstract = "Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI tool kit to call OpenAI API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75percent higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI", notes = "See also \cite{Brownlee:2024:ASE} Gin profiler co-located with ESEC/FSE 2023. https://conf.researchr.org/track/ssbse-2023/ssbse-2023-challenge#Accepted-papers%gismo", } @InProceedings{brownlee:2024:GECCOcomp, author = "Alexander Edward Ian Brownlee and Saemundur Oskar Haraldsson and John Robert Woodward and Markus Wagner", title = "Genetic Improvement: Taking real-world source code and improving it using computational search methods", booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion", year = "2024", editor = "Mengjie Zhang and Emma Hart", pages = "1197--1230", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", note = "Tutorial", keywords = "genetic algorithms, genetic programming, genetic improvement", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3648418", size = "34 pages", notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @Article{Brownlee:2024:ASE, author = "Alexander Edward Ian Brownlee and James Callan and Karine Even-Mendoza and Alina Geiger and Carol Hanna and Justyna Petke and Federica Sarro and Dominik Sobania", title = "Large Language Model Based Mutations in Genetic Improvement", journal = "Automated Software Engineering", year = "2025", volume = "15", pages = "article number 15", note = "Special Issue on Advances in Search-Based Software", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, GIN, LLM, AI, ANN, JavaParser, JVM, stochastic local search", ISSN = "0928-8910", URL = "https://rdcu.be/d67YW", URL = "https://doi.org/10.21203/rs.3.rs-4437272/v1", DOI = "doi:10.1007/s10515-024-00473-6", data_url = "https://zenodo.org/records/11173088", size = "25 pages", abstract = "we evaluate the use of LLMs as mutation operators for genetic improvement (GI), an SBSE approach, to improve the GI search process. In a preliminary work, we explored the feasibility of combining the Gin Java GI toolkit with OpenAI LLMs in order to generate an edit for the JCodec tool. Here we extend this investigation involving three LLMs and three types of prompt, and five real-world software projects. We sample the edits at random, as well as using local search. Our results show that, compared with conventional statement GI edits, LLMs produce fewer unique edits, but these compile and pass tests more often, with the OpenAI model finding test-passing edits 77percent of the time. The OpenAI and Mistral LLMs were roughly equal in finding the best run-time improvements. Simpler prompts were more successful than those providing more context and examples. Qualitative analysis revealed a wide variety of areas where LLMs typically failed to produce valid edits: commonly including inconsistent formatting, generating non-Java syntax, or refusing to provide a solution", notes = "Extends \cite{Brownlee:2023:SSBSE} ThreadMXBean.getThreadCPUTime, Langchain4J, TinyDolphin, Mistral, small changes prompt, structural changes prompt. JCodec, JUnit4, Gson, Commons-Net, Karate. Profiling. Maven 3.9.x. Sect 5.1 'over 800 valid patches were found' Sect 8 'up to 9 times more [than with random] patches passing the unit tests were found with LLM-based edits.' 'the prompts used for LLM requests greatly affect the results' ", } @InProceedings{bruce2015reducing, author = "Bobby R. Bruce and Justyna Petke and Mark Harman", title = "Reducing Energy Consumption Using Genetic Improvement", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terrence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Keswsentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "1327--1334", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", publisher = "ACM", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, Genetic Improvement, GI, SBSE, Search-Based Software Engineering and Self-* Search, Software Engineering, optimisation, energy optimisation, energy efficiency, energy consumption, Boolean satisfiability, SAT", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2015_GECCO.pdf", URL = "http://doi.acm.org/10.1145/2739480.2754752", DOI = "doi:10.1145/2739480.2754752", size = "8 pages", abstract = "Genetic Improvement (GI) is an area of Search Based Software Engineering which seeks to improve software's non-functional properties by treating program code as if it were genetic material which is then evolved to produce more optimal solutions. Hitherto, the majority of focus has been on optimising program's execution time which, though important, is only one of many non-functional targets. The growth in mobile computing, cloud computing infrastructure, and ecological concerns are forcing developers to focus on the energy their software consumes. We report on investigations into using GI to automatically find more energy efficient versions of the MiniSAT Boolean satisfiability solver when specialising for three downstream applications. Our results find that GI can successfully be used to reduce energy consumption by up to 25percent", notes = "Also known as \cite{Bruce:2015:GECCO} \cite{2754752} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Bruce:2015:gi, author = "Bobby R. Bruce", title = "Energy Optimisation via Genetic Improvement A {SBSE} technique for a new era in Software Development", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "819--820", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, GI, Search Based Software En-gineering, energy efficiency, energy consumption, energy optimisation", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/energy_optimisation_via_genetic_improvement.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768420", DOI = "doi:10.1145/2739482.2768420", size = "2 pages", abstract = "The discipline of Software Engineering has arisen during a time in which developers rarely concerned themselves with the energy efficiency of their software. Due to the growth in both mobile devices and large server clusters this period is undoubtedly coming to an end and, as such, new tools for creating energy-efficient software are required. This paper takes the position that Genetic Improvement, a Search-Based Software Engineering technique, has the potential to aid developers in refactoring their software to a more energy-efficient state; allowing focus to remain on functional requirements while leaving concerns over energy consumption to an automated process.", notes = "Slides: http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/bruce/EnergyReductionViaGeneticImprovement.pdf position paper", } @Article{Bruce:2016:sigevolution, author = "Bobby Bruce", title = "A Report on the Genetic Improvement Workshop@GECCO 2016", journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation", year = "2016", volume = "2", number = "9", pages = "7", month = aug, keywords = "genetic algorithms, genetic programming, Genetic Improvement", ISSN = "1931-8499", URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution0902.pdf", DOI = "doi:10.1145/3066157.3066159", size = "1 page", } @InProceedings{Bruce:2016:SSBSE, author = "Bobby R. Bruce and Jonathan M. Aitken and Justyna Petke", title = "Deep Parameter Optimisation for Face Detection Using the {Viola-Jones} Algorithm in {OpenCV}", booktitle = "Proceedings of the 8th International Symposium on Search Based Software Engineering, SSBSE 2016", year = "2016", editor = "Federica Sarro and Kalyanmoy Deb", volume = "9962", series = "LNCS", pages = "238--243", address = "Raleigh, North Carolina, USA", month = "8-10 " # oct, publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, OpenCV, NSGA-II, Deep parameter optimisation, Automated parameter tuning, Multi-objective optimisation, GI", isbn13 = "978-3-319-47106-8", URL = "http://www.cs.ucl.ac.uk/staff/J.Petke/papers/Bruce_2016_SSBSE.pdf", DOI = "doi:10.1007/978-3-319-47106-8_18", code_url = "https://github.com/BobbyRBruce/DPT-OpenCV", size = "6 pages", abstract = "OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a trade-off between execution time and classification accuracy. Our results show that execution time can be decreased by 48 percent if a 1.80 percent classification inaccuracy is permitted (compared to 1.04 percent classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user.", notes = "See correction \cite{bruce:RN1707} Tarball of repository is available http://www.cs.ucl.ac.uk/staff/R.Bruce/dpt_opencv.tar.gz", } @TechReport{bruce:RN1701, author = "Bobby R. Bruce and Justyna Petke and Mark Harman and Earl T. Barr", title = "Approximate Oracles and Synergy in Software Energy Search Spaces", institution = "University College, London", year = "2017", number = "RN/17/01", address = "London, UK", month = "25 " # jan, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_01.PDF", abstract = "There is a growing interest in using evolutionary computation to reduce software systems' energy consumption by using techniques such as genetic improvement. However, efficient and effective evolutionary optimisation of software systems requires a better understanding of the energy search landscape. One important choice practitioners have is whether to preserve the system's original output or permit approximation; each of which has its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by evolutionary mutation is 2.69percent (0.76percent on average). By contrast, this figure increases dramatically to 95.60percent (33.90percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for effective evolutionary optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied evolutionary mutations produce a effect greater than their individual sum. Our results reveal that 12.0percent of all joint code modifications produced such a synergistic effect though 38.5percent produce an antagonistic interaction in which simultaneously applied mutations are less effective than when applied individually. This highlights the need for an evolutionary approach over more greedy alternatives.", notes = "Replaced by \cite{Bruce:TSE}", size = "21 pages", } @TechReport{bruce:RN1707, author = "Bobby R. Bruce", title = "Deep Parameter Optimisation for Face Detection Using the {Viola-Jones} Algorithm in {OpenCV} : A Correction", institution = "University College, London", year = "2017", number = "RN/17/07", address = "London, UK", month = "7 " # jun, keywords = "genetic algorithms, genetic programming, genetic improvement", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_17_07.pdf", abstract = "In our 2016 paper 'Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV' \cite{Bruce:2016:SSBSE} we reported on an evolutionary, multi-objective approach to deep parameter optimisation that we reported could reduce execution time of a face detection algorithm by 48percent if a 1.90percent classification inaccuracy were permitted (compared to the 1.04percent classification inaccuracy of the original, unmodified algorithm) and that further execution time savings were possible depending on the degree of inaccuracy permitted by the user. However, after publication we found an error in our experimental setup; instead of running the deep parameter optimisation framework using an evolutionary search-based approach we had been using a systematic one. We therefore re-ran the experiments using the intended evolutionary implementation alongside the systematic implementation for 1000 evaluations and again for 10000 evaluations. We found that the systematic setup is superior to the intended evolutionary setup in that it produces solutions which, when run on the test set, produce a richer Pareto frontier. The evolutionary approach, in the 10000 evaluation setup, produced a better Pareto frontier on the training set. However, the majority of these solutions were infeasible or not Pareto optimal when run on the test set. We suspect this may be due to the evolutionary approach over-fitting to the training set.", notes = "\cite{Bruce:2016:SSBSE} makes reference to now non-existent Github page: https://github.com/BobbyBruce1990/DPT-OpenCV see instead https://github.com/BobbyRBruce/DPT-OpenCV Tarball of repository is available http://www.cs.ucl.ac.uk/staff/R.Bruce/dpt_opencv.tar.gz", size = "7 pages", } @TechReport{Bruce:RN1804, author = "Bobby R. Bruce and Justyna Petke", title = "Towards automatic generation and insertion of {OpenACC} directives", institution = "University College, London", year = "2018", number = "RN/18/04", address = "London, UK", month = "12 " # apr, keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, parallel computing", URL = "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_18_04.pdf", size = "35 pages", abstract = "While the use of hardware accelerators, like GPUs, can significantly improve software performance, developers often lack the expertise or time to properly translate source code to do so. In this paper we highlight two approaches to automatically offload computationally intensive tasks to a system's GPU by generating and inserting OpenACC directives; one using grammar-based genetic programming, and another using a bespoke four stage process. We find that the grammar-based genetic programming approach reduces execution time by 2.60percent on average, across the applications studied, while the bespoke four-stage approach reduces execution time by 2.44percent. Despite this, our investigation shows a handwritten OpenACC implementation is capable of reducing execution time by 65.68percent. Comparing the differences, we identified a promising avenue for future research: combining genetic improvement with better handling of data to and from the GPU.", notes = "NAS Parallel Benchmark suite GB-GP-Parallelisation and Four-Stage-Parallelisation 'both under-perform due to their poor handling of how program variables are transferred to and from the GPU' grammar-based GP. Greedy algorithms and evolutionary strategies", } @PhdThesis{bruce_bobby_r_thesis, author = "Bobby R. Bruce", title = "The Blind Software Engineer: Improving the Non-Functional Properties of Software by Means of Genetic Improvement", school = "Computer Science, University College, London", year = "2018", address = "UK", month = "12 " # jul, keywords = "genetic algorithms, genetic programming, Genetic Improvement, Search-based Software Engineering, OpenCV, Deep Parameter Optimisation, Android Smartphones, GB-GP-Parallelisation, OpenACC, DawnCC, NAS-NPB Suite, SNU-NPB", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce_bobby_r_thesis.pdf", URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756138", URL = "https://discovery.ucl.ac.uk/id/eprint/10052290/", size = "151 pages", abstract = "Life, even in its most basic of forms, continues to amaze mankind with the complexity of its design. When analysing this complexity it is easy to see why the idea of a grand designer has been such a prevalent idea in human history. If it is assumed intelligence is required to undertake a complex engineering feat, such as developing a modern computer system, then it is logical to assume a creature, even as basic as an earthworm, is the product of an even greater intelligence. Yet, as Darwin observed, intelligence is not a requirement for the creation of complex systems. Evolution, a phenomenon without consciousness or intellect can, over time, create systems of grand complexity and order. From this observation a question arises: is it possible to develop techniques inspired by Darwinian evolution to solve engineering problems without engineers? The first to ask such a question was Alan Turing, a person considered by many to be the father of computer science. In 1948 Turing proposed three approaches he believed could solve complex problems without the need for human intervention. The first was a purely logicdriven search. This arose a decade later in the form of general problem-solving algorithms. Though successful in solving toy problems which could be sufficiently formalised, solving real-world problems was found to be infeasible. The second approach Turing called cultural search. This approach would store libraries of information to then reference and provide solutions to particular problems in accordance to this information. This is similar to what we would now refer to as an expert system. Though the first expert system is hard to date due to differences in definition, the development is normally attributed to Feigenbaum, Bachanan, Lederberg, and Sutherland for their work, originating in the 1960s, on the DENRAL system. Turings last proposal was an iterative, evolutionary technique which he later expanded on stating: We cannot expect to find a good child-machine at the first attempt. One must experiment with teaching one machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution. Though a primitive proposal in comparison to modern techniques, Turing clearly identified the foundation of what we now refer to as Evolutionary Computation (EC). EC borrows principles from biological evolution and adapts them for use in computer systems. Despite EC initially appearing to be an awkward melding between the two perpendicular disciplines of biology and computer science, useful ideas from evolutionary theory can be used in engineering processes. Just as man dreamt of flight from watching birds, EC researchers dream of self-improving systems from observing evolutionary processes. Despite these similarities, evolutionary inspired techniques in computer science have yet to build complex software systems from scratch. Though they have been successfully used to solve complex problems, such as classification and clustering, there is a general acceptance that, as in nature, these evolutionary processes take vast amounts of time to create complex structures from simple starting points. Even the best computer systems cannot compete with natures ability to evaluate many millions of variants in parallel over the course of millennia. It is for this reason research into modifying and optimising already existing software, a process known as Genetic Improvement, has blossomed. Genetic Improvement (commonly referred to as GI) modifies existing software using search-based techniques with respect to some objective. These search-based techniques are typically evolutionary and, if not, are based on iterative improvement which we may view as a form of evolution. GI sets out to solve the last mile problems of software development; problems that arise in software engineering close to completion, such as bugs or sub-optimal performance. It is the genetic improvement of non-functional properties, such as execution time and energy consumption, which we concern ourselves with in this thesis, as we find it to be the area of research which is the most interesting, and the most exciting. It is hoped that those referencing this thesis may share the same vision: that the genetic improvement of non-functional properties has the potential to transform software development, and that the work presented here is a step towards that goal. The thesis is divided into six chapters (inclusive of this Introduction chapter). In Chapter 2 we explain the background material necessary to understand the content discussed later in the following chapters. From this, in Chapter 3, we highlight our investigations into the novel nonfunctional property of energy consumption which, in part, includes a study in how energy may be reduced via the approximation of output. We then expand on this in Chapter 4 by discussing our investigations into the applicability of GI in the domain of approximate computing, which covers a study into optimising the non-functional properties of software running on novel hardware: in this case, Android tablet devices. We then show, in Chapter 5, early research into how GI may be used to specialise software for specific hardware targets; in particular, how GI may automatically modify sequential code to run on GPUs. Finally, in Chapter 6 we discuss what relevant work is currently being undertaken by using the area of genetic improvement, and provide the reader with clear and concise take-away messages from this thesis.", notes = "Java physics library Rebound, Solver.c, MiniSAT, LzmaEnc.c, LzFind.c ISNI: 0000 0004 7429 091X Supervisory team: Justyna Petke, Mark Harman, Earl T. Barr.", } @Article{Bruce:TSE, author = "Bobby R. Bruce and Justyna Petke and Mark Harman and Earl T. Barr", journal = "IEEE Transactions on Software Engineering", title = "Approximate Oracles and Synergy in Software Energy Search Spaces", year = "2019", volume = "45", number = "11", pages = "1150--1169", month = nov, keywords = "genetic algorithms, genetic programming, genetic improvement, search-based software engineering, SBSE, synergy, Energy consumption, Energy measurement, antagonism, oracle, approximation, MAGEEC, Raspberry Pi", ISSN = "0098-5589", URL = "http://www.bobbybruce.net/assets/pdfs/publications/bruce-2019-approximate.pdf", URL = "https://pdfs.semanticscholar.org/83d3/685a11e8f4855047dd3fba11a67b45aab935.pdf", URL = "https://ieeexplore.ieee.org/document/8338061/", DOI = "doi:10.1109/TSE.2018.2827066", size = "20 pages", abstract = "Reducing the energy consumption of software systems though optimisations techniques such as genetic improvement is gaining interest. However, efficient and effective improvement of software systems requires a better understanding of the code-change search space. One important choice practitioners have is whether to preserve the system's original output or permit approximation with each scenario having its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by the modification operators is 2.69percent (0.76percent on average). By contrast, this figure increases dramatically to 95.60percent (33.90percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for code optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied source code modifications produce an effect greater than their individual sum. Our results reveal that 12.0percent of all joint code modifications produced such a synergistic effect though 38.5percent produce an antagonistic interaction in which simultaneously applied modifications are less effective than when applied individually. This highlights the need for more advanced search-based approaches.", notes = "Presented at ESEC/FSE 2018 Journal-First https://2018.fseconference.org/event/fse-2018-journal-first-approximate-oracles-and-synergy-in-software-energy-search-spaces also known as \cite{8338061}", } @InProceedings{Bruce:2022:GI, author = "Bobby R. Bruce", title = "Automatically Exploring Computer System Design Spaces", booktitle = "GI @ GECCO 2022", year = "2022", editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke", pages = "1926--1927", address = "Boston, USA", publisher_address = "New York, NY, USA", month = "9 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, search, computer architecture, ISA, gem5", isbn13 = "978-1-4503-9268-6/22/07", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Bruce_2022_GI.pdf", DOI = "doi:10.1145/3520304.3534021", slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/bruce-automatically-exploring-computer-gi-gecco-22.pdf", size = "2 pages", abstract = "While much research has focused on using search to optimize software, it is possible to use these same approaches to optimise other parts of the computer system. With decreased costs in silicon customisation, and the return of centralized systems carrying out specialized tasks, the time is right to begin thinking about tools to optimise systems for the needs of specific software targets. In the approach outlined, the standard Genetic Improvement process is flipped with source code considered static and the remainder of the computer system altered to the needs of the software. The project proposed is preliminary research into incorporating grammar-based GP with an advanced computer architecture simulator to automatically design computer systems. I argue this approach has the potential to significantly improve the design of computer systems while reducing manual effort.", notes = "http://geneticimprovementofsoftware.com/events/gecco2022 GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)", } @InProceedings{bruce:1996:agOOpGP, author = "Wilker Shane Bruce", title = "Automatic Generation of Object-Oriented Programs Using Genetic Programming", booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference", editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo", year = "1996", month = "28--31 " # jul, keywords = "genetic algorithms, genetic programming, memory", pages = "267--272", address = "Stanford University, CA, USA", publisher = "MIT Press", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp96.pdf/bruce96automatic.pdf", URL = "http://citeseer.ist.psu.edu/bruce96automatic.html", URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap33.pdf", URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279", size = "6 pages", abstract = "This research addresses the application of genetic programming to the generation of object-oriented programs. An extended chromosome data structure is presented where the set of methods associated with an object is stored as an array of program trees. Modified genetic operators are defined to manipulate this structure. Indexed memory is used to allow the programs generated by the system to access and modify object memory. These extensions to the standard genetic programming...", notes = "GP-96 Early version available from http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-96.ps (broken) Uses GP to induce stack, queue and P queue. Represents objects as array of trees, one per method. Mutation and crossover. {"}Strongly typed GP generally out performed untyped GP as was expected{"}. STGP. Says details in \cite{bruce:thesis}.", } @PhdThesis{bruce:thesis, author = "Wilker Shane Bruce", title = "The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs", school = "School of Computer and Information Sciences, Nova Southeastern University", year = "1995", address = "3100 SW 9th Avenue, Fort Lauderdale, Florida 33315, USA", month = Dec, keywords = "genetic algorithms, genetic programming, memory", URL = "https://nsuworks.nova.edu/gscis_etd/430/", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce.thesis.ps.gz", size = "664 pages", abstract = "Genetic programming is an automatic programming method that creates computer programs to satisfy a software designer's input/output specification through the application of principles from genetics and evolutionary biology. A population of programs is maintained where each program is represented in the chromosome data structure as a tree. Programs are evaluated to determine their fitness in solving the specified task. Simulated genetic operations like crossover and mutation are probabilistically applied to the more highly fit programs in the population to generate new programs. These programs then replace existing programs in the population according to the principles of natural selection. The process repeats until a correct program is found or an iteration limit is reached. This research concerns itself with the application of genetic programming to the generation of object-oriented programs. A new chromosome data structure is presented in which the entire set of methods associated with an object are stored as a set of program trees. Modified genetic operators that manipulate this new structure are defined. Indexed memory methods are used to allow the programs generated by the system to access and modify object memory. The result of these modifications to the standard genetic programming paradigm is a system that can simultaneously generate all of the methods associated with an object. Experiments were performed to compare the sequential generation of object methods with two variants of simultaneous generation. The first variant used information about both method return values and object internal memory state in its fitness function. The second variant only used information about method return values. It was found that simultaneous generation of methods is possible in the domain of simple collection objects both with and without the availability of internal memory state in the fitness function. It was also found that this technique is up to four orders of magnitude more computationally expensive in terms of number of individuals generated in the search than the sequential generation of the same set of methods on an individual basis.", notes = "Supervisor: Phillip M Admas", } @InProceedings{bruce:1997:lprsbGPADF, author = "Wilker Shane Bruce", title = "The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, pages = "52--57", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", keywords = "genetic algorithms, genetic programming, ADF", broken = "http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-97.ps", URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp97.pdf/bruce97lawnmower.pdf", URL = "http://citeseer.ist.psu.edu/bruce97lawnmower.html", size = "6 pages", abstract = "Stack-based genetic programming is an alternative to Koza-style tree-based genetic programming that generates linear programs that are executed on a virtual machine using a FORTH-style operand stack instead of tree-based function calls. A stack-based genetic programming system was extended to include the ability to generate programs containing automatically defined functions. Experiments were run to test the system using Koza's lawnmower problem. The stack-based system using automatically...", notes = "GP-97 Zero fitness if attempts to pop empty stack. LEFT primitive removed from population. ARG0 never in best best of run. 'SBGP required significantly more search than tree-based GP' 'comparisons ... may be problem dependant'. 'In both systems [GP and SBGP] the use of ADFs appreciably improved the ability of the GP system to quickly find a solution to the [lawn mower] problem.' failure of SBGP without ADFs to solve 8x12 'is most probably due to our limit of a maximium of 256 elements in a solution'.", } @InProceedings{brucherseifer:2001:EuroGP, author = "Eva Brucherseifer and Peter Bechtel and Stephan Freyer and Peter Marenbach", title = "An Indirect Block-Oriented Representation for Genetic Programming", booktitle = "Genetic Programming, Proceedings of EuroGP'2001", year = "2001", editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon", volume = "2038", series = "LNCS", pages = "268--279", address = "Lake Como, Italy", publisher_address = "Berlin", month = "18-20 " # apr, organisation = "EvoNET", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, Block-oriented representation, Biotechnology, Process modelling, Controller design, Causality: Poster", ISBN = "3-540-41899-7", DOI = "doi:10.1007/3-540-45355-5_21", size = "12 pages", abstract = "When Genetic Programming (GP) is applied to system identification or controller design different codings can be used for internal representation of the individuals. One common approach is a block-oriented representation where nodes of the tree structure directly correspond to blocks in a block diagram. In this paper we present an indirect block-oriented representation, which adopts some aspects of the way humans perform the modelling in order to increase the GP system's performance. A causality measure based on an edit distance is examined to compare the direct an the indirect representation. Finally, results from a real world application of the indirect block-oriented representation are presented.", notes = "EuroGP'2001, part of \cite{miller:2001:gp}", } @PhdThesis{Brucherseifer:thesis, author = "Eva Brucherseifer", title = "Der Artbegriff in der Genetischen Programmierung", school = "Department of Electrical Engineering and Information Technology, TU Darmstadt", year = "2010", address = "Germany", month = sep, publisher = "Shaker Verlag GmbH", keywords = "genetic algorithms, genetic programming, GP theory, Genetische Programmierung, Evolutionaere Algorithmen, Art Analyse, Visualisierung, Artengraph, Clustering, Strukturoptimierung, Heuristische Optimierungsverfahren", ISBN-13 = "978-3-8322-9942-2", URL = "http://tubiblio.ulb.tu-darmstadt.de/54187/", URL = "https://www.amazon.com/Artbegriff-Genetischen-Programmierung/dp/3832299424", URL = "https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8322-9942-2", size = "233 pages", zusammenfassung = "In der Automatisierung wird die Genetische Programmierung zur Modellbildung von komplexen Systemen und zum Reglerentwurf eingesetzt. Die Genetische Programmierung nimmt die biologische Evolution als Vorbild und ahmt sie nach. Mathematische Modelle werden dazu als Individuen kodiert und ueber eine Anzahl von Generationen hinweg evolutionaer selektiert und veraendert. Bei einem Evolutionslauf ueber mehrere Generationen faellt mit vielen Individuen eine kaum zu ueberblickende, grosse Datenmenge an. Deshalb sind die Auswirkungen einer fuer die Genetische Programmierung gewaehlten Konfiguration fuer den Anwender nur schwer zu kontrollieren. Fuer den Modellbildner ist es wichtig, den Ablauf und die Ergebnisse der Genetischen Programmierung schnell analysieren und bewerten zu koennen. Diese Arbeit bestimmt, erprobt und bewertet daher Methoden und Verfahren, die eine tiefergehende Analyse des Evolutionsvorgangs durch eine geeignete Visualisierung ermoeglichen. Das neuartige Artenkonzept fasst aehnliche Individuen zu Arten zusammen, um sie so visualisieren zu koennen. Die Arten ermoeglichen eine konzentrierte Analyse, die mit allen Individuen aufgrund deren grosser Anzahl nicht moeglich waere. Die Artenbildung basiert auf der Bestimmung der AEhnlichkeit von Individuen. Bekannte und neue Merkmale und Distanzmasse zum Vergleich von Individuen werden daher vorgestellt und bezueglich ihrer Anwendbarkeit fuer die Artenbildung neu bewertet. Zur Darstellung der evolutionaeren Abstammungslinien der Arten wurde der Artengraph entwickelt, der die aus den zwischen Individuen berechneten Distanzen, Abstammungen und Fitnesswerten aggregiert und so die Divergenz von Populationen visualisiert. Der Artengraph ist dem aus der Biologie bekannten Stammbaum nachempfunden und ermoeglicht eine konzentrierte, effiziente und zeitnahe Analyse eines Optimierungslaufs. Anhand einer Reglerentwicklung fuer einen Antennenarm konnte die Anwendung der entwickelten Visualisierungstechniken erfolgreich gezeigt werden. Die Analyse mittels Artengraph der evolutionaeren Entwicklung von Individuen waehrend eines Optimierungslaufs fuehrte zu einer verbesserten Parametrisierung der Genetischen Zusammenfassung In der Automatisierung wird die Genetische Programmierung zur Modellbildung von komplexen Systemen und zum Reglerentwurf eingesetzt. Die Genetische Programmierung nimmt die biologische Evolution als Vorbild und ahmt sie nach. Mathematische Modelle werden dazu als Individuen kodiert und ueber eine Anzahl von Generationen hinweg evolutionaer selektiert und veraendert. Bei einem Evolutionslauf ueber mehrere Generationen faellt mit vielen Individuen eine kaum zu ueberblickende, grosse Datenmenge an. Deshalb sind die Auswirkungen einer fuer die Genetische Programmierung gewaehlten Konfiguration fuer den Anwender nur schwer zu kontrollieren. Fuer den Modellbildner ist es wichtig, den Ablauf und die Ergebnisse der Genetischen Programmierung schnell analysieren und bewerten zu koennen. Diese Arbeit bestimmt, erprobt und bewertet daher Methoden und Verfahren, die eine tiefergehende Analyse des Evolutionsvorgangs durch eine geeignete Visualisierung ermoeglichen. Das neuartige Artenkonzept fasst aehnliche Individuen zu Arten zusammen, um sie so visualisieren zu koennen. Die Arten ermoeglichen eine konzentrierte Analyse, die mit allen Individuen aufgrund deren grosser Anzahl nicht moeglich waere. Die Artenbildung basiert auf der Bestimmung der AEhnlichkeit von Individuen. Bekannte und neue Merkmale und Distanzmasse zum Vergleich von Individuen werden daher vorgestellt und bezueglich ihrer Anwendbarkeit fuer die Artenbildung neu bewertet. Zur Darstellung der evolutionaeren Abstammungslinien der Arten wurde der Artengraph entwickelt, der die aus den zwischen Individuen berechneten Distanzen, Abstammungen und Fitnesswerten aggregiert und so die Divergenz von Populationen visualisiert. Der Artengraph ist dem aus der Biologie bekannten Stammbaum nachempfunden und ermoeglicht eine konzentrierte, effiziente und zeitnahe Analyse eines Optimierungslaufs. Anhand einer Reglerentwicklung fuer einen Antennenarm konnte die Anwendung der entwickelten Visualisierungstechniken erfolgreich gezeigt werden. Die Analyse mittels Artengraph der evolutionaeren Entwicklung von Individuen waehrend eines Optimierungslaufs fuehrte zu einer verbesserten Parametrisierung der Genetischen Programmierung. Dies konnte mit nur wenigen Optimierungslaeufen und damit geringem Zeitaufwand erreicht werden.", notes = "In German, Deutsch. SMOG from Dr.-Ing. Peter Marenbach also known as \cite{tubiblio54187}", } @Article{bruhn:2002:ECJ, author = "Peter Bruhn and Andreas Geyer-Schulz", title = "Genetic Programming over Context-Free Languages with Linear Constraints for the Knapsack Problem: First Results", journal = "Evolutionary Computation", year = "2002", volume = "10", number = "1", pages = "51--74", month = "Spring", keywords = "genetic algorithms, genetic programming, grammatical evolution, grammar-based genetic, programming, combinatorial, optimization, context-free grammars, with linear constraints, knapsack problems", broken = "http://www.ingentaconnect.com/content/mitpress/evco/2002/00000010/00000001/art00004", DOI = "doi:10.1162/106365602317301772", abstract = "we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.", } @Misc{Brule:2016:ArXiv, author = "Joshua Brule and Kevin Engel and Nick Fung and Isaac Julien", title = "Evolving Shepherding Behavior with Genetic Programming Algorithms", howpublished = "ArXiv", year = "2016", keywords = "genetic algorithms, genetic programming", bibdate = "2016-04-02", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1603.html#BruleEFJ16", URL = "http://arxiv.org/abs/1603.06141", } @InProceedings{Brum:2018:evocop, author = "Artur Brum and Marcus Ritt", title = "Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time", booktitle = "The 18th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2018", year = "2018", editor = "Arnaud Liefooghe and Manuel Lopez-Ibanez", series = "LNCS", volume = "10782", publisher = "Springer", pages = "85--100", address = "Parma, Italy", month = "4-6 " # apr, organisation = "Species", keywords = "genetic algorithms, genetic programming, Automatic algorithm configuration, Iterated greedy algorithm, Iterated local search, Flow shop scheduling problem, Total completion time", isbn13 = "978-3-319-77448-0", DOI = "doi:10.1007/978-3-319-77449-7_6", abstract = "Automatic algorithm configuration aims to automate the often time-consuming task of designing and evaluating search methods. We address the permutation flow shop scheduling problem minimizing total completion time with a context-free grammar that defines how algorithmic components can be combined to form a full heuristic search method. We implement components from various works from the literature, including several local search procedures. The search space defined by the grammar is explored with a racing-based strategy and the algorithms obtained are compared to the state of the art", notes = "Is this GP? Searching grammar EvoCOP2018 held in conjunction with EuroGP'2018 EvoMusArt2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evocop.php", } @PhdThesis{Brum:thesis, author = "Artur Ferreira Brum", title = "Automatic Algorithm Configuration for Flow Shop Scheduling Problems", school = "Instituto de Informatica, Universidade Federal do Rio Grande do Sul", year = "2020", address = "Porto Alegre, Brazil", month = aug, keywords = "genetic algorithms, genetic programming, Informatica, Automatic algorithm configuration, Flow shop scheduling problem, Iterated greedy algorithm, Iterated local search, Problema de agendamento em flow shop, Configuracao automatica dealgoritmos, Busca local iterada, Algoritmo guloso iterado", URL = "https://www.inf.ufrgs.br/site/eventos/evento/tese-de-doutorado-de-artur-ferreira-brum/", URL = "http://hdl.handle.net/10183/213705", URL = "https://lume.ufrgs.br/handle/10183/213705", URL = "https://www.lume.ufrgs.br/bitstream/handle/10183/213705/001118296.pdf", size = "130 pages", abstract = "Scheduling problems have been a subject of interest to the optimization researchers for many years. Flow shop problems, in particular, are one of the most widely studied scheduling problems due to their application to many production environments. A large variety of solution methods can be found in the literature and, since many flow shop problems are NP-hard, the most frequently found approaches are heuristic methods. Heuristic search methods are often complex and hard to design, requiring a significant amount of time and manual work to perform such a task, which can be tedious and prone to human biases. Automatic algorithm configuration (AAC) comprises techniques to automate the design of algorithms by selecting and calibrating algorithmic components. It provides a more robust approach which can contribute to improving the state of the art. In this thesis we present a study on the permutation and the non-permutation flow shop scheduling problems. We follow a grammar-based AAC strategy to generate iterated local search or iterated greedy algorithms. We implement several algorithmic components from the literature in a parameterised solver, and explore the search space defined by the grammar with a racing-based strategy. New efficient algorithms are designed with minimal manual effort and are evaluated against benchmarks from the literature. The results show that the automatically designed algorithms can improve the state of the art in many cases, as evidenced by comprehensive computational and statistical testing.", notes = " Supervisor: Marcus Peter Ritt", } @InProceedings{Brumby:1999:SPIE, author = "Steven P. Brumby and James Theiler and Simon J. Perkins and Neal Harvey and John J. Szymanski and Jeffrey J. Bloch and Melanie Mitchell", title = "Investigation of image feature extraction by a genetic algorithm", booktitle = "Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, Proceedings of SPIE", year = "1999", editor = "Bruno Bosacchi and David B. Fogel and James C. Bezdek", volume = "3812", pages = "24--31", month = "19-20 " # jul, keywords = "genetic algorithms, genetic programming, Evolutionary computation, image analysis, multi-spectral analysis", URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.8210", URL = "http://web.cecs.pdx.edu/~mm/spie3812.pdf", DOI = "doi:10.1117/12.367697", size = "8 pages", abstract = "We describe the implementation and performance of a genetic algorithm (GA) which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.", notes = "Fixed length but includes NOP to give variable length http://www.spie.org/web/meetings/programs/sd99/confs/3812.html Los Alamos National Lab; Santa Fe Institute [3812-03]", } @InProceedings{Brumby:2000:SPIE, author = "Steven P. Brumby and Neal R. Harvey and Simon Perkins and Reid B. Porter and John J. Szymanski and James Theiler and Jeffrey J. Bloch", title = "A genetic algorithm for combining new and existing image processing tools for multispectral imagery", booktitle = "Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Proceedings of SPIE", year = "2000", editor = "Sylvia S. Shen and Michael R. Descour", volume = "4049", pages = "480--490", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Hyperspectral Imagery", URL = "http://spiedigitallibrary.org/data/Conferences/SPIEP/35048/480_1.pdf", DOI = "doi:10.1117/12.410371", size = "11 pages", abstract = "We describe the implementation and performance of a genetic algorithm (GA) which evolves and combines image processing tools for multispectral imagery (MSI) datasets. Existing algorithms for particular features can also be 're-tuned' and combined with the newly evolved image processing tools to rapidly produce customised feature extraction tools. First results from our software system were presented previously. We now report on work extending our system to look for a range of broad-area features in MSI datasets. These features demand an integrated spatiospectral approach, which our system is designed to use. We describe our chromosomal representation of candidate image processing algorithms, and discuss our set of image operators. Our application has been geospatial feature extraction using publicly available MSI and hyperspectral imagery (HSI). We demonstrate our system on NASA/Jet Propulsion Laboratory's Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) HSI which has been processed to simulate MSI data from the Department of Energy's Multispectral Thermal Imager (MTI) instrument. We exhibit some of our evolved algorithms, and discuss their operation and performance.", notes = "Space and Remote Sensing Sciences, Los Alamos National Laboratory, Mail Stop D436, Los Alamos, New Mexico 87545, U.S.A.", } @InProceedings{Brumby:2001:SPIE, author = "S. P. Brumby and J. J. Bloch and N. R. Harvey and J. Theiler and S. Perkins and A. C. Young and J. J. Szymanski", title = "Evolving forest fire burn severity classification algorithms for multi-spectral imagery", booktitle = "In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE", year = "2001", editor = "Sylvia S. Shen and Michael R. Descour", volume = "4381", pages = "236--245", keywords = "genetic algorithms, genetic programming, Multispectral imagery, Supervised classification, Forest fire, Wildfire, GENIE, Aladdin", URL = "http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf", DOI = "doi:10.1117/12.437013", size = "10 pages", abstract = "Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100percent tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial colour/infrared photography.", notes = "p3 Max size directed acyclic graph, not tree GP. GENIE object-oriented Perl. RSI's IDL language and image processing environment. C. UNIX Linux. Aladdin JAVA. Output is written to one of a number of scratch planes (memory) 'temporary workspaces where an image plane can be stored.' 'the gene [ADDP rD0 rS1 wS2] applies pixel-by-pixel addition to two input planes, read from data plane 0 and from scratch plane 1, and writes its output to scratch plane 2.' 'GENIE performs an analysis of chromosome graphs when they are created and only carries out those processing steps that actually affect the final result. Therefore, the fixed length of the chromosome acts as a maximum effective length.' hamming distance fitness pop=50. 30 gens. max chrome size 20. 3 scratch registers. 'The best evolved image-processing algorithm had the chromosome, [OPEN rD1 wS1 1 1][ADDS rD4 wS3 0.34][NEG rS1 wS1][MULTP rD4 rS3 wS2] [LINCOMB rS1 rD6 wS3 0.11][ADDP rS1 rS3 wS1][SUBP rS1 rD5 wS1]' 'The final values of S1, S2, and S3 are then combined in the linear sum, where the coefficients and intercept have been chosen by the Fisher discriminant, as described in Section 2.3, above, to produce our real-valued answer plane A (Figure 6): A = 0.0147*S1 - 0.0142*S2 + 0.0134*S3 + 1.554' 'Adjusting the threshold on A to fall at the between-peak minimum of the histogram at 0.7930 (a different optimisation criterion for the threshold than that used by default by GENIE) produces a new Boolean mask, Figure 9, in which almost all the false positives have been removed, and the remaining pixels marked as burn correspond very closely to the high severity burn regions in the BAER map'", } @InProceedings{Brumby:2001:FUSION, author = "Steven P. Brumby and James Theiler and Simon Perkins and Neal R. Harvey and John J. Szymanski", title = "Genetic programming approach to extracting features from remotely sensed imagery", booktitle = "FUSION 2001: Fourth International Conference on Image Fusion", year = "2001", address = "Montreal, Quebec, Canada", month = "7-10 " # aug, email = "brumby@lanl.gov", keywords = "genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Panchromatic imagery", URL = "http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf", size = "8 pages", abstract = "Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance.", notes = "oai:CiteSeerPSU:567526 seems to be wrong", } @InProceedings{oai:CiteSeerPSU:445835, author = "Steven P. Brumby and James Theiler and Jeffrey J. Bloch and Neal R. Harvey and Simon Perkins and John J. Szymanski and A. Cody Young", title = "Evolving land cover classification algorithms for multispectral and multitemporal imagery", booktitle = "Proc. SPIE Imaging Spectrometry VII", year = "2002", editor = "Michael R. Descour and Sylvia S. Shen", volume = "4480", pages = "120--129", organisation = "SPIE", keywords = "genetic algorithms, genetic programming, Feature Extraction, Supervised classification, K-means clustering, Multi-spectral imagery, Land cover, Wildfire", URL = "http://public.lanl.gov/jt/Papers/brumby_SPIE4480-14.pdf", URL = "http://citeseer.ist.psu.edu/445835.html", DOI = "doi:10.1117/12.453331", size = "10 pages", abstract = "The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.", notes = "Los Alamos National Lab.", } @TechReport{Brun:2013:TR, author = "Yuriy Brun and Earl Barr and Ming Xiao and Claire {Le Goues} and P. Devanbu", title = "Evolution vs. Intelligent Design in Program Patching", institution = "Dept. of Computer Science, University of California, Davis", year = "2013", address = "USA", month = "Fall", keywords = "genetic algorithms, genetic programming, genetic improvement, APR, SBSE", URL = "https://escholarship.org/uc/item/3z8926ks.pdf", size = "13 pages", abstract = "While Fixing bugs requires significant manual effort, recent research has shown that genetic programming (GP) can be used to search through a space of programs to automatically Find candidate bugfixing patches. Given a program, and a set of test cases (some of which fail), a GP-based repair technique evolves a patch or a patched program using program mutation and selection operators. We evaluate GenProg, a well-known GP-based patch generator, using a large, diverse dataset of over a thousand simple (both buggy and correct) student-written homework programs, using two different test sets: a white-box test set constructed to achieve edge coverage on an oracle program, and a black-box test set developed to exercise the desired specification. We Find that GenProg often succeeds at Finding a patch that will cause student programs to pass supplied white-box test cases; however, that the solution quite often overfits to the supplied tests and doesn't pass all the black-box tests. In contrast, when students patch their own buggy programs, these patches tend to pass the black-box tests as well. We also Find that the GenProg-generated patches lack enough diversity to benefit from a kind of bagging, in which a plurality vote over a population of GP-generated patches outperforms a randomly chosen individual patch. We report these results and additional relationships between GenProg's success and the size and complexity of the manual and automatic patches.", notes = "cited by \cite{Smith:2015:FSE}", } @Article{brunello:2021:Sensors, author = "Andrea Brunello and Andrea Urgolo and Federico Pittino and Andras Montvay and Angelo Montanari", title = "Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments", journal = "Sensors", year = "2021", volume = "21", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "1424-8220", URL = "https://www.mdpi.com/1424-8220/21/8/2728", DOI = "doi:10.3390/s21082728", abstract = "Real-time estimation of temperatures in indoor environments is critical for several reasons, including the upkeep of comfort levels, the fulfillment of legal requirements, and energy efficiency. Unfortunately, setting an adequate number of sensors at the desired locations to ensure a uniform monitoring of the temperature in a given premise may be troublesome. Virtual sensing is a set of techniques to replace a subset of physical sensors by virtual ones, allowing the monitoring of unreachable locations, reducing the sensors deployment costs, and providing a fallback solution for sensor failures. In this paper, we deal with temperature monitoring in an open space office, where a set of physical sensors is deployed at uneven locations. Our main goal is to develop a black-box virtual sensing framework, completely independent of the physical characteristics of the considered scenario, that, in principle, can be adapted to any indoor environment. We first perform a systematic analysis of various distance metrics that can be used to determine the best sensors on which to base temperature monitoring. Then, following a genetic programming approach, we design a novel metric that combines and summarizes information brought by the considered distance metrics, outperforming their effectiveness. Thereafter, we propose a general and automatic approach to the problem of determining the best subset of sensors that are worth keeping in a given room. Leveraging the selected sensors, we then conduct a comprehensive assessment of different strategies for the prediction of temperatures observed by physical sensors based on other sensors' data, also evaluating the reliability of the generated outputs. The results show that, at least in the given scenario, the proposed black-box approach is capable of automatically selecting a subset of sensors and of deriving a virtual sensing model for an accurate and efficient monitoring of the environment.", notes = "also known as \cite{s21082728}", } @Article{BRUNELLO:2022:pmcj, author = "Andrea Brunello and Angelo Montanari and Nicola Saccomanno", title = "A genetic programming approach to {WiFi} fingerprint meta-distance learning", journal = "Pervasive and Mobile Computing", year = "2022", volume = "85", pages = "101681", keywords = "genetic algorithms, genetic programming, Indoor positioning, Wi-Fi fingerprinting, Metric, Machine learning", ISSN = "1574-1192", DOI = "doi:10.1016/j.pmcj.2022.101681", URL = "https://www.sciencedirect.com/science/article/pii/S1574119222000980", abstract = "Driven by the continuous growth in the number of mobile smart devices, location-based services are becoming a fundamental aspect in the ubiquitous computing domain. In this work, we focus on indoor scenarios, where positioning supports tasks such as navigation, logistics, and access management and control. Most indoor positioning solutions are based on WiFi fingerprinting, thanks to its ease of deployment. Such a technique often requires the adoption of a suitable distance metric to compare the currently observed WiFi access points with those pertaining to fingerprints contained in a database, and whose position is already known. Results from the literature make it evident that classical distance functions among WiFi fingerprints do not preserve spatial information in its entirety. Here, we explore the possibility of addressing such a shortcoming by combining a selection of fingerprint distance functions into a meta-distance, using a genetic programming approach to solve a symbolic regression problem. The outcomes of the investigation, based on 16 publicly available datasets, show that a small, but statistically relevant, improvement can be achieved in preserving spatial information, and that the developed meta-distance has a generalization capability no worse than top-performing classical fingerprint distance functions when trained on a dataset and tested on the others. In addition, when used within a k-nearest-neighbor positioning framework, the meta-distance outperforms all the contenders, despite not being expressly designed to support position estimation. This sheds a light on a significant relationship between preservation of spatial information and localization performance. The achieved results pave the way for the development of more advanced metric learning solutions, that go beyond the limitations of currently-employed fingerprint distance functions", } @Article{Brunello:2023:ACC, author = "Andrea Brunello and Dario Della Monica and Angelo Montanari and Nicola Saccomanno and Andrea Urgolo", journal = "IEEE Access", title = "Monitors That Learn From Failures: Pairing {STL} and Genetic Programming", year = "2023", volume = "11", pages = "57349--57364", abstract = "In several domains, systems generate continuous streams of data during their execution, including meaningful telemetry information, that can be used to perform tasks like preemptive failure detection. Deep learning models have been exploited for these tasks with increasing success, but they hardly provide guarantees over their execution, a problem which is exacerbated by their lack of interpretability. In many critical contexts, formal methods, which ensure the correct behaviour of a system, are thus necessary. However, specifying in advance all the relevant properties and building a complete model of the system against which to check them is often out of reach in real-world scenarios. To overcome these limitations, we design a framework that resorts to monitoring, a lightweight runtime verification technique that does not require an explicit model specification, and pairs it with machine learning. Its goal is to automatically derive relevant properties, related to a bad behaviour of the considered system, encoded by means of formulas of Signal Temporal Logic ( $\mathsf {STL}$ ). Results based on experiments performed on well-known benchmark datasets show that the proposed framework is able to effectively anticipate critical system behaviours in an online setting, providing human-interpretable results.", keywords = "genetic algorithms, genetic programming, Monitoring, Machine learning, Task analysis, Feature extraction, Runtime, Data mining, Telemetry, Failure analysis, Machine learning, formal methods, runtime verification, monitoring, failure detection, explainable AI, XAI", DOI = "doi:10.1109/ACCESS.2023.3277620", ISSN = "2169-3536", notes = "Also known as \cite{10129205}", } @Article{BRUNS:2019:ESA, author = "Ralf Bruns and Jurgen Dunkel and Norman Offel", title = "Learning of complex event processing rules with genetic programming", journal = "Expert Systems with Applications", volume = "129", pages = "186--199", year = "2019", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2019.04.007", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419302386", keywords = "genetic algorithms, genetic programming, Complex event processing, Rule learning, Pattern mining", abstract = "Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. Extensive evaluations with synthetic and real world data demonstrate the high potential of the approach and give some hints about the choice of suitable process parameters", } @Article{Bryant:2001:ETAI, author = "C. H. Bryant and S. H. Muggleton and S. G. Oliver and D. B. Kell and P. G. K. Reiser and R. D. King", title = "Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes", journal = "Electronic Transactions in Artificial Intelligence", year = "2001", volume = "6", number = "12", month = "30 " # aug, publisher = "Linkoping University Electronic Press, Sweden", keywords = "ILP", ISSN = "1401-9841", URL = "http://www.ep.liu.se/ea/cis/2001/012/", URL = "http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.ps", URL = "http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.pdf", size = "40 pages", abstract = "We aim to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. We have developed a novel form of learning curve, which in contrast to the form of learning curve normally used in Active Learning, allows one to compare the costs incurred by different leaning strategies. We plan to combine ASE-Progol with a standard laboratory robot to create a general automated approach to Functional Genomics. As a first step towards this goal, we are using ASE-Progol to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. Our approach involves auxotrophic mutant trials. To date, ASE-Progol has conducted such trials in silico. However we describe how they will be performed automatically in vitro by a standard laboratory robot designed for these sorts of liquid handling tasks, namely the Beckman/Coulter Biomek 2000. Although our work to date has been limited to trials conducted in silico, the results have been encouraging. Parts of the model were removed and the ability of ASE-Progol to efficiently recover the performance of the model was measured. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy in the range 46-88% was reduced if trials were selected by ASE-Progol rather than if they were sampled at random (without replacement). To reach an accuracy in the range 46-80%, ASE-Progol incurs five orders of magnitude less experimental costs than random sampling. ASE-Progol requires less time to converge upon a hypothesis with an accuracy in the range 74-87percent than if trials are sampled at random (without replacement) or selected using the naive strategy of always choosing the cheapest trial from the set of candidate trials. For example to reach an accuracy of 80%, ASE-Progol requires 4 days while random sampling requires 6 days and the naive strategy requires 10 days.", notes = "online only?", } @Article{1676819, author = "Randal E. Bryant", title = "Graph-Based Algorithms for Boolean Function Manipulation", journal = "IEEE Transactions on Computers", year = "1986", volume = "C-35", number = "8", pages = "677--691", month = aug, keywords = "DEC VAX, Boolean functions, binary decision diagrams, logic design verification, symbolic manipulation", ISSN = "0018-9340", DOI = "doi:10.1109/TC.1986.1676819", size = "15 pages", abstract = "In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the practicality of our approach.", notes = "NOT GP, exhaustive depth first search?", } @InProceedings{10.1162/978-0-262-31050-5-ch003, author = "David M. Bryson and Charles Ofria", title = "Digital Evolution Exhibits Surprising Robustness to Poor Design Decisions", booktitle = "ALIFE 2012: The Thirteenth International Conference on the Synthesis and Simulation of Living Systems", year = "2012", pages = "19--26", address = "East Lansing, Michigan, USA", month = jul # " 19-22", publisher = "MIT", keywords = "genetic algorithms, genetic programming", URL = "https://doi.org/10.1162/978-0-262-31050-5-ch003", URL = "https://direct.mit.edu/isal/proceedings-pdf/alife2012/24/19/1901084/978-0-262-31050-5-ch003.pdf", DOI = "doi:10.1162/978-0-262-31050-5-ch003", size = "8 pages", abstract = "When designing an evolving software system, a researcher must set many aspects of the representation and inevitably make arbitrary decisions. Here we explore the consequences of poor design decisions in the development of a virtual instruction set in digital evolution systems. We evaluate the introduction of three different severities of poor choices. (1) functionally neutral instructions that water down mutational options, (2) actively deleterious instructions, and (3) a lethal die instruction. We further examine the impact of a high level of neutral bloat on the short term evolutionary potential of genotypes experiencing environmental change. We observed surprising robustness to these poor design decisions across all seven environments designed to analyse a wide range challenges. Analysis of the short term evolutionary potential of genotypes from the principal line of descent of case study populations demonstrated that the negative effects of neutral bloat in a static environment are compensated by retention of evolutionary potential during environmental change.", notes = "Avida http://avida.devosoft.org is this GP? Logic-9 Nine 1- and 2-input logic operations. Logic-77 Seventy-seven 1-, 2-, and 3-input logic operations. Match-12 Generate up to 12 specific numbers. Fibonacci-32 Output up to 32 numbers of the Fibonacci sequence, in order. Sort-10 Input 10 random numbers and output in correctly sorted order. Limited-9 Logic-9 environment with a limited resource associated with each task. Navigation Successfully traverse a labeled pathway", } @Article{buason:2005:GPEM, author = "Gunnar Buason and Nicklas Bergfeldt and Tom Ziemke", title = "Brains, Bodies, and Beyond: Competitive Co-Evolution of Robot Controllers, Morphologies and Environments", journal = "Genetic Programming and Evolvable Machines", year = "2005", volume = "6", number = "1", pages = "25--51", month = mar, keywords = "genetic algorithms, neuronal robot controller, CCE, khepera, YAKS simulator", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-005-7618-x", abstract = "We present a series of simulation experiments that incrementally extend previous work on neural robot controllers in a predator-prey scenario, in particular the work of Floreano and Nolfi, and integrates it with ideas from work on the co-evolution of robot morphologies and control systems. The aim of these experiments has been to further systematically investigate the tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. Motivated by the fact that, despite the emphasis of the interdependence of brain, body and environment in much recent research, the environment has actually received relatively little attention, the last set of experiments lets robots/species actively adapt their environments to their own needs, rather than just adapting themselves to a given environment. This paper is an extended version of: Buason and Ziemke. {"}Co-evolving task-dependent visual morphologies in predator-prey experiments,{"} in Genetic and Evolutionary Computation Conference, Cantu-Paz et al. (Eds.), Springer Verlag: Berlin, 2003, pp. 458-469.", notes = "20/100 rule", } @Article{BUCCHERI2021108722, author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo", title = "Artificial intelligence in health data analysis: The {Darwinian} evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes", journal = "Diabetes Research and Clinical Practice", year = "2021", volume = "174", pages = "108722", month = "1 " # apr, keywords = "genetic algorithms, genetic programming, BP, Type 2 diabetes, Zero-cost dysglycemia screening, Artificial intelligence", ISSN = "0168-8227", URL = "https://pubmed.ncbi.nlm.nih.gov/33647331/", URL = "https://www.sciencedirect.com/science/article/pii/S0168822721000759", DOI = "doi:10.1016/j.diabres.2021.108722", abstract = "Aims The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia. Methods We use NHANES cross-sectional data over 10 years (2007 to 2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables. Results Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3 percent. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data. Conclusions The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors.", } @Article{BUCCHERI2022100398, author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo", title = "Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost", journal = "Obesity Medicine", year = "2022", volume = "31", pages = "100398", month = may, keywords = "genetic algorithms, genetic programming, type 2 diabetes, Dysglycemia screening tool, Stratified analysis, Artificial intelligence, AI", ISSN = "2451-8476", URL = "https://www.sciencedirect.com/science/article/pii/S2451847622000100", DOI = "doi:10.1016/j.obmed.2022.100398", abstract = "Aims We perform a stratified analysis of the recently published age-related waist circumference cut-off model to validate its performance in the screening of dysglycemia in the US population. Methods We use NHANES data as representative of the US population. Data were subdivided into sex, ethnic and glycemia groups. We evaluate the performance of the model separately in each group through the Wilcox statistic area under the (ROC) curve, AUC. We also discuss the calibration of the model. Results For the sex-stratified analysis, we obtain AUC = 0.69--0.71 (95percent confidence interval) for male individuals and AUC = 0.75--0.78 (95percent C.I.) for female individuals. The stratified analysis is performed in different ethnic groups, namely Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other Race -- Including Multi-Racial. We obtain, respectively, AUC = 0.74--0.75, AUC = 0.76--0.78, AUC = 0.73--0.75, AUC = 0.74--0.77 and AUC = 0.71--0.73 (95percent C.I.). The model achieves AUC = 0.70--0.73 (95percent C.I.) in the identification of individuals with prediabetes and AUC = 0.70--0.80 (95percent C.I.) in the identification of individuals with diabetes. Conclusions The accuracy of the model turns out to be similar in each group considered in the stratified analysis, indicating that the model is suitable to be used as a screening tool for dysglycemia in the US population", } @Article{Buccheri:2024:JPT, author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo and Rita Chiaramonte and Michele Vecchio", title = "Appendicular Skeletal Muscle Mass in Older Adults Can Be Estimated With a Simple Equation Using a Few Zero-Cost Variables", journal = "Journal of Geriatric Physical Therapy", year = "2024", volume = "47", number = "4", pages = "E149--E158", month = oct # "/" # dec, keywords = "genetic algorithms, genetic programming, Brain Project, medicine, appendicular skeletal muscle mass, artificial intelligence, dual-energy X-ray absorptiometer, muscle mass loss", URL = "https://pubmed.ncbi.nlm.nih.gov/39079022/", DOI = "doi:10.1519/JPT.0000000000000420", size = "10 pages", notes = "Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jgptonline.com) Department of Biomedical and Biotechnological Sciences, Section of Pharmacology, University of Catania, Catania, Italy.", } @InProceedings{buchanan:2024:CEC, author = "Edgar Buchanan and Simon Hickinbotham and Rahul Dubey and Imelda Friel and Andrew Colligan and Mark Price and Andy M. Tyrrell", title = "A Quality Diversity Study in {EvoDevo} Processes for Engineering Design", booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)", year = "2024", editor = "Bing Xue", address = "Yokohama, Japan", month = "30 " # jun # " - 5 " # jul, publisher = "IEEE", keywords = "genetic algorithms, genetic programming, Measurement, Dimensionality reduction, Refining, Evolutionary computation, Calibration, evodevo, generative design, structural engineering, quality diversity, neural networks", isbn13 = "979-8-3503-0837-2", DOI = "doi:10.1109/CEC60901.2024.10612076", abstract = "For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results.", notes = "also known as \cite{10612076} WCCI 2024", } @Article{BUCHNER:2020:IFAC-PapersOnLine, author = "Jens S. Buchner and Sebastian Boblest and Patrick Engel and Andrej Junginger and Holger Ulmer", title = "An Artificial-Intelligence-Based Method to Automatically Create Interpretable Models from Data Targeting Embedded Control Applications", journal = "IFAC-PapersOnLine", volume = "53", number = "2", pages = "13789--13796", year = "2020", note = "21st IFAC World Congress", ISSN = "2405-8963", DOI = "doi:10.1016/j.ifacol.2020.12.887", URL = "https://www.sciencedirect.com/science/article/pii/S240589632031226X", keywords = "genetic algorithms, genetic programming, Nonlinear, optimal automotive control, Automotive system identification, modeling, Modeling, supervision, control, diagnosis of automotive systems", abstract = "The development of new automotive drivetrain layouts requires modeling of the involved components to allow for ideal control strategies. The creation of these models is both costly and challenging, specifically because interpretability, accuracy, and computational effort need to be balanced. In this study, a method is put forward which supports experts in the modeling process and in making an educated choice to balance these constraints. The method is based on the artificial intelligence technique of genetic programming. By solving a symbolic regression problem, it automatically identifies equation-based models from data. To address possible system complexities, data-based expressions like curves and maps can additionally be employed for the model identification. The performance of the method is demonstrated based on two examples: 1. Identification of a pure equation based model, demonstrating the benefit of interpretability. 2. Creation of a hybrid-model, combining a base equation with data-based expressions. Possible applications of the method are model creation, system identification, structural optimization, and model reduction. The existing implementation in ETAS ASCMO-MOCA also offers a high efficiency increase by combining and automating the two procedural steps of embedded function engineering and calibration", } @InProceedings{eurogp06:BuchsbaumVossner, author = "Thomas Buchsbaum and Siegfried V{\"o}ssner", title = "Information-Dependent Switching of Identification Criteria in a Genetic Programming System for System Identification", editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art", booktitle = "Proceedings of the 9th European Conference on Genetic Programming", publisher = "Springer", series = "Lecture Notes in Computer Science", volume = "3905", year = "2006", address = "Budapest, Hungary", month = "10-12 " # apr, organisation = "EvoNet", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-33143-3", pages = "300--309", DOI = "doi:10.1007/11729976_27", bibsource = "DBLP, http://dblp.uni-trier.de", abstract = "Genetic Programming (GP) can be used to identify the nonlinear differential equations of dynamical systems. If, however, the fitness function is chosen in a classical way, the optimisation will not work very well. In this article, we explain the reasons for the failure of the GP approach and present a solution strategy for improving performance. Using more than one identification criterion (fitness function) and switching based on the information content of the data enable standard GP algorithms to find better solutions in shorter times. A computational example illustrates that identification criteria switching has a bigger influence on the results than the choice of the GP parameters has.", notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006", } @InProceedings{Buchsbaum:2007:cec, author = "Thomas Buchsbaum", title = "Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "1269--1275", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1490.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424616", abstract = "System identification is the scientific art of building models from data. Good models are of essential importance in many areas of science and industry. Models are used to analyse, simulate, and predict systems and their states. Model structure selection and estimation of the model parameters with respect to a chosen criterion of fit are essential parts of the identification process. In this article, we investigate the suitability of genetic programming for creating continuous nonlinear state-space models from noisy time series data. We introduce methodologies from the field of chaotic time series estimation and present concepts for integrating them into a genetic programming system. We show that even small changes of the fitness evaluation approach may lead to a significantly improved performance. In combination with multiobjective optimisation, a multiple shooting approach is able to create powerful models from noisy data.", notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @PhdThesis{Buchsbaum:thesis, author = "Thomas Buchsbaum", title = "Improvement of Evolutionary Computation Approaches for Continuous Dynamical System Identification - Robustness and Performance Improvement of Standard Genetic Programming by Approximation, Multiple Shooting Methods, and Iterative Approaches", school = "Institute of Engineering and Business Informatics, Graz University of Technology", year = "2007", address = "Kopernikusgasse 24 Graz, Austria", keywords = "genetic algorithms, genetic programming, System Identification, Evolutionary Computation, Design of Experiments, Input Signal Shaping, Multiple shooting Approximation Time series modeling, Dynamical systems, Continuous state space models, System Identifikation, Evolutionaere Algorithmen, Zeitreihenanalyse, Versuchsplanung, Eingangsgroessenoptimierung", language = "English", URL = "https://online.tugraz.at/tug_online/pl/ui/$ctx;lang=DE/wbAbs.showThesis?pThesisNr=24591&pOrgNr=13706", URL = "https://graz.elsevierpure.com/en/publications/improvement-of-evolutionary-computation-approaches-for-continuous", abstract = "The objective of a mathematical model is to describe certain aspects of a real system; the aim of system identification is to create models from, usually noisy, measurement data. Genetic Programming (GP) is a biology-inspired method for optimising structured representations in general, and dynamical model structures and their parameters in particular. It has been applied to continuous dynamical system identification, but suffered from weak performance and premature convergence behavior. This thesis investigates GP's suitability for creating nonlinear continuous state-space models from noisy time series data. Methodologies are introduced that improve GP's performance and robustness. For the considered test problem, it is shown that instead of solving a dynamical problem by an initial value method, a static problem can be approximated, which can be solved by symbolic regression. This approximation approach speeds up evolution considerably. Fitness evaluation using multiple shooting methods, known from the field of chaotic time series, simplifies the optimization problem by smoothing the fitness landscape; the GP algorithm finds useful building blocks more easily. Three concepts for integrating multiple shooting into GP systems are developed and compared. This thesis offers a concept for automatically switching the identification approach based on the information content of the training data. Computational studies showed that automatic switching combined the advantages of different identification approaches: Better models were created in shorter times. Further, multi-objective methods for regularisation were shown to improve the evolved models generalization abilities substantially. Investigations of model-based input signal optimisation by evolutionary computation methods complete this dissertation. The developed methodologies improve GPs performance and robustness on continuous dynamical system identification tasks. This makes GP a useful tool that assists human modelers in finding building blocks for model synthesis. By applying the introduced methods, the chance of finding hidden information in complex signals can be increased. Medicine, natural sciences, technology, and business could benefit from the improved prediction qualities of the resulting models and the cost savings due to data-efficient modeling procedures.", notes = "also known as \cite{711f8ca066c0490eb8e33fadbcd363a9}", } @Article{Buckingham:2015:JH, author = "David Buckingham and Christian Skalka and Josh Bongard", title = "Inductive machine learning for improved estimation of catchment-scale snow water equivalent", journal = "Journal of Hydrology", volume = "524", pages = "311--325", year = "2015", ISSN = "0022-1694", DOI = "doi:10.1016/j.jhydrol.2015.02.042", URL = "http://www.sciencedirect.com/science/article/pii/S0022169415001547", abstract = "Summary Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastructure, wireless sensor networks, and machine learning algorithms. Because snowpack distribution is highly nonlinear, we focus on Genetic Programming (GP), a nonlinear, white-box, inductive machine learning algorithm. Because we did not have access to near-real-time catchment-scale SWE data, we used available data as ground truth for machine learning in a set of experiments that are successive approximations of our goal of catchment-wide SWE estimation. First, we used a history of maritime snowpack data collected by manual snow courses. Second, we used distributed snow depth (HS) data collected automatically by wireless sensor networks. We compared the performance of GP against linear regression (LR), binary regression trees (BT), and a widely used basic method (BM) that naively assumes non-variable snowpack. In the first experiment set, GP and LR models predicted SWE with lower error than BM. In the second experiment set, GP had lower error than LR, but outperformed BT only when we applied a technique that specifically mitigated the possibility of over-fitting.", keywords = "genetic algorithms, genetic programming, Snow water equivalent, Machine learning, Wireless sensor network, Snowpack modelling", } @InCollection{Buckley:2010:Chiong, author = "Muneer Buckley and Zbigniew Michalewicz and Ralf Zurbruegg", title = "An Application of Genetic Programming to Forecasting Foreign Exchange Rates", booktitle = "Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering", publisher = "IGI Global", year = "2010", editor = "Raymond Chiong", chapter = "2", pages = "26--48", keywords = "genetic algorithms, genetic programming", isbn13 = "1605667056", URL = "http://hdl.handle.net/2440/54525", DOI = "doi:10.4018/978-1-60566-705-8", bibsource = "OAI-PMH server at digital.library.adelaide.edu.au", oai = "oai:digital.library.adelaide.edu.au:2440/54525", notes = "http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=794&DetailsType=Description Muneer Buckley (University of Adelaide, Australia) Zbigniew Michalewicz (University of Adelaide, Australia) Ralf Zurbruegg (Institute of Computer Science, Polish Academy of Sciences & Polish-Japanese Institute of Information Technology, Poland)", } @InCollection{Buckley:2009:niiiakd, title = "An Application of Genetic Programming to Forecasting Foreign Exchange Rates", author = "Muneer Buckley and Zbigniew Michalewicz and Ralf Zurbruegg", publisher = "IGI Global", year = "2009", booktitle = "Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering", editor = "Raymond Chiong", chapter = "2", pages = "26--48", keywords = "genetic algorithms, genetic programming", isbn13 = "1605667056", URL = "http://www.igi-global.com/bookstore/chapter.aspx?titleid=36310", URL = "http://hdl.handle.net/2440/54525", oai = "oai:digital.library.adelaide.edu.au:2440/54525", abstract = "There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested.", } @Article{Bucur:2014:ASC, author = "Doina Bucur and Giovanni Iacca and Giovanni Squillero and Alberto Tonda", title = "The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation", journal = "Applied Soft Computing", year = "2014", volume = "16", pages = "210--222", month = mar, keywords = "genetic algorithms, genetic programming, genetic improvement, Collection tree protocol (CTP), MultiHopLQI (MHLQI), Wireless sensor networks (WSN), Evolutionary algorithms (EA), Routing protocols, Verification, Energy consumption", ISSN = "1568-4946", URL = "http://www.sciencedirect.com/science/article/pii/S1568494613004213", DOI = "doi:10.1016/j.asoc.2013.12.002", size = "13 pages", abstract = "The analysis of worst-case behaviour in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analysing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of how interesting such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here, that is, the new topological metrics and the causal explanation, can be fruitfully reused in different contexts, even beyond wireless sensor networks.", notes = "Also known as \cite{Bucur2014210}", } @InProceedings{Buffoni:2018:ICCC, author = "Federico Buffoni and Gabriele Gianini and Ernesto Damiani and Michael Granitzer", booktitle = "2018 IEEE International Conference on Cognitive Computing (ICCC)", title = "All-Implicants Neural Networks for Efficient Boolean Function Representation", year = "2018", pages = "82--86", abstract = "Boolean classifiers can be evolved by means of genetic algorithms. This can be done within an intercommunicating island system, of evolutionary niches, undergoing cycles that alternate long periods of isolation to short periods of information exchange. In these settings, the efficiency of the communication is a key requirement. In the present work, we address this requirement by providing a technique for efficiently representing and transmitting differential encodings of Boolean functions. We introduce a new class of Boolean Neural Networks (BNN), the all-implicants BNN, and show that this representation supports efficient update communication, better than the classical representation, based on truth tables.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/ICCC.2018.00019", month = jul, notes = "Also known as \cite{8457700}", } @InProceedings{bui:286, author = "Tai D. Bui and Alan A. Smith", editor = "Don Phelps and Gerald Sehlke", title = "Water Resource Engineers and Environmental Hydraulics", publisher = "ASCE", year = "2001", volume = "111", pages = "286--286", booktitle = "World Water Congress 2001", address = "Orlando, Florida, USA", month = "20-24 " # may, keywords = "genetic algorithms, genetic programming", broken = "http://link.aip.org/link/?ASC/111/286/1", DOI = "doi:10.1061/40569(2001)286", size = "10 pages", abstract = "In past decades, the fundamental notion of employing a multi-disciplinary approach to water resource projects was well received and promoted. According to this approach, water resource practitioners (especially engineers) should change their solution techniques and evaluation so that a solution would encompass a plethora of issues ? both structural and non-structural ? related to the project. It was recognized that solutions could not be based solely on mathematical models of flow conditions. Aspects such as ecology and non-technical issues such as recreational and societal needs should all be considered in the solution derivation process. Undoubtedly, sophisticated technical and mathematical tools (such as artificial neural network and genetic programming, and other tools related to Hydro-informatics) are essential to implement the approach. Added to this is the involvement of various professionals in certain projects. Planners, biologists, limnologists, economists, landscape architects, etc. are some of the other disciplines, besides engineers, involved in dealing with water resource projects. To address the issues, a distinct branch of engineering is imperative. The International Association of Hydraulic Engineering and Research initiated the Eco-hydraulic branch, the American Society of Civil Engineers formed the Environmental Hydraulic Technical Committee and the Canadian Society of Civil Engineers has the Hydrotechnical branch. All in all, these efforts are intended to ensure not only that levels of awareness are elevated but also those levels of engineering practice are adjusted to suit. As a result, solutions would be environmentally friendly and/or sympathetic.", notes = "number = 40569 Conference Proceeding Paper", } @InCollection{Bui:1997:s8p, author = "Thai Bui", title = "Solving the 8-Puzzle with Genetic Programming", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1997", publisher = "Stanford Bookstore", year = "1997", editor = "John R. Koza", pages = "11--17", address = "Stanford, California, 94305-3079 USA", month = "17 " # mar, keywords = "genetic algorithms, genetic programming", ISBN = "0-18-205981-2", notes = "part of \cite{koza:1997:GAGPs}", } @InProceedings{Buk:2009:ICANNGA, author = "Zdenek Buk and Jan Koutnik and Miroslav Snorek", title = "{NEAT} in {HyperNEAT} Substituted with Genetic Programming", year = "2009", booktitle = "9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009", editor = "Mikko Kolehmainen and Pekka Toivanen and Bartlomiej Beliczynski", series = "Lecture Notes in Computer Science", volume = "5495", pages = "243--252", address = "Kuopio, Finland", month = "23-25 " # apr, publisher = "Springer", note = "Revised selected papers", keywords = "genetic algorithms, genetic programming, HyperGP, ANN", isbn13 = "978-3-642-04920-0", DOI = "doi:10.1007/978-3-642-04921-7_25", size = "10 pages", abstract = "In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers with similar abilities.", notes = "ICANNGA 2009", } @InProceedings{Bukhtoyarov:2010:cec, author = "Vladimir V. Bukhtoyarov and Olga E. Semenkina", title = "Comprehensive evolutionary approach for neural network ensemble automatic design", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the component networks predictions to produce the ensemble output. In this paper, a probability-based method is proposed to accomplish the first stage. Although this method is based on the genetic algorithm, it requires fewer parameters to be tuned. A method based on genetic programming is proposed for combining the predictions of component networks. This method allows us to build nonlinear combinations of component networks predictions providing more flexible and adaptive solutions. To demonstrate robustness of the proposed approach, its results are compared with the results obtained using other methods.", DOI = "doi:10.1109/CEC.2010.5586516", notes = "WCCI 2010. Also known as \cite{5586516}", } @Article{bukhtoyarov:2021:Computation, author = "Vladimir Viktorovich Bukhtoyarov and Vadim Sergeevich Tynchenko", title = "Design of Computational Models for Hydroturbine Units Based on a Nonparametric Regression Approach with Adaptation by Evolutionary Algorithms", journal = "Computation", year = "2021", volume = "9", number = "8", keywords = "genetic algorithms, genetic programming", ISSN = "2079-3197", URL = "https://www.mdpi.com/2079-3197/9/8/83", DOI = "doi:10.3390/computation9080083", abstract = "This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment--hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya-Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modelling error by 20percent and 28percent for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modelling error of about 5percent compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.", notes = "also known as \cite{computation9080083}", } @Article{bukhtoyarov:2023:Electronics, author = "Vladimir V. Bukhtoyarov and Vadim S. Tynchenko and Vladimir A. Nelyub and Igor S. Masich and Aleksey S. Borodulin and Andrei P. Gantimurov", title = "A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability", journal = "Electronics", year = "2023", volume = "12", number = "1", pages = "Article No. 215", keywords = "genetic algorithms, genetic programming", ISSN = "2079-9292", URL = "https://www.mdpi.com/2079-9292/12/1/215", DOI = "doi:10.3390/electronics12010215", abstract = "Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting.", notes = "also known as \cite{electronics12010215}", } @InProceedings{bull:1996:SandS, author = "Lawrence Bull and Terence C. Fogarty", title = "Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis", editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel", booktitle = "Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation", year = "1996", publisher = "Springer-Verlag", volume = "1141", series = "LNCS", pages = "12--21", address = "Berlin, Germany", publisher_address = "Heidelberg, Germany", month = "22-26 " # sep, keywords = "genetic algorithms", ISBN = "3-540-61723-X", DOI = "doi:10.1007/3-540-61723-X_965", size = "10 pages", abstract = "In this paper we introduce two macro-level operators to enhance the use of population-based evolutionary computing techniques in multiagent environments: speciation and symbiogenesis. We describe their use in conjunction with the genetic algorithm to evolve Pittsburgh-style classifier systems, where each classifier system represents an agent in a cooperative multi-agent system. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing a controller for the gait of a wall-climbing quadrupedal robot, where each leg of the quadruped is controlled by a classifier system. We find that the use of such operators can give improved performance over static population/agent configurations.", notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 Wall climbing quadruped robot simulation", } @InProceedings{Bull:1997:ecmaee, author = "Larry Bull and Owen Holland", title = "Evolutionary Computing in Multi-Agent Environments: Eusociality", booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference", editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo", year = "1997", month = "13-16 " # jul, keywords = "Genetic Algorithms", pages = "347--352", address = "Stanford University, CA, USA", publisher_address = "San Francisco, CA, USA", publisher = "Morgan Kaufmann", notes = "GP-97", } @InProceedings{bull:1999:OZSCDM, author = "Larry Bull", title = "On using ZCS in a Simulated Continuous Double-Auction Market", booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference", year = "1999", editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith", volume = "1", pages = "83--90", address = "Orlando, Florida, USA", publisher_address = "San Francisco, CA 94104, USA", month = "13-17 " # jul, publisher = "Morgan Kaufmann", keywords = "genetic algorithms and classifier systems", ISBN = "1-55860-611-4", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-806.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GA-806.pdf", notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)", } @InProceedings{Bull:2009:eurogp, author = "Larry Bull and Richard Preen", title = "On Dynamical Genetic Programming: Random {Boolean} Networks in Learning Classifier Systems", booktitle = "Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009", year = "2009", editor = "Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe {De Falco} and Marc Ebner", volume = "5481", series = "LNCS", pages = "37--48", address = "Tuebingen", month = apr # " 15-17", organisation = "EvoStar", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-642-01180-1", DOI = "doi:10.1007/978-3-642-01181-8_4", notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009", } @Article{Bull:2009:IJPEDS, author = "Larry Bull", title = "On dynamical genetic programming: simple {Boolean} networks in learning classifier systems", journal = "International Journal of Parallel, Emergent and Distributed Systems", year = "2009", volume = "24", number = "5", pages = "421--442", month = oct, publisher = "Taylor \& Francis", keywords = "genetic algorithms, genetic programming, discrete, dynamical systems, evolution, multiplexer, unorganised machines", ISSN = "1744-5760", DOI = "doi:10.1080/17445760802660387", abstract = "Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic programming (GP). This paper presents results from an initial investigation into using simple dynamical GP representations within a learning classifier system. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.", notes = "a Department of Computer Science, University of the West of England, Bristol, UK Formerly Parallel Algorithms and Applications", } @InCollection{Bull:2017:miller, author = "Larry Bull and Rita Toth and Chris Stone and Ben {De Lacy Costello} and Andrew Adamatzky", title = "Chemical Computing Through Simulated Evolution", booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday", publisher = "Springer", year = "2017", editor = "Susan Stepney and Andrew Adamatzky", volume = "28", series = "Emergence, Complexity and Computation", chapter = "13", pages = "269--286", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-67996-9", DOI = "doi:10.1007/978-3-319-67997-6_13", abstract = "Many forms of unconventional computing, i.e., massively parallel computers which exploit the non-linear material properties of their substrate, can be realised through simulated evolution. That is, the behaviour of non-linear media can be controlled automatically and the structural design of the media optimized through the nature-inspired machine learning approach. This chapter describes work using the Belousov-Zhabotinsky reaction as a non-linear chemical medium in which to realise computation. Firstly, aspects of the basic structure of an experimental chemical computer are evolved to implement two Boolean logic functions through a collision-based scheme. Secondly, a controller is evolved to dynamically affect the rich spatio-temporal chemical wave behaviour to implement three Boolean functions, in both simulation and experimentation.", notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624", } @Article{bullard:1998:MD, author = "James B. Bullard and John Duffy", title = "learning and the Stability of Cycles", journal = "Macroeconomic Dynamics", year = "1998", volume = "2", number = "1", pages = "22--48", month = mar, keywords = "genetic algorithms, Learning, Multiple Equilibria, Coordination", size = "27 pages", abstract = "We investigate the extent to which agents can learn to coordinate on stationary perfect-foresight cycles in a general-equilibrium environment. Depending on the value of a preference parameter, the limiting backward (direction of time reversed) perfect-foresight dynamics are characterized by steady-state, periodic, or chaotic trajectories for real money balances. We relax the perfect-foresight assumption and examine how a population of artificial, heterogeneous adaptive agents might learn in such an environment. These artificial agents optimize given their forecasts of future prices, and they use forecast rules that are consistent with steady-state or periodic trajectories for prices. The agents' forecast rules are updated by a genetic algorithm. We find that the population of artificial adaptive agents is able eventually to coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect-foresight assumption.", notes = "Also available as working paper 1995-006B http://research.stlouisfed.org/wp/1995/95-006.pdf", } @Article{bullard:1998:JEDC, author = "James Bullard and John Duffy", title = "A model of learning and emulation with artificial adaptive agents", journal = "Journal of Economic Dynamics and Control", year = "1998", volume = "22", number = "2", pages = "179--207", month = feb, keywords = "genetic algorithms, Learning, Coordination, Overlapping generations", DOI = "doi:10.1016/S0165-1889(97)00072-9", abstract = "We study adaptive learning in a sequence of overlapping generations economies in which agents live for n periods. Agents initially have heterogeneous beliefs, and form multi-step-ahead forecasts using a forecast rule chosen from a vast set of candidate rules. Agents learn in every period by creating new forecast rules and by emulating the forecast rules of other agents. Computational experiments show that systems so defined can yield three qualitatively different types of long-run outcomes: (1) coordination on a low inflation, stationary perfect foresight equilibrium; (2) persistent currency collapse; and (3) coordination failure within the allotted time frame.", notes = "JEL classification codes: D83; C63 Also available as working paper 1994-014C http://research.stlouisfed.org/wp/1994/94-014.pdf", } @Article{buontempo:2005:CIM, author = "Frances V. Buontempo and Xue Zhong Wang and Mulaisho Mwense and Nigel Horan and Anita Young and Daniel Osborn", title = "Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data", journal = "Journal of Chemical Information and Modeling", year = "2005", volume = "45", pages = "904--912", note = "ASAP article. Web Release Date: May 12, 2005", keywords = "genetic algorithms, genetic programming, decision trees, model ecotoxicity, EPTree, C5.0 See5, recursive partitioning, S-Plus, SIMCA-P 8.0, QSAR", DOI = "doi:10.1021/ci049652n", size = "9 pages", abstract = "Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.", notes = " http://pubs.acs.org/journals/jcisd8/index.html S1549-9596(04)09652-4 ACS Publications Division cites EPtree \cite{delisle:2004:CIM} y-scrambling. at least 10\% data coverage required of decision trees. Tournament size 16. No parsimony fitness preassure. Trees regrown. Lots of mutation if pop stagnated. Elitist but gives no improvement. -Log(LC50) vibrio fischeri. 1093 features. 60 training compounds. 100 generation. Pop 600. 1 second per generation. Department of Chemical Engineering and School of Civil Engineering, University of Leeds, Leeds LS2 9JT, U.K., AstraZeneca UK Ltd., Brixham Environmental Laboratory, Freshwater Quarry, Brixham, Devon TQ5 8BA, U.K., and Centre of Ecology and Hydrology, Monks Wood, Huntingdon PE28 2LS, U.K.", } @Article{Burakov:2013:IJICA, author = "Sergei V. Burakov and Eugene S. Semenkin", title = "Solving variational and Cauchy problems with self-configuring genetic programming algorithm", journal = "International Journal of Innovative Computing and Applications", year = "2013", volume = "5", number = "3", pages = "152--162", note = "Special Issue on: BIOMA 2012 Advances in Bio-inspired Computing. Guest Editors: Assistant Professor Jurij Silc and Associate Professor Bogdan Filipic", keywords = "genetic algorithms, genetic programming, Ordinary differential equations; Cauchy problem; variational problem; numeric methods; genetic programming algorithm; self-configuration.", ISSN = "1751-648X", DOI = "doi:10.1504/IJICA.2013.055931", abstract = "It is suggested to use genetic programming techniques for solving Cauchy problem and variational problem that allows getting the exact analytical solution if it does exist and an approximate analytical expression otherwise. Features of solving process with this approach are considered. Results of numerical experiments are given. The approach improvement is fulfilled by adopting the self-configuring genetic programming algorithm that does not require extra efforts for choosing its effective settings but demonstrates the competitive performance", notes = "Acceptance Date: 30 Nov 2012 IJICA http://www.inderscience.com/jhome.php?jcode=ijica ", } @InProceedings{wssec-rb-final, author = "Robert Burbidge", title = "A Contribution to the Foundations of {AI}: Genetic Programming and Support Vector Machines", booktitle = "Workshop and Summer School on Evolutionary Computing Lecture Series by Pioneers", year = "2008", editor = "T. M. McGinnity", address = "University of Ulster", month = "18-20 " # aug, organisation = "School of Computing and Intelligent Systems, University of Ulster", keywords = "genetic algorithms, genetic programming, SVM", URL = "http://users.aber.ac.uk/rvb/wssec-rb-final.pdf", size = "4 pages", abstract = "The aim of genetic programming is to automatically find computer programs that solve problems; using an algorithm inspired by biological evolution. The aim of the support vector machine is to model empirical data; using an algorithm based on statistical optimality. Fundamentally, both these techniques, and all artificial intelligence disciplines, use search; with differing representations, search operators and objective functions. We formally compare these two techniques as a contribution toward the foundations of artificial intelligence, and less grandiosely, in order to encourage transfer of knowledge between the two disciplines.", notes = "broken 2016 http://isel.infm.ulst.ac.uk/conference/wssec2008/ 'The search space for GP is hard'. 'The search space for the SVM is easy'. 'inherent capacity control in GP' VC-dimension.", } @InProceedings{Burbidge:2009:TAROS, author = "Robert Burbidge and Joanne H. Walker and Myra S. Wilson", title = "A Grammar for Evolution of a Robot Controller", booktitle = "TAROS 2009 Towards Autonomous Robotic Systems", year = "2009", editor = "Theocharis Kyriacou and Ulrich Nehmzow and Chris Melhuish and Mark Witkowski", series = "Intelligent Systems Research Centre Technical Report Series", pages = "182--189", address = "University of Ulster, Londonderry, United Kingdom", month = aug # " 31 - " # sep # " 2", keywords = "genetic algorithms, genetic programming, grammatical evolution, robot control", URL = "http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf", size = "8 pages", abstract = "An autonomous mobile robot requires an onboard controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot.", notes = "http://www.infm.ulst.ac.uk/~ulrich/Taros09/", } @InProceedings{Burbidge:2009:IROS, author = "Robert Burbidge and Joanne H. Walker and Myra S. Wilson", title = "Grammatical evolution of a robot controller", booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009", year = "2009", month = "11-15 " # oct, address = "St. Louis, USA", pages = "357--362", keywords = "genetic algorithms, genetic programming, grammatical evolution, Khepera robot, artificial intelligence, autonomous mobile robot, evolutionary algorithm, evolutionary technique, onboard controller, robot controller, grammars, mobile robots", DOI = "doi:10.1109/IROS.2009.5354411", abstract = "An autonomous mobile robot requires an on board controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot.", notes = "Department of Computer Science, Aberystwyth University, Penglais, SY23 3DB, UK Also known as \cite{5354411}", } @Article{Burbidge:2014:IS, author = "Robert Burbidge and Myra S. Wilson", title = "Vector-valued function estimation by grammatical evolution for autonomous robot control", journal = "Information Sciences", volume = "258", pages = "182--199", year = "2014", ISSN = "0020-0255", DOI = "doi:10.1016/j.ins.2013.09.044", URL = "http://www.sciencedirect.com/science/article/pii/S0020025513006920", keywords = "genetic algorithms, genetic programming, Grammatical evolution, Evolutionary robotics, Vector-valued function, Ripple crossover, Schema", size = "18 pages", abstract = "An autonomous mobile robot requires a robust onboard controller that makes intelligent responses in dynamic environments. Current solutions tend to lead to unnecessarily complex solutions that only work in niche environments. Evolutionary techniques such as genetic programming (GP) can successfully be used to automatically program the controller, minimising the limitations arising from explicit or implicit human design criteria, based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been applied to various problems, particularly those for which GP has performed. We formulate robot control as vector-valued function estimation and present a novel generative grammar for vector valued functions. A consideration of the crossover operator leads us to propose a design criterion for the application of GE to vector-valued function estimation, along with a second novel generative grammar which meets this criterion. The suitability of these grammars for vectorvalued function estimation is assessed empirically on a simulated task for the Khepera robot", } @InProceedings{Burger:2011:SAICSIT, author = "Clayton Burger and Mathys C. {Du Plessis}", title = "Does Chomsky complexity affect genetic programming computational requirements?", booktitle = "Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment", year = "2011", pages = "31--39", address = "Cape Town, South Africa", publisher = "ACM", keywords = "genetic algorithms, genetic programming, theory of computation, Turing machines", isbn13 = "978-1-4503-0878-6", DOI = "doi:10.1145/2072221.2072226", size = "9 page", abstract = "This paper presents an exploration into the relationship between Chomsky problem complexity, as defined by Theory of Computation, and the computational requirements to evolve solutions to these problems. Genetic programming is used to explore these computational requirements by evolving Turing machines that accept the languages posed. Quantifiable results are obtained by applying various metrics to the evolutionary success of these evolved Turing machines. The languages posed are samples out of three language classes from the Chomsky hierarchy, with each class having increasing levels of complexity based on the hierarchy. These languages are evolved on a two-tape Turing machine representation by making use of genetic operators found to be effective in the literature. By exploring the effects of the genetic programming algorithm population sizes and coupled genetic operator rates, it was found that the evolutionary success rates of the classes of Regular and Context-Sensitive problems have no statistical difference in computational requirements, while the Context-Free class was found to be more difficult than the other two Chomsky problem classes through the statistical significance discovered when compared to the other classes.", notes = "SAICSIT '11", acmid = "2072226", } @Article{Burgess:2001:IST, author = "Colin J. Burgess and Martin Lefley", title = "Can genetic programming improve software effort estimation? A comparative evaluation", year = "2001", journal = "Information and Software Technology", volume = "43", number = "14", pages = "863--873", month = "15 " # dec, keywords = "genetic algorithms, genetic programming, Case-based reasoning, Machine learning, Neural networks, Software effort estimation", URL = "http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73", DOI = "doi:10.1016/S0950-5849(01)00192-6", abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000586", size = "11 pages", abstract = "Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently, attention has turned to a variety of machine learning (ML) methods. This paper attempts to evaluate critically the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches, in terms of accuracy and ease of use. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. The input variables are restricted to those available from the specification stage and significant effort is put into the GP and all of the other solution strategies to offer a realistic and fair comparison. There is evidence that GP can offer significant improvements in accuracy but this depends on the measure and interpretation of accuracy used. GP has the potential to be a valid additional tool for software effort estimation but set up and running effort is high and interpretation difficult, as it is for any complex meta-heuristic technique.", } @InCollection{2000240, author = "C. J. Burgess and M. Lefley", title = "Can Genetic Programming improve Software Effort Estimation? {A} Comparative Evaluation", booktitle = "Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering", editor = "Du Zhang and Jeffrey J. P. Tsai", volume = "16", ISBN = "981-256-094-7", publisher = "World Scientific Publishing Co.", pages = "95--105", month = may, year = "2005", keywords = "genetic algorithms, genetic programming, Artificial Intelligence, Machine Learning, SBSE", pubtype = "7", broken = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000240", abstract = "Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation.", notes = "This paper is not on-line. Contact the author see \cite{Burgess:2001:IST}", } @TechReport{burgess:1999:faasdeGP, author = "Glenn Burgess", title = "Finding Approximate Analytic Solutions To Differential Equations Using Genetic Programming", institution = "Surveillance Systems Division, Defence Science and Technology Organisation, Australia", month = Feb, year = "1999", type = "Technical Report", number = "DSTO-TR-0838", address = "Salisbury, SA, 5108, Australia", notes = "Based on author's 1997 Dept. Phys. Honours Thesis, Flinders University of South Australia", keywords = "genetic algorithms, genetic programming, differential equations", broken = "http://203.36.224.190/cgi-bin/dsto/extract.pl?DSTO-TR-0838", URL = "http://www.dsto.defence.gov.au/corporate/reports/DSTO-TR-0838.pdf", size = "73 pages", abstract = "The computational optimisation technique, genetic programming, is applied to the analytic solution of general differential equations. The approach generates a mathematical expression that is an approximate or exact solution to the particular equation under consideration. The technique is applied to a number of differential equations of increasing complexity in one and two dimensions. Comparative results are given for varying several parameters of the algorithm such as the size of the calculation stack and the variety of available mathematical operators. Several novel approaches gave negative results. Angeline's module acquisition (MA) and Koza's automatically defined functions (ADF) are considered and the results of some modifications are presented. One result of significant theoretical interest is that the syntax-preserving crossover used in Genetic Programming may be generalised to allow the exchange of n-argument functions without adverse effects. The results show that Genetic Programming is an effective technique that can give reasonable results, given plenty of computing resources. The technique used here can be applied to higher dimensions; although in practice the algorithmic complexity may be too high.", } @InProceedings{Burgin:2010:cec, author = "Mark Burgin and Eugene Eberbach", title = "Bounded and periodic evolutionary machines", booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)", year = "2010", address = "Barcelona, Spain", month = "18-23 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-4244-6910-9", abstract = "The aim of this paper is the development of foundations for evolutionary computation. We introduce and study two classes of evolutionary automata: bounded and periodic evolutionary machines.", DOI = "doi:10.1109/CEC.2010.5586271", notes = "WCCI 2010. Also known as \cite{5586271}", } @InProceedings{Burian:2013:AE, author = "Petr Burian", title = "Reduction of fitness calculations in Cartesian Genetic Programming", booktitle = "International Conference on Applied Electronics (AE 2013)", year = "2013", month = "10-12 " # sep, address = "Pilsen", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Multiplier, Evolutionary design, Evolutionary Algorithm, Multiplier", ISSN = "1803-7232", URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6636478", size = "6 pages", abstract = "This paper deals with the valuation issue in Cartesian Genetic Programming. It explores the possibilities of the reduction of candidate solutions which are needed to be evaluated. This reduction may accelerate the process of the evolution - evolutionary design. The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the valuation of a candidate solution. The author shows this approach on the evolutionary design of multipliers.", notes = "Also known as \cite{6636478}", } @InProceedings{Burian:2014:ICSES, author = "P. Burian", booktitle = "International Conference on Signals and Electronic Systems (ICSES 2014)", title = "Fast detection of active genes in Cartesian Genetic Programming", year = "2014", month = sep, abstract = "This paper deals with the implementation of fast active genes detector by an FPGA device. The author introduces the modular structure that determines active genes in genotypes of Cartesian Genetic Programming. The use of the detector is suitable if the l-back parameter takes value of 1 or 2. The paper also discusses timing performance. The introduced active genes detector can be used for the reduction of fitness calculations in Cartesian Genetic Programming.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/ICSES.2014.6948721", notes = "Also known as \cite{6948721}", } @InProceedings{Burian:2014:AE, author = "P. Burian", booktitle = "International Conference on Applied Electronics (AE 2014)", title = "Compact version of Cartesian Genetic Programming", year = "2014", month = sep, pages = "63--66", abstract = "This paper deals with the design of the compact version of Cartesian Genetic Programming. The focus is given to the search algorithm of type (1+1). The paper presents the approach that detects changes in the phenotype and, based on that, the algorithm can omit the evaluation of a candidate solution. The author uses the evolutionary design of multipliers as benchmark to present the efficiency of the algorithm.", keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming", DOI = "doi:10.1109/AE.2014.7011669", ISSN = "1803-7232", notes = "Also known as \cite{7011669}", } @InCollection{burjorjee:1999:GAGGS, author = "Keki M. Burjorjee", title = "Genetic Algorithms Go to Grade School", booktitle = "Genetic Algorithms and Genetic Programming at Stanford 1999", year = "1999", editor = "John R. Koza", pages = "31--40", address = "Stanford, California, 94305-3079 USA", month = "15 " # mar, publisher = "Stanford Bookstore", keywords = "genetic algorithms, genetic programming", notes = "part of \cite{koza:1999:GAGPs}", } @Article{burk:1998:pmgGA, author = "Donald S. Burke and Kenneth A. {De Jong} and John J. Grefenstette and Connie Loggia Ramsey and Annie S. Wu", title = "Putting More Genetics into Genetic Algorithms", journal = "Evolutionary Computation", year = "1998", volume = "6", number = "4", pages = "387--410", month = "Winter", keywords = "genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes", URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387", DOI = "doi:10.1162/evco.1998.6.4.387", size = "25 pages", abstract = "The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). The VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.", notes = "Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang Banzhaf", } @Misc{burk:1998:pmgGAx, author = "Donald S. Burke and Kenneth A. {De Jong} and John J. Grefenstette and Connie Loggia Ramsey and Annie S. Wu", title = "Putting More Genetics into Genetic Algorithms", howpublished = "preprint of \cite{burk:1998:pmgGA}", year = "1998", month = "19 " # oct, keywords = "genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes", URL = "http://www.ib3.gmu.edu/gref/papers/burke-ec98.ps", } @InProceedings{burke:2002:gecco, author = "Edmund Burke and Steven Gustafson and Graham Kendall", title = "A Survey And Analysis Of Diversity Measures In Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "716--723", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, diversity, population diversity, population dynamics", ISBN = "1-55860-878-8", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.pdf", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP125.ps", URL = "http://gpbib.cs.ucl.ac.uk/gecco2002/GP125.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Nominated for best at GECCO award", } @InProceedings{burke:ppsn2002:pp341, author = "Edmund Burke and Steven Gustafson and Graham Kendall and Natalio Krasnogor", title = "Advanced Population Diversity Measures in Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN VII", address = "Granada, Spain", month = "7-11 " # sep, pages = "341--350", year = "2002", editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel", number = "2439", series = "Lecture Notes in Computer Science, LNCS", publisher = "Springer-Verlag", URL = "http://www.gustafsonresearch.com/research/publications/ppsn-2002.pdf", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/ppsn-2002.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/ppsn-2002.pdf", URL = "http://slater.chem.nott.ac.uk/~natk/Public/PAPERS/gp-ppsn2002.ps.Z", URL = "http://citeseer.ist.psu.edu/529057.html", DOI = "doi:10.1007/3-540-45712-7_33", keywords = "genetic algorithms, genetic programming, Theory of EC, Evolution dynamics", ISBN = "3-540-44139-5", abstract = "This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to gain a deeper understanding of the conditions under which genetic programming evolution is successful. Three benchmark problems (Artificial Ant, Symbolic Regression and Even-5-Parity) are used to illustrate different diversity measures and to analyse their correlation with performance. Results show that measures of population diversity based on edit distances and phenotypic diversity suggest that successful evolution occurs when populations converge to a similar structure but with high fitness diversity.", } @InProceedings{burke:2003:gecco, author = "Edmund Burke and Steven Gustafson and Graham Kendall", title = "Ramped Half-n-Half Initialisation Bias in {GP}", booktitle = "Genetic and Evolutionary Computation -- GECCO-2003", editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller", year = "2003", pages = "1800--1801", address = "Chicago", publisher_address = "Berlin", month = "12-16 " # jul, volume = "2724", series = "LNCS", ISBN = "3-540-40603-4", publisher = "Springer-Verlag", keywords = "genetic algorithms, genetic programming, poster", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.ps", URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.pdf", DOI = "doi:10.1007/3-540-45110-2_71", abstract = "Tree initialisation techniques for genetic programming (GP) are examined in [4,3], highlighting a bias in the standard implementation of the initialisation method Ramped Half-n-Half (RHH) [1]. GP trees typically evolve to random shapes, even when populations were initially full or minimal trees [2]. In canonical GP, unbalanced and sparse trees increase the probability that bigger subtrees are selected for recombination, ensuring code growth occurs faster and that subtree crossover will have more difficultly in producing trees within specified depth limits. The ability to evolve tree shapes which allow more legal crossover operations, by providing more possible crossover points (by being bushier), and control code growth is critical. The GP community often uses RHH [4]. The standard implementation of the RHH method selects either the grow or full method with 0.5 probability to produce a tree. If the tree is already in the initial population it is discarded and another is created by grow or full. As duplicates are typically not allowed, this standard implementation of RHH favours full over grow and possibly biases the evolutionary process.", notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)", } @InProceedings{1277273, author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John Woodward", title = "Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one", booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation", year = "2007", editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener", volume = "2", isbn13 = "978-1-59593-697-4", pages = "1559--1565", address = "London", URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1559.pdf", DOI = "doi:10.1145/1276958.1277273", publisher = "ACM Press", publisher_address = "New York, NY, USA", month = "7-11 " # jul, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, bin packing, heuristics, hyper heuristic, reliability", abstract = "It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off between the performance and generality of the heuristics generated and their applicability to new problems.", notes = "GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071", } @InProceedings{Burke:2007:cec, author = "E. K. Burke and M. R. Hyde and G. Kendall and J. R. Woodward", title = "The Scalability of Evolved on Line Bin Packing Heuristics", booktitle = "2007 IEEE Congress on Evolutionary Computation", year = "2007", editor = "Dipti Srinivasan and Lipo Wang", pages = "2530--2537", address = "Singapore", month = "25-28 " # sep, organization = "IEEE Computational Intelligence Society", publisher = "IEEE Press", ISBN = "1-4244-1340-0", file = "1668.pdf", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2007.4424789", size = "8 pages", abstract = "The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed 'bestfit' algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by 'best fit'. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. How- ever, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.", notes = "Feb 2013 IEEE Xplore meta data gives wrong author. CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C", } @Article{Burke2013, author = "Edmund K Burke and Michel Gendreau and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and Rong Qu", title = "Hyper-heuristics: a survey of the state of the art", journal = "Journal of the Operational Research Society", year = "2013", volume = "64", number = "12", pages = "1695--1724", month = dec, keywords = "genetic algorithms, genetic programming, Hyper-heuristics, evolutionary computation, metaheuristics, machine learning, combinatorial optimisation, scheduling", publisher = "Palgrave Macmillan", ISSN = "0160-5682", URL = "http://www.cs.nott.ac.uk/~rxq/files/HHSurveyJORS2013.pdf", DOI = "doi:10.1057/jors.2013.71", size = "20 pages", abstract = "Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new, it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.", notes = "several listed approaches use GP", } @InProceedings{Burke:2005:WWERC, author = "John J. Burke", title = "Genetic Programming of Crops to Sustain or Increase Yields under Reduced Irrigation", booktitle = "World Water and Environmental Resources Congress 2005", year = "2005", editor = "Raymond Walton", address = "Anchorage, Alaska, USA", month = may # " 15-19", DOI = "doi:10.1061/40792(173)532", abstract = "Crop productivity is determined by the plant's capacity to convert energy, nutrients, and water into harvestable yield of high quality and high value. The challenge is to sustain or enhance the outputs with a declining land base, reduced water supplies, and a changing global environment. The process of crop adaptation to the environment is restricted by the genetic potential of the plant. Improving the capacity of crops to overcome or adapt to factors that limit growth would increase yield and quality, while reducing demand for irrigation. Research identifying the molecular and biochemical factors underlying crop productivity, adaptation to stressful environments, and production of high-value end products is providing new insights into strategies for germplasm improvement. Characterisation of existing genetic diversity within U.S germplasm collections for water-deficit and temperature stress resistance; and the use of biotechnology to enhance yield stabilisation in water limited environments will ensure farming sustainability in the future.", notes = "Perhaps not about GP? c2005 ASCE", } @InProceedings{Burke:PPSN:2006, author = "E. K. Burke and M. R. Hyde and G. Kendall", title = "Evolving Bin Packing Heuristics with Genetic Programming", booktitle = "Parallel Problem Solving from Nature - PPSN IX", year = "2006", editor = "Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao", volume = "4193", pages = "860--869", series = "LNCS", address = "Reykjavik, Iceland", publisher_address = "Berlin", month = "9-13 " # sep, publisher = "Springer-Verlag", ISBN = "3-540-38990-3", keywords = "genetic algorithms, genetic programming", URL = "http://www.cs.nott.ac.uk/~mvh/ppsn2006.pdf", DOI = "doi:10.1007/11844297_87", size = "10 pages", abstract = "The bin-packing problem is a well known NP-Hard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed first-fit heuristic. Thus, the contribution of this paper is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been designed by humans.", notes = "PPSN-IX", } @InCollection{Burke:2010:HBMH, author = "Edmund K. Burke and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and John R. Woodward", title = "A Classification of Hyper-heuristics Approaches", booktitle = "Handbook of Metaheuristics", publisher = "Springer", year = "2010", editor = "Michel Gendreau and Jean-Yves Potvin", volume = "57", series = "International Series in Operations Research \& Management Science", chapter = "15", pages = "449--468", edition = "2nd", keywords = "genetic algorithms, genetic programming", isbn13 = "978-4419-1663-1", URL = "http://www.cs.nott.ac.uk/~gxo/papers/ChapterClassHH.pdf", DOI = "doi:10.1007/978-1-4419-1665-5_15", abstract = "The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research.", } @Article{Burke:2011:ieeeTEC, author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall", title = "Grammatical Evolution of Local Search Heuristics", journal = "IEEE Transactions on Evolutionary Computation", year = "2012", volume = "16", number = "3", pages = "406--417", month = jun, keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Grammar, Heuristic algorithms, Production, Search problems, Bin packing, heuristics, local search, stock cutting", ISSN = "1089-778X", DOI = "doi:10.1109/TEVC.2011.2160401", size = "12 pages", abstract = "Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which initialises the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.", notes = "iGE also known as \cite{6029980}", } @InCollection{Burkowski:2005:HBBAA, author = "Forbes Burkowski", title = "Optimization via Gene Expression Algorithms", booktitle = "Handbook of Bioinspired Algorithms and Applications", publisher = "Chapman and Hall/CRC", year = "2005", editor = "Stephan Olariu and Albert Y. Zomaya", series = "Computer \& Information Science Series", chapter = "8", pages = "Pages 8--121--8--134", keywords = "SVM", isbn13 = "978-1-58488-475-0", DOI = "doi:10.1201/9781420035063.ch8", notes = "Not on GP?", } @InProceedings{Burks:2015:GECCO, author = "Armand R. Burks and William F. Punch", title = "An Efficient Structural Diversity Technique for Genetic Programming", booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation", year = "2015", editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr", isbn13 = "978-1-4503-3472-3", pages = "991--998", keywords = "genetic algorithms, genetic programming", month = "11-15 " # jul, organisation = "SIGEVO", address = "Madrid, Spain", URL = "http://doi.acm.org/10.1145/2739480.2754649", DOI = "doi:10.1145/2739480.2754649", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Genetic diversity plays an important role in avoiding premature convergence, which is a phenomenon that stifles the search effectiveness of evolutionary algorithms. However, approaches that avoid premature convergence by maintaining genetic diversity can do so at the cost of efficiency, requiring more fitness evaluations to find high quality solutions. We introduce a simple and efficient genetic diversity technique that is capable of avoiding premature convergence while maintaining a high level of search quality in tree-based genetic programming. Our method finds solutions to a set of benchmark problems in significantly fewer fitness evaluations than the algorithms that we compared against.", notes = "Also known as \cite{2754649} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)", } @InProceedings{Burks:2016:GPTP, author = "Armand R. Burks and William F. Punch", title = "An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming", booktitle = "Genetic Programming Theory and Practice XIV", year = "2016", editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier", pages = "19--34", address = "Ann Arbor, USA", month = "19-21 " # may, publisher = "Springer", keywords = "genetic algorithms, genetic programming, premature convergence, genetic diversity, structural diversity, behavioural diversity, semantics", isbn13 = "978-3-319-97087-5", URL = "https://www.springer.com/us/book/9783319970875", DOI = "doi:10.1007/978-3-319-97088-2_2", abstract = "Premature convergence is a serious problem that plagues genetic programming, stifling its search performance. Several genetic diversity maintenance techniques have been proposed for combating premature convergence and improving search efficiency in genetic programming. Recent research has shown that while genetic diversity is important, focusing directly on sustaining behavioural diversity may be more beneficial. These two related areas have received a lot of attention, yet they have often been developed independently. We investigated the feasibility of hybrid genetic and behavioral diversity techniques on a suite of problems.", notes = " Part of \cite{Tozier:2016:GPTP} published after the workshop", } @Article{Burks:2016:GPEM, author = "Armand R. Burks and William F. Punch", title = "An analysis of the genetic marker diversity algorithm for genetic programming", journal = "Genetic Programming and Evolvable Machines", year = "2017", volume = "18", number = "2", pages = "213--245", month = jun, keywords = "genetic algorithms, genetic programming, Genotypic diversity, Structural diversity, Premature convergence", ISSN = "1389-2576", DOI = "doi:10.1007/s10710-016-9281-9", size = "33 pages", abstract = "Many diversity techniques have been developed for addressing premature convergence, which is a serious problem that stifles the search effectiveness of evolutionary algorithms. However, approaches that aim to avoid premature convergence can often take longer to discover a solution. The Genetic Marker Diversity algorithm is a new technique that has been shown to find solutions significantly faster than other approaches while maintaining diversity in genetic programming. This study provides a more in-depth analysis of the search behaviour of this technique compared to other state-of-the-art methods, as well as a comparison of the performance of these techniques on a larger and more modern set of test problems.", } @PhdThesis{Burks:thesis, author = "Armand Rashad Burks", title = "Hybrid Structural and Behavioral Diversity Techniques for Effective Genetic Programming", school = "Computer Science, Michigan State University", year = "2017", address = "USA", keywords = "genetic algorithms, genetic programming, GMD-GP, Tuberculosis Diagnosis", URL = "https://search.proquest.com/docview/1952843117", URL = "https://d.lib.msu.edu/etd/6744/datastream/OBJ/View/", URL = "https://doi.org/doi:10.25335/M5527Z", size = "107 pages", abstract = "Sustaining the diversity of evolving populations is a fundamental issue in genetic programming. We describe a novel measure of structural diversity for tree-based genetic programming, and we demonstrate its utility compared to other diversity techniques. We demonstrate our technique on the real-world application of tuberculosis screening from X-ray images. We then introduce a new paradigm of genetic programming that involves simultaneously maintaining structural and behavioural diversity in order to further improve the efficiency of genetic programming. Our results show that simultaneously promoting structural and behavioural diversity improves genetic programming by leveraging the benefits of both aspects of diversity while overcoming the shortcomings of either technique in isolation. The hybridization increases the behavioral diversity of our structural diversity technique, and increases the structural diversity of the behavioural diversity techniques. This increased diversity leads to performance gains compared to either technique in isolation. We found that in many cases, our structural diversity technique provides significant performance improvement compared to other state-of-the-art techniques. Our results from the experiments comparing the hybrid techniques indicate that the largest performance gain was typically attributed to our structural diversity technique. The incorporation of the behavioural diversity techniques provide additional improvement in many cases.", isbn13 = "9780355181548", notes = "ProQuest Number: 10621139 Supervisor William F. Punch Dissertation/thesis number 10621139", } @InProceedings{Burks:2018:GECCO, author = "Armand R. Burks and William F. Punch", title = "Genetic programming for tuberculosis screening from raw {X-ray} images", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1214--1221", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205461", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "Genetic programming has been successfully applied to several real-world problem domains. One such application area is image classification, wherein genetic programming has been used for a variety of problems such as breast cancer detection, face detection, and pedestrian detection, to name a few. We present the use of genetic programming for detecting active tuberculosis in raw X-ray images. Our results demonstrate that genetic programming evolves classifiers that achieve promising accuracy results compared to that of traditional image classification techniques. Our classifiers do not require pre-processing, segmentation, or feature extraction beforehand. Furthermore, our evolved classifiers process a raw X-ray image and return a classification orders of magnitude faster than the reported times for traditional techniques.", notes = "Also known as \cite{3205461} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Burlacu:2012:EMSS, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan M. Winkler and Gabriel Kronberger", title = "Evolution Tracking in Genetic Programming", booktitle = "The 24th European Modeling and Simulation Symposium, EMSS 2012", year = "2012", editor = "Emilio Jimenez and Boris Sokolov", address = "Vienna, Austria", month = sep # ", 19-21", keywords = "genetic algorithms, genetic programming, tree fragments, evolutionary dynamics, schema theory, population diversity, bloat, introns", URL = "http://research.fh-ooe.at/en/publication/3444", URL = "http://research.fh-ooe.at/files/publications/3444_EMSS_2012_Burlacu.pdf", size = "4 pages", abstract = "Much effort has been put into understanding the artificial evolutionary dynamics within genetic programming (GP). However, the details are yet unclear so far, as to which elements make GP so powerful. This paper presents an attempt to study the evolution of a population of computer programs using HeuristicLab. A newly developed methodology for recording heredity information, based on a general conceptual framework of evolution, is employed for the analysis of algorithm behaviour on a symbolic regression benchmark problem. In our example, we find the complex interplay between selection and crossover to be the cause for size increase in the population, as the average amount of genetic information transmitted from parents to offspring remains constant and independent of run constraints (i.e., tree size and depth limits). Empirical results reveal many interesting details and confirm the validity and generality of our approach, as a tool for understanding the complex aspects of GP.", notes = "EMSS_80 University of Applied Sciences Upper Austria - Austria http://www.msc-les.org/conf/emss2012/index.htm http://www.m-s-net.org/conf/i3m2012_program.pdf", } @InProceedings{conf/eurocast/BurlacuAK13, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda", title = "On the Evolutionary Behavior of Genetic Programming with Constants Optimization", booktitle = "Computer Aided Systems Theory, EUROCAST 2013", year = "2013", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "8111", series = "Lecture Notes in Computer Science", pages = "284--291", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 10-15", publisher = "Springer", note = "14th International Conference, Revised Selected Papers", keywords = "genetic algorithms, genetic programming, evolutionary behaviour, constant optimisation, symbolic regression, algorithm analysis", bibdate = "2013-12-16", bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurocast/eurocast2013-1.html#BurlacuAK13", isbn13 = "978-3-642-53855-1", URL = "http://dx.doi.org/10.1007/978-3-642-53856-8_36", DOI = "doi:10.1007/978-3-642-53856-8_36", size = "8 pages", abstract = "Evolutionary systems are characterised by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism's ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism's ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behaviour and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimisation step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions.", } @InProceedings{Burlacu:2013:GECCOcomp, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan Winkler and Gabriel Kronberger", title = "Visualization of genetic lineages and inheritance information in genetic programming", booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion", year = "2013", editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks", isbn13 = "978-1-4503-1964-5", keywords = "genetic algorithms, genetic programming", pages = "1351--1358", month = "6-10 " # jul, organisation = "SIGEVO", address = "Amsterdam, The Netherlands", DOI = "doi:10.1145/2464576.2482714", publisher = "ACM", publisher_address = "New York, NY, USA", abstract = "Many studies emphasise the importance of genetic diversity and the need for an appropriate tuning of selection pressure in genetic programming. Additional important aspects are the performance and effects of the genetic operators (crossover and mutation) on the transfer and stabilisation of inherited information blocks during the run of the algorithm. In this context, different ideas about the usage of lineage and genealogical information for improving genetic programming have taken shape in the last decade. Our work builds on those ideas by introducing an evolution tracking framework for assembling genealogical and inheritance graphs of populations. The proposed approach allows detailed investigation of phenomena related to building blocks, size evolution, ancestry and diversity. We introduce the notion of genetic fragments to represent sub-trees that are affected by reproductive operators (mutation and crossover) and present a methodology for tracking such fragments using flexible similarity measures. A fragment matching algorithm was designed to work on both structural and semantic levels, allowing us to gain insight into the exploratory and exploitative behaviour of the evolutionary process. The visualisation part which is the subject of this paper integrates with the framework and provides an easy way of exploring the population history. The paper focuses on a case study in which we investigate the evolution of a solution to a symbolic regression benchmark problem.", notes = "Also known as \cite{2482714} Distributed at GECCO-2013.", } @InProceedings{Burlacu:2015:APCASE, author = "Bogdan Burlacu and Michael Kommenda and Michael Affenzeller", booktitle = "2015 Asia-Pacific Conference on Computer Aided System Engineering (APCASE)", title = "Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming", year = "2015", pages = "152--157", abstract = "Often, the performance of genetic programming (GP) is explained in terms of building blocks -- high-quality solution elements that get gradually assembled into larger and more complex patterns by the evolutionary process. However, the weak theoretical foundations of GP building blocks causes their role in GP evolutionary dynamics to remain still somewhat of a mystery. This paper presents a methodology for identifying GP building blocks based on their respective sample counts in the population (defined as the number of times they were sampled by the recombination operators and included in surviving offspring). Our approach represents a problem-independent way to identify important solution elements based on their influence on the evolutionary process.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/APCASE.2015.34", month = jul, notes = "Also known as \cite{7287011}", } @InProceedings{DBLP:conf/eurocast/BurlacuAK15, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda", title = "On the Effectiveness of Genetic Operations in Symbolic Regression", booktitle = "15th International Conference Computer Aided Systems Theory, EUROCAST 2015", year = "2015", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "9520", series = "Lecture Notes in Computer Science", pages = "367--374", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 8-13", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, Evolutionary dynamics, Algorithm analysis, Symbolic regression", isbn13 = "978-3-319-27339-6", URL = "https://doi.org/10.1007/978-3-319-27340-2_46", DOI = "doi:10.1007/978-3-319-27340-2_46", timestamp = "Thu, 25 May 2017 00:43:36 +0200", biburl = "https://dblp.org/rec/bib/conf/eurocast/BurlacuAK15", bibsource = "dblp computer science bibliography, https://dblp.org", abstract = "This paper describes a methodology for analysing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolution of the best solutions in the population.", } @InCollection{series/sci/BurlacuAWKK15, author = "Bogdan Burlacu and Michael Affenzeller and Stephan M. Winkler and Michael Kommenda and Gabriel Kronberger", title = "Methods for Genealogy and Building Block Analysis in Genetic Programming", bibdate = "2015-06-29", bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci595.html#BurlacuAWKK15", booktitle = "Computational Intelligence and Efficiency in Engineering Systems", publisher = "Springer", year = "2015", volume = "595", editor = "Grzegorz Borowik and Zenon Chaczko and Witold Jacak and Tadeusz Luba", isbn13 = "978-3-319-15719-1", pages = "61--74", series = "Studies in Computational Intelligence", keywords = "genetic algorithms, genetic programming", URL = "http://dx.doi.org/10.1007/978-3-319-15720-7", DOI = "doi:10.1007/978-3-319-15720-7_5", abstract = "Genetic programming gradually assembles high-level structures from low-level entities or building blocks. This chapter describes methods for investigating emergent phenomena in genetic programming by looking at a population's collective behaviour. It details how these methods can be used to trace genotypic changes across lineages and genealogies. Part of the methodology, we present an algorithm for decomposing arbitrary subtrees from the population to their inherited parts, picking up the changes performed by either crossover or mutation across ancestries. This powerful tool creates new possibilities for future theoretical investigations on evolutionary algorithm behavior concerning building blocks and fitness landscape analysis.", } @InProceedings{DBLP:conf/eurocast/BurlacuAKKW17, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler", title = "Analysis of Schema Frequencies in Genetic Programming", booktitle = "16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Part I", year = "2017", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "10671", series = "Lecture Notes in Computer Science", pages = "432--438", address = "Las Palmas de Gran Canaria, Spain", month = feb # " 19-24", publisher = "Springer", note = "Revised Selected Papers", keywords = "genetic algorithms, genetic programming, building blocks, Schema analysis, Symbolic regression, Tree pattern matching, Evolutionary dynamics, Loss of diversity", isbn13 = "978-3-319-74717-0", URL = "https://doi.org/10.1007/978-3-319-74718-7_52", DOI = "doi:10.1007/978-3-319-74718-7_52", timestamp = "Fri, 26 Jan 2018 12:44:51 +0100", biburl = "https://dblp.org/rec/bib/conf/eurocast/BurlacuAKKW17", bibsource = "dblp computer science bibliography, https://dblp.org", size = "7 pages", abstract = "Genetic Programming (GP) schemas are structural templates equivalent to hyperplanes in the search space. Schema theories provide information about the properties of subsets of the population and the behaviour of genetic operators. In this paper we propose a practical methodology to identify relevant schemas and measure their frequency in the population. We demonstrate our approach on an artificial symbolic regression benchmark where the parts of the formula are already known. Experimental results reveal how solutions are assembled within GP and explain diversity loss in GP populations through the proliferation of repeated patterns.", notes = "p433 'propergation of genes in GP' p434 'one point crossover' from OS-GP \cite{Affenzeller:2005:ICANNGA} 'repeated patterns, modules and building blocks' 'to see how the main terms were assembled together by' [evolution]", } @InProceedings{burlacu2018schema, author = "Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Gabriel Kronberger and Stephan Winkler", title = "Schema Analysis in Tree-Based Genetic Programming", booktitle = "Genetic Programming Theory and Practice XV", editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo", year = "2017", pages = "17--37", address = "University of Michigan in Ann Arbor, USA", month = may # " 18--20", organisation = "the Center for the Study of Complex Systems", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-319-90511-2", URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_2", DOI = "doi:10.1007/978-3-319-90512-9_2", abstract = "In this chapter we adopt the concept of schemata from schema theory and use it to analyse population dynamics in genetic programming for symbolic regression. We define schemata as tree-based wild card patterns and we empirically measure their frequencies in the population at each generation. Our methodology consists of two steps: in the first step we generate schemata based on genealogical information about crossover parents and their offspring, according to several possible schema definitions inspired from existing literature. In the second step, we calculate the matching individuals for each schema using a tree pattern matching algorithm. We test our approach on different problem instances and algorithmic flavours and we investigate the effects of different selection mechanisms on the identified schemata and their frequencies.", notes = "GPTP 2017, published 2018", } @PhdThesis{Burlacu:thesis, author = "Bogdan Burlacu", title = "Tracing of Evolutionary Search Trajectories in Complex Hypothesis Spaces", school = "University Linz", year = "2017", address = "Austria", keywords = "genetic algorithms, genetic programming, evolutionary computation, evolutionary tracing, evolutionary dynamics, genotype-phenotype map, system identification, genetische Programmierung, evolutionare Algorithmus, evolutionaere Verfolgung, evolutionaere Dynamik, Genotyp-Phaenotyp Karte, Systemidentifikation", URL = "https://epub.jku.at/obvulihs/content/titleinfo/2246376", URL = "https://epub.jku.at/obvulihs/download/pdf/2246376", URL = "https://epub.jku.at/obvulihs/download/pdf/2246376.pdf", size = "213 pages", abstract = "Understanding the internal functioning of evolutionary algorithms is an essential requirement for improving their performance and reliability. Increased computational resources available in current mainstream computers make it possible for new previously infeasible research directions to be explored. Therefore, a comprehensive theoretical analysis of their mechanisms and dynamics using modern tools becomes possible. Recent algorithmic achievements like offspring selection in combination with linear scaling have enabled genetic programming (GP) to achieve high quality results in system identification in less than 50 generations using populations of only several hundred individuals. Therefore, the active gene pool of evolutionary search remains manageable and may be subjected to new theoretical investigations closely related to genetic programming schema theories, building block hypotheses and bloat theories. Genetic algorithms emulate emergent systems in which complex patterns are formed from an initially simple and random pool of elementary structures. In GP, complexity emerges under the influence of stabilizing selection which preserves the useful genetic variation created by recombination and mutation. The mapping between the structures used for solution representation and the ones used for the evaluation of fitness has a major influence on algorithm behavior. Population-wide effects concerning building blocks, genetic diversity and bloat can be conceptually seen as results of the complex interaction between phenotypic operators (selection) and genotypic operators (mutation and recombination). This coupling known as the variation-selection loop is the main engine for GP emergent behavior and constitutes the main topic of this research. This thesis aims to provide a unified theoretical framework which can explain GP evolution. To this end, it explores the way in which the genotype-phenotype map, in relation with the evolutionary operators (selection, recombination, mutation) determines algorithmic behaviour. As the title suggests, the main contribution consists of a novel tracing framework that makes it possible to determine the exact patterns of building block and gene propagation through the generations and the way smaller elements are gradually assembled into more complex structures by the evolutionary algorithm.", zusammenfassung = "Um die Leistung und Zuverlaessigkeit von evolutionaeren Algorithmen zu verbessern, ist es notwendig deren interne Funktionsweise zu verstehen. Die hohe Rechenleistung aktueller Mainstream-Computer erlaubt neue Forschungsrichtungen zu erkunden, welche frher, aufgrund fehlender Rechenleistung, nicht moeglich waren. Fuer evolutionaere Algorithmen wird es dadurch moeglich, deren interne Mechaniken und Dynamiken umfangreich zu analysieren. Aktuelle algorithmische Fortschritte, wie Nachkommensselektion (Offspring Selection) in Kombination mit linearer Skalierung, ermoeglicht Genetischer Programmierung (GP) hochqualitative Ergebnisse in der Systemidentifikation zu erreichen, in weniger als 50 Generationen bei einer Populationsgroesse von nur wenigen hunderten Individuen. Der dadurch ueberschaubare aktive Genpool der evolutionaeren Suche ermoeglicht neue theoretische Untersuchungen zum GP Schema Theorem, zur Baustein Hypothese und zu Bloat-Theorien. Genetische Algorithmen emulieren emergente Systeme in denen komplexe Muster geformt werden, basierend auf einer initialen, zufaellig generiertem Menge an elementaren Strukturen. In GP entsteht die Komplexitaet durch den Einfluss der stabilisierenden Selektion, welche nuetzliche genetische Variation erhaelt die von Rekombination und Mutation erzeugt werden. Die Zuordnung zwischen Strukturen zur Loesungsrepraesentation (Genotyp) und Fitnessevaluierung (Phaenotyp) beeinflusst das algorithmische Verhalten stark. Populationsweite Auswirkungen betreffend Bausteine, genetischer Diversitaet und Bloat entstehen durch das komplexe Zusammenwirken phaenotypischen Operatoren (Selektion) und genotypischen Operatoren (Rekombination und Mutation). Dieser Mechanismus, bekannt als Variation-Selektion Schleife, ist die treibende Kraft des emergente Verhalten von GP und bildet das Hauptforschungsthema dieser Arbeit. Diese Arbeit zielt darauf ab, einen einheitlichen, theoretischen Rauem zu schaffen welcher Evolution in GP erklaeren kann. Dafuer wird der Einfluss von auf das algorithmische Verhalten untersucht, basierend auf die Zuordnung von Genotyp und Phaenotyp, unter Beruecksichtigung der evolutionaerer Operatoren (Selektion, Rekombination, Mutation). Wie der Titel der Arbeit bereits andeutet, besteht der wichtigste Beitrag aus einem neuartigen System zur UEberwachung und Rueckverfolgung von Genen ueber mehrere Generationen hinweg. Dies ermoeglicht es das Verhalten von Bausteinen zu erforschen, sowie zu erkunden wie bei evolutionaeren Algorithmen aus kleinen Elementen nach und nach komplexere Strukturen gebildet werden.", notes = "In English Supervisor: Michael Affenzeller", } @InProceedings{Burlacu:2019:EUROCAST, author = "Bogdan Burlacu and Lukas Kammerer and Michael Affenzeller and Gabriel Kronberger", title = "Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression", booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019", year = "2019", editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia", volume = "12013", series = "Lecture Notes in Computer Science", pages = "361--369", address = "Las Palmas de Gran Canaria, Spain", month = "17-22 " # feb, publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-3-030-45092-2", DOI = "doi:10.1007/978-3-030-45093-9_44", abstract = "We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees. Based on this hashing approach, we propose a simple diversity-preservation mechanism with promising results on a collection of symbolic regression benchmark problems.", } @InProceedings{Burlacu:2018:GECCO, author = "Bogdan Burlacu and Michael Affenzeller", title = "Schema-based diversification in genetic programming", booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference", year = "2018", editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre", isbn13 = "978-1-4503-5618-3", pages = "1111--1118", address = "Kyoto, Japan", DOI = "doi:10.1145/3205455.3205594", publisher = "ACM", publisher_address = "New York, NY, USA", month = "15-19 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", abstract = "In genetic programming (GP), population diversity represents a key aspect of evolutionary search and a major factor in algorithm performance. In this paper we propose a new schema-based approach for observing and steering the loss of diversity in GP populations. We employ a well-known hyperschema definition from the literature to generate tree structural templates from the population's genealogy, and use them to guide the search via localized mutation within groups of individuals matching the same schema. The approach depends only on genealogy information and is easily integrated with existing GP variants. We demonstrate its potential in combination with Offspring Selection GP (OSGP) on a series of symbolic regression benchmark problems where our algorithmic variant called OSGP-S obtains superior results.", notes = "Also known as \cite{3205594} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)", } @InProceedings{Burlacu:2019:CEC, author = "B. Burlacu and M. Affenzeller and G. Kronberger and M. Kommenda", booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)", title = "Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure", year = "2019", pages = "2175--2182", month = jun, keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/CEC.2019.8790162", abstract = "Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.", notes = "Also known as \cite{8790162}", } @InProceedings{Burlacu:2019:GECCOcomp, author = "Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Michael Affenzeller", title = "Parsimony measures in multi-objective genetic programming for symbolic regression", booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion", year = "2019", editor = "Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu {Ceberio Uribe} and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William {La Cava} and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale", isbn13 = "978-1-4503-6748-6", pages = "338--339", address = "Prague, Czech Republic", DOI = "doi:10.1145/3319619.3322087", publisher = "ACM", publisher_address = "New York, NY, USA", month = "13-17 " # jul, organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming", notes = "Also known as \cite{3322087} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)", } @InProceedings{Burlacu:2020:GECCOcomp, author = "Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda", title = "{Operon C++}: An Efficient Genetic Programming Framework for Symbolic Regression", year = "2020", editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion", pages = "1562--1570", address = "internet", series = "GECCO '20", month = jul # " 8-12", organisation = "SIGEVO", keywords = "genetic algorithms, genetic programming, C++, symbolic regression", isbn13 = "9781450371278", URL = "https://doi.org/10.1145/3377929.3398099", DOI = "doi:10.1145/3377929.3398099", code_url = "https://github.com/heal-research/operon", size = "9 pages", abstract = "Genetic Programming (GP) is a dynamic field of research where empirical testing plays an important role in validating new ideas and algorithms. The ability to easily prototype new algorithms by reusing key components and quickly obtain results is therefore important for the researcher. In this paper we introduce Operon, a C++ GP framework focused on performance, modularity and usability, featuring an efficient linear tree encoding and a scalable concurrency model where each logical thread is responsible for generating a new individual. We model the GP evolutionary process around the concept of an offspring generator, a streaming operator that defines how new individuals are obtained. The approach allows different algorithmic variants to be expressed using high-level constructs within the same generational basic loop. The evaluation routine supports both scalar and dual numbers, making it possible to calculate model derivatives via automatic differentiation. This functionality allows seamless integration with gradient-based local search approaches. We discuss the design choices behind the proposed framework and compare against two other popular GP frameworks, DEAP and HeuristicLab. We empirically show that Operon is competitive in terms of solution quality, while being able to generate results significantly faster.", notes = "See also \cite{LaCava:2021:NeurIPS} Also known as \cite{10.1145/3377929.3398099} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)", } @Article{burlacu:NC, author = "Bogdan Burlacu and Kaifeng Yang and Michael Affenzeller", title = "Population diversity and inheritance in genetic programming for symbolic regression", journal = "Natural Computing", year = "2024", volume = "23", number = "3", pages = "531--566", month = sep, note = "Special Issue: Selected papers from the 27th International Conference on DNA Computing and Molecular Programming", keywords = "genetic algorithms, genetic programming, XAI, Symbolic regression, Supervised learning, Explainable AI, Optimization algorithm, Evolutionary algorithm", URL = "https://rdcu.be/c7n0f", URL = "http://link.springer.com/article/10.1007/s11047-022-09934-x", DOI = "doi:10.1007/s11047-022-09934-x", size = "36 pages", abstract = "we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterised by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.", notes = "HEAL, University of Applied Sciences Upper Austria,Software park 11, 4232 Hagenberg, Austria", } @InProceedings{Burlacu:2024:GPTP, author = "Bogdan Burlacu and Stephan M. Winkler and Michael Affenzeller", title = "Revisiting Gradient-Based Local Search in Symbolic Regression", booktitle = "Genetic Programming Theory and Practice XXI", year = "2024", editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini", series = "Genetic and Evolutionary Computation", pages = "259--273", address = "University of Michigan, USA", month = jun # " 6-8", publisher = "Springer", keywords = "genetic algorithms, genetic programming", isbn13 = "978-981-96-0076-2", URL = "https://heal.heuristiclab.com/news/post/gptp-xxi", DOI = "doi:10.1007/978-981-96-0077-9_13", notes = "Published in 2025 after the workshop", } @InProceedings{burlacu:2024:GECCOcomp, author = "Bogdan Burlacu", title = "Backend-agnostic Tree Evaluation for Genetic Programming", booktitle = "Open Source Software for Evolutionary Computation", year = "2024", editor = "Stefan Wagner and Michael Affenzeller", pages = "1649--1657", address = "Melbourne, Australia", series = "GECCO '24", month = "14-18 " # jul, organisation = "SIGEVO", publisher = "Association for Computing Machinery", publisher_address = "New York, NY, USA", keywords = "genetic algorithms, genetic programming, energy efficiency, symbolic regression", isbn13 = "979-8-4007-0495-6", DOI = "doi:10.1145/3638530.3664161", size = "9 pages", abstract = "The explicit vectorization of the mathematical operations required for fitness calculation can dramatically increase the efficiency of tree-based genetic programming for symbolic regression. In this paper, we introduce a modern software design for the seamless integration of vectorized math libraries with tree evaluation, and we benchmark each library in terms of runtime, solution quality and energy efficiency. The latter, in particular, is an aspect of increasing concern given the growing carbon footprint of AI. With this in mind, we introduce metrics for measuring the energy usage and power draw of the evolutionary algorithm. Our results show that an optimized math backend can decrease energy usage by as much as 35\% (with a proportional decrease in runtime) without any negative effects in the quality of solutions.", notes = "GECCO-2024 EvoOSS A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)", } @InProceedings{Swan:2015:gi, author = "Nathan Burles and Jerry Swan and Edward Bowles and Alexander E. I. Brownlee and Zoltan A. Kocsis and Nadarajen Veerapen", title = "Embedded Dynamic Improvement", booktitle = "Genetic Improvement 2015 Workshop", year = "2015", editor = "William B. Langdon and Justyna Petke and David R. White", pages = "831--832", address = "Madrid", publisher_address = "New York, NY, USA", month = "11-15 " # jul, organisation = "SIGEvo", publisher = "ACM", keywords = "genetic algorithms, genetic programming, Genetic Improvement", isbn13 = "978-1-4503-3488-4", URL = "http://gpbib.cs.ucl.ac.uk/gi2015/embedded_dynamic_improvement.pdf", URL = "http://doi.acm.org/10.1145/2739482.2768423", DOI = "doi:10.1145/2739482.2768423", size = "2 pages", abstract = "We discuss the useful role that can be played by a subtype of improvement programming, which we term Embedded Dynamic Improvement. In this approach, developer-specified variation points define the scope of improvement. A search framework is embedded at these variation points, facilitating the creation of adaptive software that can respond online to changes in its execution environment.", notes = "position paper, slides http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/burles ", } @InProceedings{Burles:2015:SSBSE, author = "Nathan Burles and Edward Bowles and Alexander E. I. Brownlee and Zoltan A. Kocsis and Jerry Swan and Nadarajen Veerapen", title = "Object-Oriented Genetic Improvement for Improved Energy Consumption in {Google Guava}", booktitle = "SSBSE", year = "2015", editor = "Yvan Labiche and Marcio Barros", volume = "9275", series = "LNCS", pages = "255--261", address = "Bergamo, Italy", month = sep # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, Object-oriented programming, Subclass substitution, Liskov Substitution Principle, Energy profiling", isbn13 = "978-3-319-22182-3", URL = "https://dspace.stir.ac.uk/bitstream/1893/22227/1/SSBSE15-oogiiecgg.pdf", URL = "http://hdl.handle.net/1893/22227", DOI = "doi:10.1007/978-3-319-22183-0_20", size = "6 pages", abstract = "In this work we use metaheuristic search to improve Google's Guava library, finding a semantically equivalent version of com.google.common.collect.ImmutableMultimap with reduced energy consumption. Semantics-preserving transformations are found in the source code, using the principle of subtype polymorphism. We introduce a new tool, Opacitor, to deterministically measure the energy consumption, and find that a statistically significant reduction to Guava's energy consumption is possible. We corroborate these results using Jalen, and evaluate the performance of the metaheuristic search compared to an exhaustive search-finding that the same result is achieved while requiring almost 200 times fewer fitness evaluations. Finally, we compare the metaheuristic search to an independent exhaustive search at each variation point, finding that the metaheuristic has superior performance.", notes = "Excludes Java garbage collection and JIT. Exhaustive search (80 CPU days). http://ssbse.info/2015", } @InProceedings{Burles:2015:SSBSEa, author = "Nathan Burles and Edward Bowles and Bobby R. Bruce and Komsan Srivisut", title = "Specialising {Guava's} Cache to Reduce Energy Consumption", booktitle = "SSBSE", year = "2015", editor = "Yvan Labiche and Marcio Barros", volume = "9275", series = "LNCS", pages = "276--281", address = "Bergamo, Italy", month = sep # " 5-7", publisher = "Springer", keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Parameter tuning, Library specialisation, Energy profiling, Reduced power consumption", isbn13 = "978-3-319-22182-3", URL = "http://www.cs.ucl.ac.uk/staff/R.Bruce/Burles2015Specialising.pdf", DOI = "doi:10.1007/978-3-319-22183-0_23", size = "6 pages", abstract = "In this article we use a Genetic Algorithm to perform parameter tuning on Google Guava Cache library, specialising it to OpenTripPlanner. A new tool, Opacitor, is used to deterministically measure the energy consumed, and we find that the energy consumption of OpenTripPlanner may be significantly reduced by tuning the default parameters of Guava's Cache library. Finally we use Jalen, which uses time and CPU load as a proxy to calculate energy consumption, to corroborate these results.", notes = "Not on GP? BRB suggests it is GP. Open Street Map tiles cached. http://ssbse.info/2015", } @InProceedings{Burling-Claridge:2016:CEC, author = "Francine Burling-Claridge and Muhammad Iqbal and Mengjie Zhang", title = "Evolutionary Algorithms for Classification of Mammographic Densities using Local Binary Patterns and Statistical Features", booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)", year = "2016", editor = "Yew-Soon Ong", pages = "3847--3854", address = "Vancouver", month = "24-29 " # jul, publisher = "IEEE Press", keywords = "genetic algorithms, genetic programming", isbn13 = "978-1-5090-0623-6", DOI = "doi:10.1109/CEC.2016.7744277", abstract = "Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Breast density is one of the many factors that lead to an increased risk of breast cancer for women. However, it is difficult for radiologists to provide both accurate and uniform evaluations of different density levels in a large number of mammographic images generated in the screening process. Various computer aided diagnosis systems for digital mammograms have been reported in literature, but very few of them thoroughly investigate mammographic densities. This study presents a thorough analysis of classifying mammographic densities using different local binary patterns and statistical features of digital mammograms in two evolutionary algorithms, i.e., genetic programming and learning classifier systems; and four conventional classification methods, i.e., naive Bayes, decision trees, K-nearest neighbour, and support vector machines. The obtained results show that evolutionary algorithms have potential to solve these challenging real-world tasks. It is found that statistical features produced better results than local binary patterns for the experiments conducted in this study. Further, in genetic programming, the reuse of extracted knowledge from one feature set to another shows statistically significant improvement over the standard approach.", notes = "WCCI2016", } @InProceedings{burnett:2017:CEC, author = "Andrew W. Burnett and Andrew J. Parkes", booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)", title = "Exploring the landscape of the space of heuristics for local search in SAT", year = "2017", editor = "Jose A. Lozano", pages = "2518--2525", address = "Donostia, San Sebastian, Spain", publisher = "IEEE", isbn13 = "978-1-5090-4601-0", abstract = "Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, and so finding good ones is in itself a challenging search problem. A natural method to search for effective heuristics is to represent the heuristic as a small program and then apply evolutionary methods, such as genetic programming. However, the search within the space of heuristics is not well understood, and in particular little is known of the associated search landscapes. In this paper, we consider the domain of propositional satisfiability (SAT), and a generic class of local search methods called `WalkSAT'. We give a language for generating the heuristics; using this we generated over three million heuristics, in a systematic manner, and evaluated their associated fitness values. We then use this data set as the basis for an initial analysis of the landscape of the space of heuristics. We give evidence that the heuristic landscape exhibits clustering. We also consider local search on the space of heuristics and show that it can perform quite well, and could complement genetic programming methods on that space.", keywords = "genetic algorithms, genetic programming, computability, search problems, WalkSAT, clustering, combinatorial optimisation problems, evolutionary methods, local search, propositional satisfiability, search space landscapes, Computer science, Measurement, Reactive power, Systematics", isbn13 = "978-1-5090-4601-0", DOI = "doi:10.1109/CEC.2017.7969611", month = "5-8 " # jun, notes = "IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969611}", } @PhdThesis{Burrow:thesis, author = "Peter Richard Burrow", title = "Hybridising evolution and temporal difference learning", school = "University of Essex", year = "2011", address = "UK", keywords = "genetic algorithms, genetic programming", URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572783", abstract = "This work investigates combinations of two different nature-inspired machine learning algorithms - Evolutionary Algorithms and Temporal Difference Learning. Both algorithms are introduced along with a survey of previous work in the field. A variety of ways of hybridising the two algorithms are considered, falling into two main categories - those where both algorithms operate on the same set of parameters, and those where evolution searches for beneficial parameters to aid Temporal Difference Learning. These potential approaches to hybridisation are explored by applying them to three different problem domains, all loosely linked by the theme of games. The Mountain Car task is a common reinforcement learning benchmark that has been shown to be potentially problematic for neural networks. Ms. Pac-Man is a classic arcade game with a complex virtual environment, and Othello is a popular two-player zero sum board game. Results show that simple hybridisation approaches often do not improve performance, which can be dependent on many factors of the individual algorithms. However, results have also shown that these factors can be successfully tuned by evolution. The main contributions of this thesis are an analysis of the factors that can affect individual algorithm performance, and demonstration of some novel approaches to hybridisation. These consist of use of Evolution Strategies to tune Temporal Difference Learning parameters on multiple problem domains, and evolution of n-tuple configurations for Othello board evaluation. In the latter case, a level of performance was achieved that was competitive with the state of the art.", notes = "Is this GP? EThOS ID: uk.bl.ethos.572783", } @InProceedings{busch:2002:EuroGP, title = "Automatic Generation of Control Programs for Walking Robots Using Genetic Programming", author = "Jens Busch and Jens Ziegler and Wolfgang Banzhaf and Andree Ross and Daniel Sawitzki and Christian Aue", editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi", booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002", volume = "2278", series = "LNCS", pages = "258--267", publisher = "Springer-Verlag", address = "Kinsale, Ireland", publisher_address = "Berlin", month = "3-5 " # apr, year = "2002", keywords = "genetic algorithms, genetic programming", ISBN = "3-540-43378-3", URL = "http://ls2-www.cs.uni-dortmund.de/~sawitzki/AGoCPfWRUGP_Proc.pdf", DOI = "doi:10.1007/3-540-45984-7_25", abstract = "We present the system SIGEL that combines the simulation and visualization of robots with a Genetic Programming system for the automated evolution of walking. It is designed to automatically generate control programs for arbitrary robots without depending on detailed analytical information of the robots' kinematic structure. Different fitness functions as well as a variety of parameters allow the easy and interactive configuration and adaptation of the evolution process and the simulations.", notes = "EuroGP'2002, part of \cite{lutton:2002:GP}", } @Misc{oai:CiteSeerPSU:572931, author = "Stephen F. Bush and Amit B. Kulkarni", title = "Genetically Induced Communication Network Fault Tolerance", howpublished = "Invited Paper: SFI Workshop: Resilient and Adaptive Defence of Computing Networks 2002", year = "2002", keywords = "genetic algorithms, genetic programming", citeseer-isreferencedby = "oai:CiteSeerPSU:98543", citeseer-references = "oai:CiteSeerPSU:506477; oai:CiteSeerPSU:155080; oai:CiteSeerPSU:454895; oai:CiteSeerPSU:439074; oai:CiteSeerPSU:11748; oai:CiteSeerPSU:373663; oai:CiteSeerPSU:185401; oai:CiteSeerPSU:163938; oai:CiteSeerPSU:276739; oai:CiteSeerPSU:470039; oai:CiteSeerPSU:506805; oai:CiteSeerPSU:461659; oai:CiteSeerPSU:462512", annote = "The Pennsylvania State University CiteSeer Archives", language = "en", oai = "oai:CiteSeerPSU:572931", rights = "unrestricted", URL = "http://www.crd.ge.com/~bushsf/ftn/GE-SFI-AdaptiveSecurity.pdf", URL = "http://citeseer.ist.psu.edu/572931.html", size = "9 pages", abstract = "This paper presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes apriori. Apriori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and Algorithmic Information Theory play a key role in understanding and guiding code evolution within the network.", notes = "No confirmation", } @Article{1005412, author = "Stephen F. Bush", title = "Genetically induced communication network fault tolerance", journal = "Complexity", volume = "9", number = "2", year = "2003", ISSN = "1076-2787", pages = "19--33", DOI = "doi:10.1002/cplx.20002", publisher = "John Wiley \& Sons, Inc.", keywords = "genetic algorithms, genetic programming, active networks, algorithmic information theory, Kolmogorov complexity, complexity theory, self-healing networks", URL = "http://www.crd.ge.com/~bushsf/pdfpapers/ComplexityJournal.pdf", abstract = "This article presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes a priori. A priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and algorithmic information theory play a key role in understanding and guiding code evolution within the network.", } @InProceedings{bush:evows05, author = "William S. Bush and Alison A. Motsinger and Scott M. Dudek and Marylyn D. Ritchie", title = "Can neural network constraints in GP provide power to detect genes associated with human disease?", booktitle = "Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}", year = "2005", month = "30 " # mar # "-1 " # apr, editor = "Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and George D. Smith and Giovanni Squillero", series = "LNCS", volume = "3449", publisher = "Springer Verlag", address = "Lausanne, Switzerland", publisher_address = "Berlin", pages = "44--53", keywords = "genetic algorithms, genetic programming, evolutionary computation, ANN", ISBN = "3-540-25396-3", ISSN = "0302-9743", URL = "https://rdcu.be/dEt3y", DOI = "doi:10.1007/978-3-540-32003-6_5", DOI = "doi:10.1007/b106856", size = "10 pages", abstract = "A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. Identifying gene-gene and gene-environment interactions which comprise the genetic architecture for a majority of common diseases is a difficult challenge. To this end, novel computational approaches have been applied to studies of human disease. Previously, a GP neural network (GPNN) approach was employed. Although the GPNN method has been quite successful, a clear comparison of GPNN and GP alone to detect genetic effects has not been made. In this paper, we demonstrate that using NN evolved by GP can be more powerful than GP alone. This is most likely due to the confined search space of the GPNN approach, in comparison to a free form GP. This study demonstrates the benefits of using GP to evolve NN in studies of the genetics of common, complex human disease.", notes = "EvoWorkshops2005 Center for Human Genetics Research, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37232, USA", } @TechReport{butler:1995:eddie, author = "James M. Butler and Edward P. K. Tsang", title = "{EDDIE} Beats the Bookies", institution = "Computer Science, University of Essex", year = "1995", type = "Technical Report", number = "CSM-259", address = "Colchester CO4 3SQ, UK", month = "15 " # dec, keywords = "genetic algorithms, genetic programming", URL = "http://cswww.essex.ac.uk/CSP/papers/CSM-259.ps.Z", URL = "http://citeseer.ist.psu.edu/tsang98eddie.html", abstract = "Betting on a horse race is, in many ways, like investing in a financial market. You invest your money on the horse that you b