%% Genetic Programming Bibliography %%$Revision: 1.6309 $ $Date: 2022/01/22 10:27:45 $ %%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/ @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", 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", 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", 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{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", bibdate = "2014-11-03", bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/iajit/iajit11.html#AbbasiSA14", URL = "http://ccis2k.org/iajit/?option=com_content&task=blogcategory&id=94&Itemid=364", 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.", } @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", volume = "241", pages = "104205", year = "2020", ISSN = "1871-1413", DOI = "doi:10.1016/j.livsci.2020.104205", URL = "http://www.sciencedirect.com/science/article/pii/S1871141320302481", keywords = "genetic algorithms, genetic programming, Precision livestock farming, Evolutionary algorithms, Machine learning, Cattle breeding, Piemontese bovines", 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", } @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 ", } @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{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 = "Supervisors: Meng, H and Cosmas, J", } @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{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", } @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", } @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{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", } @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.", } @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{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", 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}", } @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", 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.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/BRACIS.2019.00059", ISSN = "2643-6264", month = oct, 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", } @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", volume = "141", pages = "112908", year = "2020", ISSN = "0957-4174", DOI = "doi:10.1016/j.eswa.2019.112908", URL = "http://www.sciencedirect.com/science/article/pii/S0957417419306268", keywords = "genetic algorithms, genetic programming, Automatic generation of algorithms, Binary knapsack problem, Hyperheuristic, Generative design of algorithms", 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", volume = "164", pages = "107260", year = "2020", keywords = "genetic algorithms, genetic programming, Electroencephalogram, Fast Fourier Transform, Emotion recognition, Movie clips, Cinema Films", ISSN = "0003-682X", 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}", } @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, Python", month = "12-16 " # jul, organisation = "SIGEVO", address = "Dublin, Ireland", 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", 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.", keywords = "genetic algorithms, genetic programming", DOI = "doi:10.1109/SSCI.2017.8280833", month = nov, notes = "Also known as \cite{8280833}", } @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' University of Passau, Germany", } @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.", } @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", 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}", } @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)", } @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}", } @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", 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", } @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 = "6-21 " # 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", 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.", } @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", 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}", } @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, 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}", } @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", keywords = "genetic algorithms, genetic programming, Image classification, Supervised learning", URL = "http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656", ISSN = "0167-8655", DOI = "doi:10.1016/S0167-8655(01)00128-3", 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{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", volume = "42", number = "21", pages = "7684--7697", year = "2015", 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.", keywords = "genetic algorithms, genetic programming, Evolutionary computation, Finance, Portfolio optimization, Survey", } @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", } @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", } @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", } @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 ues 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", } @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.", } @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", } @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", DOI = "doi:10.1016/S1383-7621(01)00016-9", URL = "http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3", 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, real world applications", month = "7-11 " # jul, organisation = "SIGEVO", address = "Philadelphia, Pennsylvania, USA", DOI = "doi:10.1145/2330163.2330307", publisher = "ACM", publisher_address = "New York, NY, USA", 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)", } @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{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}", } @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", } @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{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", 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 10077_10976", } @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}", } @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 algorithms, 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.", } @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{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", } @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", } @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, genetic algorithms, 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}", } @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: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{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{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}", } @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", 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, 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", 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}", } @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", 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}", } @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", } @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{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}", } @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", } @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", } @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}", } @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 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)", } @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}", } @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 http://ppsn2014.ijs.si/ 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 http://ppsn2016.org", } @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", 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 = "6-21 " # 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", 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.", notes = "https://workshop07.gi-workshops.org", } @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", 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", } @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", 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{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", DOI = "doi:10.1016/j.jbi.2015.01.004", URL = "http://www.sciencedirect.com/science/article/pii/S1532046415000064", 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.", } @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", 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", 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{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}", } @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", } @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{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.", } @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", 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. Dataset https://github.com/GP-ECC/community-smells ICGSE 2020 co-located with ICSE 2020", } @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}", } @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{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", 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", 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", } @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)", } @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{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", DOI = "doi:10.1109/CEC.2015.7257190", 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: Zhang and 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", 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", URL = "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", DOI = "doi:10.1007/978-3-319-99241-9_1", 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{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", } @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}", } @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", 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}", size = "29 pages", } @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 = "1995", 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 = "Berlin, Germany", pages = "205--259", keywords = "genetic algorithms, genetic programming", URL = "http://dynamics.org/~altenber/PAPERS/GGEGPM/", URL = "http://dynamics.org/Altenberg/FILES/LeeGGEGPM.pdf", 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 = " ", } @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{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.", } @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{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", 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, ISSN = "1104-6899", DOI = "doi:10.1016/j.jfe.2009.02.002", URL = "http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1", keywords = "genetic algorithms, genetic programming, Dichotomous choice contingent valuation, Genetic program, Parametric techniques, Proportional hazard model", 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.", } @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