%0 Conference Proceedings
%T Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%D 2011
%8 jun
%F toc:2011:cec
%O CEC 2011
%R doi:10.1109/CEC.2011.5949582
%U http://dx.doi.org/doi:10.1109/CEC.2011.5949582

%0 Conference Proceedings
%T 13th International Symposium MECHATRONIKA, 2010
%D 2010
%8 jun
%F cover:2010:MECHATRONIKA
%O MECHATRONIKA, 2010
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5521207

%0 Journal Article
%T 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
%J Computers & Mathematics with Applications
%D 1997
%V 33
%N 5
%@ 0898-1221
%F tagkey1997126
%O tagkey1997126
%9 journal article
%R doi:10.1016/S0898-1221(97)00025-4
%U http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-D/2/23afe396341b39baf74fcd29db315b46
%U http://dx.doi.org/doi:10.1016/S0898-1221(97)00025-4
%P 126-127

%0 Journal Article
%T Advances in genetic programming, volume 2 : Edited by Peter Angeline and Kenneth Kinnear, Jr. MIT Press, Cambridge, MA. (1996). 538 pages. $50.00
%J Computers & Mathematics with Applications
%D 1997
%V 33
%N 5
%@ 0898-1221
%F tagkey1997129
%O tagkey1997129
%9 journal article
%R doi:10.1016/S0898-1221(97)82933-1
%U http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-T/2/4d3bcc2dda31e9aca679eba60ff95a3a
%U http://dx.doi.org/doi:10.1016/S0898-1221(97)82933-1
%P 129

%0 Journal Article
%T 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
%J Computers & Mathematics with Applications
%D 1999
%V 38
%N 11-12
%@ 0898-1221
%F tagkey1999291
%O tagkey1999291
%9 journal article
%R doi:10.1016/S0898-1221(99)91267-1
%U http://www.sciencedirect.com/science/article/B6TYJ-48778B1-3H/2/1d6f4728f10e14a24f4f28189d15f818
%U http://dx.doi.org/doi:10.1016/S0898-1221(99)91267-1
%P 291-291

%0 Journal Article
%T 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
%J Computers & Mathematics with Applications
%D 1999
%V 37
%N 3
%@ 0898-1221
%F tagkey1999132
%O tagkey1999132
%9 journal article
%R doi:10.1016/S0898-1221(99)90375-9
%U http://www.sciencedirect.com/science/article/B6TYJ-489YTT5-2T/2/13179f12104abafe66b36e402ef358d9
%U http://dx.doi.org/doi:10.1016/S0898-1221(99)90375-9
%P 132-132

%0 Journal Article
%T Genetic programming II: Automatic discovery of reusable programs : By John R. Koza. MIT Press, Cambridge, MA. (1994). 746 pages. $45.00
%J Computers & Mathematics with Applications
%D 1995
%V 29
%N 3
%@ 0898-1221
%F tagkey1995115
%O tagkey1995115
%9 journal article
%R doi:10.1016/0898-1221(95)90099-3
%U http://www.sciencedirect.com/science/article/B6TYJ-48F4PJH-H/2/bd467ac24453cb0b3f9dbbf15075bedb
%U http://dx.doi.org/doi:10.1016/0898-1221(95)90099-3
%P 115-115

%0 Journal Article
%T 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
%J Computers & Mathematics with Applications
%D 1999
%V 38
%N 11-12
%@ 0898-1221
%F tagkey1999282
%O tagkey1999282
%9 journal article
%R doi:10.1016/S0898-1221(99)91189-6
%U http://www.sciencedirect.com/science/article/B6TYJ-48778B1-24/2/ee28594e33abf3bd7c4a9fc997b98492
%U http://dx.doi.org/doi:10.1016/S0898-1221(99)91189-6
%P 282-282

%0 Journal Article
%T Automated generation of robust error recovery logic in assembly systems using genetic programming : Cem M. Baydar, Kazuhiro Saitou, v20, n1, 2001, pp55-68
%J Journal of Manufacturing Systems
%D 2002
%V 21
%N 6
%@ 0278-6125
%F tagkey2002475
%O tagkey2002475
%9 journal article
%R doi:10.1016/S0278-6125(02)80094-2
%U http://www.sciencedirect.com/science/article/B6VJD-4920DSC-1N/2/93bf79c7eb0d6ad94d169ed1b37ec77f
%U http://dx.doi.org/doi:10.1016/S0278-6125(02)80094-2
%P 475-476

%0 Generic
%T Intelligent Machines Evolutionary algorithm outperforms deep-learning machines at video games
%D 2018
%8 18 jul
%I MIT Technolgy Review
%F 2018:MITtechreview
%X 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 \citeWilson:2018:GECCO
%K genetic algorithms, genetic programming

%0 Journal Article
%T Evolutionary Algorithms for Software Testing in Facebook
%J SIGEVOlution
%D 2018
%8 December
%V 11
%N 2
%@ 1931-8499
%F Sapienz:2018:sigevolution
%X 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!
%K genetic algorithms, genetic programming, SBSE, mobile computing, smart phone
%9 journal article
%R doi:10.1145/3264700.3264702
%U http://www.sigevolution.org/issues/SIGEVOlution1102.pdf
%U http://dx.doi.org/doi:10.1145/3264700.3264702
%P 7

%0 Generic
%T Store Steel 165 years
%I Internal information magazine
%F glasilo_1_16_ang
%K genetic algorithms, genetic programming
%U http://www.store-steel.si/Data/InterniInformativniCasopis/glasilo_1_16_ang.pdf

%0 Journal Article
%T Genetic programming-based self-reconfiguration planning for metamorphic robot
%A Ababsa, Tarek
%A Djedl, Noureddine
%A Duthen, Yves
%J International Journal of Automation and Computing
%D 2018
%V 15
%N 4
%F ababsa:2018:IJAC
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s11633-016-1049-4
%U http://link.springer.com/article/10.1007/s11633-016-1049-4
%U http://dx.doi.org/doi:10.1007/s11633-016-1049-4

%0 Conference Proceedings
%T A Survey of Pattern Recognition Applications in Cancer Diagnosis
%A Abarghouei, Amir Atapour
%A Ghanizadeh, Afshin
%A Sinaie, Saman
%A Shamsuddin, Siti Mariyam
%S International Conference of Soft Computing and Pattern Recognition, SOCPAR ’09
%D 2009
%8 dec
%F Abarghouei:2009:SOCPAR
%X 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.
%K genetic algorithms, genetic programming, artificial neural networks, cancer diagnosis, image processing, medical images, pattern recognition applications, wavelet analysis, cancer, medical image processing, pattern recognition
%R doi:10.1109/SoCPaR.2009.93
%U http://dx.doi.org/doi:10.1109/SoCPaR.2009.93
%P 448-453

%0 Journal Article
%T Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination
%A Abba, S. I.
%A Hadi, Sinan Jasim
%A Sammen, Saad Sh.
%A Salih, Sinan Q.
%A Abdulkadir, R. A.
%A Pham, Quoc Bao
%A Yaseen, Zaher Mundher
%J Journal of Hydrology
%D 2020
%V 587
%@ 0022-1694
%F ABBA:2020:JH
%X 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
%K genetic algorithms, genetic programming, Water quality index, Watershed management, Extreme Gradient Boosting, Extreme Learning Machine, Kinta River
%9 journal article
%R doi:10.1016/j.jhydrol.2020.124974
%U http://www.sciencedirect.com/science/article/pii/S0022169420304340
%U http://dx.doi.org/doi:10.1016/j.jhydrol.2020.124974
%P 124974

%0 Journal Article
%T Multi block based image watermarking in wavelet domain using genetic programming
%A Abbasi, Almas
%A Seng, Woo Chaw
%A Ahmad, Imran Shafiq
%J The International Arab Journal of Information Technology
%D 2014
%V 11
%N 6
%F journals/iajit/AbbasiSA14
%X 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.
%K genetic algorithms, genetic programming, Robust watermark, wavelet domain, digital watermarking, HVS
%9 journal article
%U http://ccis2k.org/iajit/?option=com_content&task=blogcategory&id=94&Itemid=364
%P 582-589

%0 Journal Article
%T Estimation of hydraulic jump on corrugated bed using artificial neural networks and genetic programming
%A Abbaspour, Akram
%A Farsadizadeh, Davood
%A Ghorbani, Mohammad Ali
%J Water Science and Engineering
%D 2013
%V 6
%N 2
%@ 1674-2370
%F Abbaspour:2013:WSE
%X 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.
%K genetic algorithms, genetic programming, artificial neural networks, corrugated bed, Froude number, hydraulic jump
%9 journal article
%R doi:10.3882/j.issn.1674-2370.2013.02.007
%U http://www.sciencedirect.com/science/article/pii/S1674237015302362
%U http://dx.doi.org/doi:10.3882/j.issn.1674-2370.2013.02.007
%P 189-198

%0 Conference Proceedings
%T AntTAG: A New Method to Compose Computer Programs Using Colonies of Ants
%A Abbass, H.
%A Hoai, N. X.
%A McKay, R. I. (Bob)
%S Proceedings, 2002 World Congress on Computational Intelligence
%D 2002
%V 2
%I IEEE Press
%F Abbass:2002:WCCI
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2002.1004490
%U http://sc.snu.ac.kr/PAPERS/TAGACOcec02.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2002.1004490
%P 1654-1666

%0 Conference Proceedings
%T Scout Algorithms and Genetic Algorithms: A Comparative Study
%A Abbattista, Fabio
%A Carofiglio, Valeria
%A Koppen, Mario
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F abbattista:1999:SAGAACS
%K genetic algorithms and classifier systems, poster papers
%U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-803.pdf
%P 769

%0 Conference Proceedings
%T Evolutionary Computing for Metals Properties Modelling
%A Abbod, Maysam F.
%A Mahfouf, M.
%A Linkens, D. A.
%A Sellars, C. M.
%S THERMEC 2006
%S Materials Science Forum
%D 2006
%8 jul 4 8
%V 539
%I Trans Tech Publications
%C Vancouver
%G en
%F abbod2007
%X 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.
%K genetic algorithms, genetic programming, strain, alloy materials, modeling, material property, stress
%R doi:10.4028/www.scientific.net/MSF.539-543.2449
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.6271
%U http://dx.doi.org/doi:10.4028/www.scientific.net/MSF.539-543.2449
%P 2449-2454

%0 Conference Proceedings
%T A GP Approach for Precision Farming
%A Abbona, Francesca
%A Vanneschi, Leonardo
%A Bona, Marco
%A Giacobini, Mario
%Y Jin, Yaochu
%S 2020 IEEE Congress on Evolutionary Computation, CEC 2020
%D 2020
%8 19 24 jul
%I IEEE Press
%C internet
%F Abbona:2020:CEC
%X 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.
%K genetic algorithms, genetic programming, Cows, Precision Livestock Farming, PLF, Cattle Breeding, Piedmontese Bovines
%R doi:10.1109/CEC48606.2020.9185637
%U http://dx.doi.org/doi:10.1109/CEC48606.2020.9185637
%P paperid24248

%0 Journal Article
%T Towards modelling beef cattle management with Genetic Programming
%A Abbona, Francesca
%A Vanneschi, Leonardo
%A Bona, Marco
%A Giacobini, Mario
%J Livestock Science
%D 2020
%V 241
%@ 1871-1413
%F ABBONA:2020:LS
%X 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
%K genetic algorithms, genetic programming, Precision livestock farming, Evolutionary algorithms, Machine learning, Cattle breeding, Piemontese bovines
%9 journal article
%R doi:10.1016/j.livsci.2020.104205
%U http://www.sciencedirect.com/science/article/pii/S1871141320302481
%U http://dx.doi.org/doi:10.1016/j.livsci.2020.104205
%P 104205

%0 Conference Proceedings
%T Niches as a GA divide-and-conquer strategy
%A Abbott, R. J.
%Y Chapman, Art
%Y Myers, Leonard
%S Proceedings of the Second Annual AI Symposium for the California State University
%D 1991
%I California State University
%F aicsu-91:abbot
%K genetic algorithms, genetic programming
%P 133-136

%0 Conference Proceedings
%T Object-Oriented Genetic Programming, An Initial Implementation
%A Abbott, Russell J.
%S Proceedings of the Sixth International Conference on Computational Intelligence and Natural Computing
%D 2003
%8 sep 26 30
%C Embassy Suites Hotel and Conference Center, Cary, North Carolina USA
%F abbott:2003:OOGP
%X 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.
%K genetic algorithms, genetic programming, object-oriented, STGP
%U http://abbott.calstatela.edu/PapersAndTalks/OOGP.pdf

%0 Conference Proceedings
%T Guided Genetic Programming
%A Abbott, Russ
%A Guo, Jiang
%A Parviz, Behzad
%S The 2003 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA’03)
%D 2003
%8 23 26 jun
%I CSREA Press
%C las Vegas
%F abbott:2003:MLMTA
%X 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.
%K genetic algorithms, genetic programming, guided genetic programming
%U http://abbott.calstatela.edu/PapersAndTalks/Guided%20Genetic%20Programming.pdf

%0 Conference Proceedings
%T Genetic Programming Reconsidered
%A Abbott, Russ
%A Parviz, Behzad
%A Sun, Chengyu
%Y Arabnia, Hamid R.
%Y Mun, Youngsong
%S 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
%D 2004
%8 jun 21 24
%V 2
%I CSREA Press
%C Las Vegas, Nevada, USA
%@ 1-932415-32-7
%F DBLP:conf/icai/AbbottPS04
%X 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.
%K genetic algorithms, genetic programming, evolutionary pathway, fitness function, teleological evolution, adaptive evolution
%U http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf
%P 1113-1116

%0 Conference Proceedings
%T Gene Expression Programming Algorithm for Transient Security Classification
%A Abdelaziz, Almoataz Y.
%A Mekhamer, S. F.
%A Khattab, H. M.
%A Badr, M. L. A.
%A Panigrahi, Bijaya Ketan
%Y Panigrahi, Bijaya Ketan
%Y Das, Swagatam
%Y Suganthan, Ponnuthurai Nagaratnam
%Y Nanda, Pradipta Kumar
%S Proceedings of the Third International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012
%S Lecture Notes in Computer Science
%D 2012
%8 dec 20 22
%V 7677
%I Springer
%C Bhubaneswar, India
%F Abdelaziz:2012:SEMCCO
%X 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.
%K genetic algorithms, genetic programming, gene expression programming
%R doi:10.1007/978-3-642-35380-2_48
%U http://works.bepress.com/almoataz_abdelaziz/42
%U http://dx.doi.org/doi:10.1007/978-3-642-35380-2_48
%P 406-416

%0 Journal Article
%T Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors
%A Abdelbaky, Ibrahim Z.
%A Al-Sadek, Ahmed F.
%A Badr, Amr A.
%J International Journal of Advanced Computer Science and Applications
%D 2018
%V 9
%N 11
%I The Science and Information (SAI) Organization
%G eng
%F Abdelbaky:2018:IJACSA
%X 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.
%K genetic algorithms, genetic programming, cdk inhibitors, random forest classification
%9 journal article
%R doi:10.14569/IJACSA.2018.091132
%U http://thesai.org/Downloads/Volume9No11/Paper_32-Applying_Machine_Learning_Techniques.pdf
%U http://dx.doi.org/doi:10.14569/IJACSA.2018.091132
%P 229-235

%0 Conference Proceedings
%T Applying Co-Evolutionary Particle Swam Optimization to the Egyptian Board Game Seega
%A Abdelbar, Ashraf M.
%A Ragab, Sherif
%A Mitri, Sara
%Y Cho, Sung-Bae
%Y Hoai, Nguyen Xuan
%Y Shan, Yin
%S Proceedings of The First Asian-Pacific Workshop on Genetic Programming
%D 2003
%8 August
%C Rydges (lakeside) Hotel, Canberra, Australia
%@ 0-9751724-0-9
%F Abdelbar:aspgp03
%X 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.
%K Particle Swarm Optimisation, Co-evolution, Game
%U http://infoscience.epfl.ch/record/90539/
%P 9-15

%0 Conference Proceedings
%T A Genetic Programming Ensemble Method for Learning Dynamical System Models
%A Abdelbari, Hassan
%A Shafi, Kamran
%S Proceedings of the 8th International Conference on Computer Modeling and Simulation
%D 2017
%I ACM
%C Canberra, Australia
%F Abdelbari:2017:ICCMS
%X 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.
%K genetic algorithms, genetic programming, complex dynamical systems, modelling and simulation, symbolic regression
%R doi:10.1145/3036331.3036336
%U http://doi.acm.org/10.1145/3036331.3036336
%U http://dx.doi.org/doi:10.1145/3036331.3036336
%P 47-51

%0 Journal Article
%T Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming
%A Abdelmalek, Wafa
%A Ben Hamida, Sana
%A Abid, Fathi
%J Journal of Applied Mathematics and Decision Sciences
%D 2009
%I Hindawi Publishing Corporation
%@ 11739126
%G eng
%F Abdelmalek:2009:JAMDS
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1155/2009/179230
%U http://downloads.hindawi.com/journals/ads/2009/179230.pdf
%U http://dx.doi.org/doi:10.1155/2009/179230

%0 Journal Article
%T Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers
%A Abdelmutalab, Ameen
%A Assaleh, Khaled
%A El-Tarhuni, Mohamed
%J Physical Communication
%D 2016
%V 21
%@ 1874-4907
%F Abdelmutalab:2016:PC
%X 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.
%K genetic algorithms, genetic programming, Modulation classification, Hierarchical polynomial classifiers, High order cumulants, Adaptive modulation
%9 journal article
%R doi:10.1016/j.phycom.2016.08.001
%U http://www.sciencedirect.com/science/article/pii/S1874490716301094
%U http://dx.doi.org/doi:10.1016/j.phycom.2016.08.001
%P 10-18

%0 Conference Proceedings
%T Tackling Dead End Scenarios by Improving Follow Gap Method with Genetic Programming
%A Abdelwhab, Mohamed
%A Abouelsoud, A. A.
%A Elbab, Ahmed M. R. Fath
%S 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)
%D 2018
%8 sep
%C Nara, Japan
%F Abdelwhab:2018:SICE
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.23919/SICE.2018.8492687
%U http://dx.doi.org/doi:10.23919/SICE.2018.8492687
%P 1566-1571

%0 Thesis
%T Artificial intelligence system for continuous affect estimation from naturalistic human expressions
%A Abd Gaus, Yona Falinie
%D 2018
%8 jan
%C London, UK
%C Brunel University
%F AbdGaus:thesis
%X 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.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://bura.brunel.ac.uk/handle/2438/16348

%0 Conference Proceedings
%T Linear and Non-Linear Multimodal Fusion for Continuous Affect Estimation In-the-Wild
%A Gaus, Yona Falinie A.
%A Meng, Hongying
%S 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018)
%D 2018
%8 may
%F AbdGaus:2018:ieeeFG
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/FG.2018.00079
%U http://dx.doi.org/doi:10.1109/FG.2018.00079
%P 492-498

%0 Conference Proceedings
%T Fast convergence strategy for Particle Swarm Optimization using spread factor
%A Latiff, I. Abd
%A Tokhi, M. O.
%S Evolutionary Computation, 2009. CEC ’09. IEEE Congress on
%D 2009
%8 may
%F 4983280
%K PSO velocity equation, fast convergence strategy, inertia weight, particle swarm optimization, spread factor, convergence, particle swarm optimisation
%R doi:10.1109/CEC.2009.4983280
%U http://dx.doi.org/doi:10.1109/CEC.2009.4983280
%P 2693-2700

%0 Journal Article
%T Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing
%A Abdolahzare, Zahra
%A Mehdizadeh, Saman Abdanan
%J Neural Computing and Applications
%D 2018
%V 29
%N 2
%F journals/nca/AbdolahzareM18
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s00521-016-2450-1
%U http://dx.doi.org/doi:10.1007/s00521-016-2450-1
%P 363-375

%0 Journal Article
%T Genetic programming for credit scoring: The case of Egyptian public sector banks
%A Abdou, Hussein A.
%J Expert Systems with Applications
%D 2009
%V 36
%N 9
%@ 0957-4174
%F Abdou200911402
%X 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.
%K genetic algorithms, genetic programming, Credit scoring, Weight of evidence, Egyptian public sector banks
%9 journal article
%R doi:10.1016/j.eswa.2009.01.076
%U http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec
%U http://dx.doi.org/doi:10.1016/j.eswa.2009.01.076
%P 11402-11417

%0 Thesis
%T Credit Scoring Models for Egyptian Banks: Neural Nets and Genetic Programming versus Conventional Techniques
%A Abdou, Hussein Ali Hussein
%D 2009
%8 apr
%C UK
%C Plymouth Business School, University of Plymouth
%F 2009AbdouEthosPhD
%X 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.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U https://pearl.plymouth.ac.uk/bitstream/handle/10026.1/379/2009AbdouEthosPhD.pdf

%0 Conference Proceedings
%T An Empirical Comparison of Code Size Limit in Auto-Constructive Artificial Life
%A Abdul rahim, A. B.
%A Teo, J.
%A Saudi, A.
%S 2006 IEEE Conference on Cybernetics and Intelligent Systems
%D 2006
%8 jun
%I IEEE
%C Bangkok
%@ 1-4244-0023-6
%F Abdul-Rahim:2006:ccis
%X 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
%K genetic algorithms, genetic programming, Push, Breve, ALife, PushGP
%R doi:10.1109/ICCIS.2006.252308
%U http://dx.doi.org/doi:10.1109/ICCIS.2006.252308
%P 1-6

%0 Conference Proceedings
%T Genetic programming for evolving programs with loop structures for classification tasks
%A Abdulhamid, Fahmi
%A Neshatian, Kourosh
%A Zhang, Mengjie
%S 5th International Conference on Automation, Robotics and Applications (ICARA 2011)
%D 2011
%8 June 8 dec
%C Wellington, New Zealand
%F Abdulhamid:2011:ICARA
%X 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.
%K 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
%R doi:10.1109/ICARA.2011.6144882
%U http://dx.doi.org/doi:10.1109/ICARA.2011.6144882
%P 202-207

%0 Conference Proceedings
%T Evolving Genetic Programming Classifiers with Loop Structures
%A Abdulhamid, Fahmi
%A Song, Andy
%A Neshatian, Kourosh
%A Zhang, Mengjie
%Y Li, Xiaodong
%S Proceedings of the 2012 IEEE Congress on Evolutionary Computation
%D 2012
%8 October 15 jun
%C Brisbane, Australia
%@ 0-7803-8515-2
%F Abdulhamid:2012:CEC
%X 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.
%K genetic algorithms, genetic programming, Conflict of Interest Papers, Classification, clustering, data analysis and data mining
%R doi:10.1109/CEC.2012.6252877
%U http://dx.doi.org/doi:10.1109/CEC.2012.6252877
%P 2710-2717

%0 Conference Proceedings
%T Genetic programming hyper-heuristic for solving dynamic production scheduling problem
%A Abednego, Luciana
%A Hendratmo, Dwi
%S International Conference on Electrical Engineering and Informatics (ICEEI 2011)
%D 2011
%8 17 19 jul
%C Bandung, Indonesia
%F Abednego:2011:ICEEI
%X 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.
%K 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
%R doi:10.1109/ICEEI.2011.6021768
%U http://dx.doi.org/doi:10.1109/ICEEI.2011.6021768
%P K3-2

%0 Book Section
%T Using a Genetic Algorithm to Select Beam Configurations for Radiosurgery of the Brain
%A Abernathy, Neil
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%D 2000
%8 jun
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F abernathy:2000:UGASBCRB
%K genetic algorithms
%P 1-7

%0 Journal Article
%T Comparing Predictability of Genetic Programming and ANFIS on Drilling Performance Modeling for GFRP Composites
%A Abhishek, Kumar
%A Panda, Biranchi Narayan
%A Datta, Saurav
%A Mahapatra, Siba Sankar
%J Procedia Materials Science
%D 2014
%V 6
%@ 2211-8128
%F Abhishek:2014:PMS
%O 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014)
%X 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.
%K genetic algorithms, genetic programming, Glass fibre reinforced polymer (GFRP), Adaptive Neuro Fuzzy Inference System (ANFIS), GPTIPS.
%9 journal article
%R doi:10.1016/j.mspro.2014.07.069
%U http://www.sciencedirect.com/science/article/pii/S2211812814004349
%U http://dx.doi.org/doi:10.1016/j.mspro.2014.07.069
%P 544-550

%0 Book Section
%T Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models
%A Abid, Fathi
%A Abdelmalek, Wafa
%A Ben Hamida, Sana
%E Ventura, Sebastian
%B Genetic Programming - New Approaches and Successful Applications
%D 2012
%I InTech
%F Abid:2012:GPnew
%K genetic algorithms, genetic programming
%R doi:10.5772/48148
%U http://dx.doi.org/doi:10.5772/48148
%P 141-172

%0 Journal Article
%T Estimation of dynamic viscosity of natural gas based on genetic programming methodology
%A Abooali, Danial
%A Khamehchi, Ehsan
%J Journal of Natural Gas Science and Engineering
%D 2014
%V 21
%@ 1875-5100
%F Abooali:2014:JNGSE
%X 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.
%K genetic algorithms, genetic programming, Natural gas, Dynamic viscosity, Correlation
%9 journal article
%R doi:10.1016/j.jngse.2014.11.006
%U http://www.sciencedirect.com/science/article/pii/S1875510014003394
%U http://dx.doi.org/doi:10.1016/j.jngse.2014.11.006
%P 1025-1031

%0 Journal Article
%T A new empirical model for estimation of crude oil/brine interfacial tension using genetic programming approach
%A Abooali, Danial
%A Sobati, Mohammad Amin
%A Shahhosseini, Shahrokh
%A Assareh, Mehdi
%J Journal of Petroleum Science and Engineering
%D 2019
%V 173
%@ 0920-4105
%F ABOOALI:2019:JPSE
%X 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
%K genetic algorithms, genetic programming, Interfacial tension, Correlation, Crude oil, Brine, Genetic programming (GP)
%9 journal article
%R doi:10.1016/j.petrol.2018.09.073
%U http://www.sciencedirect.com/science/article/pii/S0920410518308283
%U http://dx.doi.org/doi:10.1016/j.petrol.2018.09.073
%P 187-196

%0 Journal Article
%T Characterization of physico-chemical properties of biodiesel components using smart data mining approaches
%A Abooali, Danial
%A Soleimani, Reza
%A Gholamreza-Ravi, Saeed
%J Fuel
%D 2020
%V 266
%@ 0016-2361
%F ABOOALI:2020:Fuel
%X 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
%K genetic algorithms, genetic programming, Fatty acid ester, Density, Speed of sound, Isentropic and isothermal compressibility, Stochastic gradient boosting
%9 journal article
%R doi:10.1016/j.fuel.2020.117075
%U http://www.sciencedirect.com/science/article/pii/S0016236120300703
%U http://dx.doi.org/doi:10.1016/j.fuel.2020.117075
%P 117075

%0 Journal Article
%T New predictive method for estimation of natural gas hydrate formation temperature using genetic programming
%A Abooali, Danial
%A Khamehchi, Ehsan
%J Neural Comput. Appl.
%D 2019
%V 31
%N 7
%F DBLP:journals/nca/AbooaliK19
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s00521-017-3208-0
%U https://doi.org/10.1007/s00521-017-3208-0
%U http://dx.doi.org/doi:10.1007/s00521-017-3208-0
%P 2485-2494

%0 Conference Proceedings
%T Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming
%A Abraham, Ajith
%A Ramos, Vitorino
%Y Sarker, Ruhul
%Y Reynolds, Robert
%Y Abbass, Hussein
%Y Tan, Kay Chen
%Y McKay, Bob
%Y Essam, Daryl
%Y Gedeon, Tom
%S Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%D 2003
%8 August 12 dec
%I IEEE Press
%C Canberra
%@ 0-7803-7804-0
%F abraham:2003:CEC
%X 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.
%K 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
%R doi:10.1109/CEC.2003.1299832
%U http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2003.1299832
%P 1384-1391

%0 Report
%T Soft Computing Models for Network Intrusion Detection Systems
%A Abraham, Ajith
%A Jain, Ravi
%D 2004
%8 13 may 2004
%I OSU
%F abraham:2004:0405046
%O 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
%X 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.
%K genetic algorithms, genetic programming, Cryptography and Security
%U http://www.softcomputing.net/saman2.pdf

%0 Journal Article
%T Business Intelligence from Web Usage Mining
%A Abraham, Ajith
%J Journal of Information & Knowledge Management
%D 2003
%V 2
%N 4
%F Abraham:2003:JIKM
%X 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.
%K genetic algorithms, genetic programming, Web mining, knowledge discovery, business intelligence, hybrid soft computing, neuro-fuzzy-genetic system
%9 journal article
%R doi:10.1142/S0219649203000565
%U http://www.softcomputing.net/jikm.pdf
%U http://dx.doi.org/doi:10.1142/S0219649203000565
%P 375-390

%0 Generic
%T Business Intelligence from Web Usage Mining
%A Abraham, Ajith
%D 2004
%8 may  06
%F oai:arXiv.org:cs/0405030
%X 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.
%U http://arxiv.org/abs/cs/0405030

%0 Book Section
%T Evolutionary Computation in Intelligent Network Management
%A Abraham, Ajith
%E Ghosh, Ashish
%E Jain, Lakhmi C.
%B Evolutionary Computing in Data Mining
%S Studies in Fuzziness and Soft Computing
%D 2004
%V 163
%I Springer
%@ 3-540-22370-3
%F abraham:2004:ECDM
%X 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.
%K 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
%U http://www.softcomputing.net/ec_web-chapter.pdf
%P 189-210

%0 Book Section
%T Evolutionary Computation: from Genetic Algorithms to Genetic Programming
%A Abraham, Ajith
%A Nedjah, Nadia
%A de Macedo Mourelle, Luiza
%E Nedjah, Nadia
%E Abraham, Ajith
%E de Macedo Mourelle, Luiza
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%D 2006
%V 13
%I Springer
%C Germany
%@ 3-540-29849-5
%F intro:2006:GSP
%X 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).
%K genetic algorithms, genetic programming, cartesian genetic programming
%R doi:10.1007/3-540-32498-4_1
%U http://www.softcomputing.net/gpsystems.pdf
%U http://dx.doi.org/doi:10.1007/3-540-32498-4_1
%P 1-20

%0 Book Section
%T Evolving Intrusion Detection Systems
%A Abraham, Ajith
%A Grosan, Crina
%E Nedjah, Nadia
%E Abraham, Ajith
%E de Macedo Mourelle, Luiza
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%D 2006
%V 13
%I Springer
%C Germany
%@ 3-540-29849-5
%F abraham:2006:GSP
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/3-540-32498-4_3
%U http://falklands.globat.com/~softcomputing.net/ids-chapter.pdf
%U http://dx.doi.org/doi:10.1007/3-540-32498-4_3
%P 57-79

%0 Conference Proceedings
%T Genetic Programming Approach for Fault Modeling of Electronic Hardware
%A Abraham, Ajith
%A Grosan, Crina
%Y Corne, David
%Y Michalewicz, Zbigniew
%Y Dorigo, Marco
%Y Eiben, Gusz
%Y Fogel, David
%Y Fonseca, Carlos
%Y Greenwood, Garrison
%Y Chen, Tan Kay
%Y Raidl, Guenther
%Y Zalzala, Ali
%Y Lucas, Simon
%Y Paechter, Ben
%Y Willies, Jennifier
%Y Guervos, Juan J. Merelo
%Y Eberbach, Eugene
%Y McKay, Bob
%Y Channon, Alastair
%Y Tiwari, Ashutosh
%Y Volkert, L. Gwenn
%Y Ashlock, Dan
%Y Schoenauer, Marc
%S Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%D 2005
%8 February 5 sep
%V 2
%I IEEE Press
%C Edinburgh, UK
%@ 0-7803-9363-5
%F abraham:2005:CEC
%X 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.
%K genetic algorithms, genetic programming, MEP, ANN, LGP
%R doi:10.1109/CEC.2005.1554875
%U http://www.softcomputing.net/cec05.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2005.1554875
%P 1563-1569

%0 Journal Article
%T Decision Support Systems Using Ensemble Genetic Programming
%A Abraham, Ajith
%A Grosan, Crina
%J Journal of Information & Knowledge Management (JIKM)
%D 2006
%8 dec
%V 5
%N 4
%@ 0219-6492
%F journals/jikm/AbrahamG06
%O Special topic: Knowledge Discovery Using Advanced Computational Intelligence Tools
%X 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.
%K genetic algorithms, genetic programming, gene expression programming, Decision support systems, ensemble systems, evolutionary multi-objective optimisation
%9 journal article
%R doi:10.1142/S0219649206001566
%U http://dx.doi.org/doi:10.1142/S0219649206001566
%P 303-313

%0 Journal Article
%T D-SCIDS: Distributed soft computing intrusion detection system
%A Abraham, Ajith
%A Jain, Ravi
%A Thomas, Johnson
%A Han, Sang Yong
%J Journal of Network and Computer Applications
%D 2007
%8 jan
%V 30
%N 1
%F Abraham:2007:JNCS
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1016/j.jnca.2005.06.001
%U http://dx.doi.org/doi:10.1016/j.jnca.2005.06.001
%P 81-98

%0 Conference Proceedings
%T Real time intrusion prediction, detection and prevention programs
%A Abraham, Ajith
%S IEEE International Conference on Intelligence and Security Informatics, ISI 2008
%D 2008
%8 jun
%F Abraham:2008:ieeeISI
%O IEEE ISI 2008 Invited Talk (VI)
%X 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.
%K 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
%R doi:10.1109/ISI.2008.4565018
%U http://dx.doi.org/doi:10.1109/ISI.2008.4565018
%P xli-xlii

%0 Conference Proceedings
%T Programming Risk Assessment Models for Online Security Evaluation Systems
%A Abraham, Ajith
%A Grosan, Crina
%A Snasel, Vaclav
%S 11th International Conference on Computer Modelling and Simulation, UKSIM ’09
%D 2009
%8 25 27 mar
%F Abraham:2009:UKSIM
%X 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.
%K genetic algorithms, genetic programming, genetic programming methods, human reasoning, online security evaluation systems, perception process, programming risk assessment models, risk management, security of data
%R doi:10.1109/UKSIM.2009.75
%U http://dx.doi.org/doi:10.1109/UKSIM.2009.75
%P 41-46

%0 Conference Proceedings
%T Hierarchical Takagi-Sugeno Models for Online Security Evaluation Systems
%A Abraham, Ajith
%A Grosan, Crina
%A Liu, Hongbo
%A Chen, Yuehui
%S Fifth International Conference on Information Assurance and Security, IAS ’09
%D 2009
%8 aug
%V 1
%F Abraham:2009:IAS
%X 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.
%K 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
%R doi:10.1109/IAS.2009.348
%U http://dx.doi.org/doi:10.1109/IAS.2009.348
%P 687-692

%0 Book Section
%T Complimentary Selection as an Alternative Method for Population Reproduction
%A Abrams, Zoe
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%D 2000
%8 jun
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F abrams:2000:CSAMPR
%K genetic algorithms, genetic programming
%P 8-15

%0 Conference Proceedings
%T Classification using Cultural Co-Evolution and Genetic Programming
%A Abramson, Myriam
%A Hunter, Lawrence
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F abramson:1996:cccGP
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap30.pdf
%P 249-254

%0 Journal Article
%T Automatic Modulation Classification Using Moments And Likelihood Maximization
%A Abu-Romoh, M.
%A Aboutaleb, A.
%A Rezki, Z.
%J IEEE Communications Letters
%D 2018
%@ 1089-7798
%F Abu-Romoh:2018:ieeeCL
%X 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.
%K genetic algorithms, genetic programming, Feature extraction, Machine learning algorithms, Modulation, Probability density function, Receivers, Signal to noise ratio, Support vector machines
%9 journal article
%R doi:10.1109/LCOMM.2018.2806489
%U http://dx.doi.org/doi:10.1109/LCOMM.2018.2806489

%0 Conference Proceedings
%T New universal gate library for synthesizing reversible logic circuit using genetic programming
%A Abubakar, Mustapha Yusuf
%A Jung, Low Tang
%A Zakaria, Mohamed Nordin
%A Younesy, Ahmed
%A Abdel-Atyz, Abdel-Haleem
%S 2016 3rd International Conference on Computer and Information Sciences (ICCOINS)
%D 2016
%8 aug
%F Abubakar:2016:ICCOINS
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/ICCOINS.2016.7783234
%U http://dx.doi.org/doi:10.1109/ICCOINS.2016.7783234
%P 316-321

%0 Journal Article
%T Reversible circuit synthesis by genetic programming using dynamic gate libraries
%A Abubakar, Mustapha Yusuf
%A Jung, Low Tang
%A Zakaria, Nordin
%A Younes, Ahmed
%A Abdel-Aty, Abdel-Haleem
%J Quantum Information Processing
%D 2017
%V 16
%N 6
%F journals/qip/AbubakarJZYA17
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s11128-017-1609-8
%U http://dx.doi.org/doi:10.1007/s11128-017-1609-8
%P 160

%0 Conference Proceedings
%T Synthesis of Reversible Logic Using Enhanced Genetic Programming Approach
%A Abubakar, Mustapha Yusuf
%A Tang Jung, Low
%S 2018 4th International Conference on Computer and Information Sciences (ICCOINS)
%D 2018
%8 aug
%F Abubakar:2018:ICCOINS
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/ICCOINS.2018.8510602
%U http://dx.doi.org/doi:10.1109/ICCOINS.2018.8510602

%0 Conference Proceedings
%T Action Scheduling Optimization using Cartesian Genetic Programming
%A Abud Kappel, Marco Andre
%S 2019 8th Brazilian Conference on Intelligent Systems (BRACIS)
%D 2019
%8 oct
%F Abud-Kappel:2019:BRACIS
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/BRACIS.2019.00059
%U http://dx.doi.org/doi:10.1109/BRACIS.2019.00059
%P 293-298

%0 Conference Proceedings
%T A Genetic Algorithm for Solving the P-Median Problem
%A Abu Dalhoum, Abdel Latif
%A Al Zoubi, Moh’d
%A de la Cruz, Marina
%A Ortega, Alfonso
%A Alfonseca, Manuel
%Y Teixeira, J. Manuel Feliz
%Y E.Carvalho Brito, A.
%S European Simulation and Modeling Conference ESM’2005
%D 2005
%8 oct 24 26
%I http://www.eurosis.org
%C Porto, Portugal
%@ 90-77381-22-8
%F AbuDalhoum:2005:ESM
%X 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.
%K genetic algorithms, genetic programming, grammatical evolution, Christiansen grammar, location allocation, p-median model, grammar evolution
%U http://arantxa.ii.uam.es/~alfonsec/docs/confint/pmedian.pdf
%P 141-145

%0 Report
%T Intensional Encapsulations of Database Subsets by Genetic Programming
%A Acar, Aybar C.
%A Motro, Amihai
%D 2005
%8 feb
%N ISE-TR-05-01
%I Information and Software Engineering Department, The Volgenau School of Information Technology and Engineering, George Mason University
%F AcarM05tr
%X 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.
%K genetic algorithms, genetic programming
%U http://ise.gmu.edu/techrep/2005/05_01.pdf

%0 Conference Proceedings
%T Intensional Encapsulations of Database Subsets via Genetic Programming
%A Acar, Aybar C.
%A Motro, Amihai
%Y Andersen, Kim Viborg
%Y Debenham, John K.
%Y Wagner, Roland
%S Database and Expert Systems Applications, 16th International Conference, DEXA 2005, Proceedings
%S Lecture Notes in Computer Science
%D 2005
%8 aug 22 26
%V 3588
%I Springer
%C Copenhagen, Denmark
%@ 3-540-28566-0
%F conf/dexa/AcarM05
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/11546924_36
%U http://dx.doi.org/doi:10.1007/11546924_36
%P 365-374

%0 Thesis
%T Query Consolidation: Interpreting Queries Sent to Independent Heterogenous Databases
%A Acar, Aybar C.
%D 2008
%8 23 jul
%C Fairfax, VA, USA
%C The Volgenau School of Information Technology and Engineering, George Mason University
%F Acar:thesis
%X 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.
%K genetic algorithms, genetic programming, Databases, Information Integration, Query Processing, Machine Learning
%9 Ph.D. thesis
%U http://hdl.handle.net/1920/3223

%0 Journal Article
%T Automatic design of specialized algorithms for the binary knapsack problem
%A Acevedo, Nicolas
%A Rey, Carlos
%A Contreras-Bolton, Carlos
%A Parada, Victor
%J Expert Systems with Applications
%D 2020
%V 141
%@ 0957-4174
%F ACEVEDO:2020:ESA
%X 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
%K genetic algorithms, genetic programming, Automatic generation of algorithms, Binary knapsack problem, Hyperheuristic, Generative design of algorithms
%9 journal article
%R doi:10.1016/j.eswa.2019.112908
%U http://www.sciencedirect.com/science/article/pii/S0957417419306268
%U http://dx.doi.org/doi:10.1016/j.eswa.2019.112908
%P 112908

%0 Journal Article
%T A novel fitness function in genetic programming to handle unbalanced emotion recognition data
%A Acharya, Divya
%A Goel, Shivani
%A Asthana, Rishi
%A Bhardwaj, Arpit
%J Pattern Recognition Letters
%D 2020
%V 133
%@ 0167-8655
%F ACHARYA:2020:PRL
%X 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
%K genetic algorithms, genetic programming, Emotion recognition, Fitness function, EEG, Fast Fourier transformation
%9 journal article
%R doi:10.1016/j.patrec.2020.03.005
%U http://www.sciencedirect.com/science/article/pii/S0167865520300830
%U http://dx.doi.org/doi:10.1016/j.patrec.2020.03.005
%P 272-279

%0 Journal Article
%T Emotion recognition using fourier transform and genetic programming
%A Acharya, Divya
%A Billimoria, Anosh
%A Srivastava, Neishka
%A Goel, Shivani
%A Bhardwaj, Arpit
%J Applied Acoustics
%D 2020
%V 164
%@ 0003-682X
%F ACHARYA:2020:AA
%X In cognitive science, the real-time recognition of human’s 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 recognizing these emotions
%K genetic algorithms, genetic programming, Electroencephalogram, Fast Fourier Transform, Emotion recognition, Movie clips
%9 journal article
%R doi:10.1016/j.apacoust.2020.107260
%U http://www.sciencedirect.com/science/article/pii/S0003682X19306954
%U http://dx.doi.org/doi:10.1016/j.apacoust.2020.107260
%P 107260

%0 Conference Proceedings
%T Evolving patches for software repair
%A Ackling, Thomas
%A Alexander, Bradley
%A Grunert, Ian
%Y Krasnogor, Natalio
%Y Lanzi, Pier Luca
%Y Engelbrecht, Andries
%Y Pelta, David
%Y Gershenson, Carlos
%Y Squillero, Giovanni
%Y Freitas, Alex
%Y Ritchie, Marylyn
%Y Preuss, Mike
%Y Gagne, Christian
%Y Ong, Yew Soon
%Y Raidl, Guenther
%Y Gallager, Marcus
%Y Lozano, Jose
%Y Coello-Coello, Carlos
%Y Silva, Dario Landa
%Y Hansen, Nikolaus
%Y Meyer-Nieberg, Silja
%Y Smith, Jim
%Y Eiben, Gus
%Y Bernado-Mansilla, Ester
%Y Browne, Will
%Y Spector, Lee
%Y Yu, Tina
%Y Clune, Jeff
%Y Hornby, Greg
%Y Wong, Man-Leung
%Y Collet, Pierre
%Y Gustafson, Steve
%Y Watson, Jean-Paul
%Y Sipper, Moshe
%Y Poulding, Simon
%Y Ochoa, Gabriela
%Y Schoenauer, Marc
%Y Witt, Carsten
%Y Auger, Anne
%S GECCO ’11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%D 2011
%8 December 16 jul
%I ACM
%C Dublin, Ireland
%F Ackling:2011:GECCO
%X 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.
%K genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Python
%R doi:10.1145/2001576.2001768
%U http://dx.doi.org/doi:10.1145/2001576.2001768
%P 1427-1434

%0 Journal Article
%T Learning to Assemble Classifiers via Genetic Programming
%A Acosta-Mendoza, Niusvel
%A Morales-Reyes, Alicia
%A Escalante, Hugo Jair
%A Alonso, Andres Gago
%J IJPRAI
%D 2014
%V 28
%N 7
%F journals/ijprai/Acosta-MendozaMEA14
%K genetic algorithms, genetic programming
%9 journal article
%U http://dx.doi.org/10.1142/S0218001414600052

%0 Book Section
%T Computers from Plants We Never Made: Speculations
%A Adamatzky, Andrew
%A Harding, Simon
%A Erokhin, Victor
%A Mayne, Richard
%A Gizzie, Nina
%A Baluska, Frantisek
%A Mancuso, Stefano
%A Sirakoulis, Georgios Ch.
%E Stepney, Susan
%E Adamatzky, Andrew
%B Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday
%S Emergence, Complexity and Computation
%D 2017
%V 28
%I Springer
%F Adamatzky:2017:miller
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-67997-6_17
%U http://dx.doi.org/doi:10.1007/978-3-319-67997-6_17
%P 357-387

%0 Conference Proceedings
%T Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks
%A Adamopoulos, Adam V.
%A Georgopoulos, Efstratios F.
%A Likothanassis, Spiridon D.
%A Anninos, Photios A.
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 2
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F adamopoulos:1999:FMEPUGONN
%K real world applications
%U http://gpbib.cs.ucl.ac.uk/gecco1999/RW-767.pdf
%P 1457-1462

%0 Book Section
%T Creation of Simple, Deadline, and Priority Scheduling Algorithms using Genetic Programming
%A Adams, Thomas P.
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%D 2002
%8 jun
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F adams:2002:CSDPSAGP
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2002/Adams.pdf
%P 1-10

%0 Conference Proceedings
%T Computational Scientific Discovery and Cognitive Science Theories
%A Addis, Mark
%A Sozou, Peter D.
%A Lane, Peter C.
%A Gobet, Fernand
%Y Mueller, Vincent C.
%S Computing and Philosophy: Selected Papers from IACAP 2014
%D 2016
%I Springer
%F Addis:2014:IACAP
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-23291-1_6
%U http://eprints.lse.ac.uk/66168/
%U http://dx.doi.org/doi:10.1007/978-3-319-23291-1_6
%P 83-97

%0 Conference Proceedings
%T Regression genetic programming for estimating trend end in foreign exchange market
%A Adegboye, Adesola
%A Kampouridis, Michael
%A Johnson, Colin G.
%S 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2017
%8 nov
%F Adegboye:2017:ieeeSSCI)
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/SSCI.2017.8280833
%U http://dx.doi.org/doi:10.1109/SSCI.2017.8280833

%0 Conference Proceedings
%T Shear Force Analysis and Modeling for Discharge Estimation Using Numerical and GP for Compound Channels
%A Adhikari, Alok
%A Adhikari, Nibedita
%A Patra, K. C.
%S Soft Computing in Data Analytics
%D 2019
%I Springer
%F adhikari:2019:SCDA
%K genetic algorithms, genetic programming
%R doi:10.1007/978-981-13-0514-6_32
%U http://link.springer.com/chapter/10.1007/978-981-13-0514-6_32
%U http://dx.doi.org/doi:10.1007/978-981-13-0514-6_32

%0 Journal Article
%T Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels
%A Adhikari, Alok
%A Adhikari, N.
%A Patra, K. C.
%J Journal of The Institution of Engineers (India): Series A
%D 2019
%8 sep
%V 100
%N 3
%@ 2250-2149
%F adhikari:JIEIa
%X 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.
%K genetic algorithms, genetic programming, FIS, ANFIS, GP
%9 journal article
%R doi:10.1007/s40030-019-00367-x
%U http://link.springer.com/article/10.1007/s40030-019-00367-x
%U http://dx.doi.org/doi:10.1007/s40030-019-00367-x
%P 395-405

%0 Conference Proceedings
%T A cellular-programming approach to pattern classification
%A Adorni, Giovanni
%A Bergenti, Federico
%A Cagnoni, Stefano
%Y Banzhaf, Wolfgang
%Y Poli, Riccardo
%Y Schoenauer, Marc
%Y Fogarty, Terence C.
%S Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%D 1998
%8 14 15 apr
%V 1391
%I Springer-Verlag
%C Paris
%@ 3-540-64360-5
%F adorni:1998:cpapc
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0055934
%U http://dx.doi.org/doi:10.1007/BFb0055934
%P 142-150

%0 Conference Proceedings
%T Genetic Programming of a Goal-Keeper Control Strategy for the RoboCup Middle Size Competition
%A Adorni, Giovanni
%A Cagnoni, Stefano
%A Mordonini, Monica
%Y Poli, Riccardo
%Y Nordin, Peter
%Y Langdon, William B.
%Y Fogarty, Terence C.
%S Genetic Programming, Proceedings of EuroGP’99
%S LNCS
%D 1999
%8 26 27 may
%V 1598
%I Springer-Verlag
%C Goteborg, Sweden
%@ 3-540-65899-8
%F adorni:1999:GPgkcsrcmsc
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/3-540-48885-5_9
%U http://dx.doi.org/doi:10.1007/3-540-48885-5_9
%P 109-119

%0 Conference Proceedings
%T Efficient low-level vision program design using Sub-machine-code Genetic Programming
%A Adorni, Giovanni
%A Cagnoni, Stefano
%A Mordonini, Monica
%Y Gori, Marco
%S AIIA 2002, Workshop sulla Percezione e Visione nelle Macchine
%D 2002
%8 October 13 sep
%C Siena, Italy
%G en
%F oai:CiteSeerPSU:539182
%X 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.
%K genetic algorithms, genetic programming
%U http://www-dii.ing.unisi.it/aiia2002/paper/PERCEVISIO/adorni-aiia02.pdf

%0 Conference Proceedings
%T Design of Explicitly or Implicitly Parallel Low-resolution Character Recognition Algorithms by Means of Genetic Programming
%A Adorni, Giovanni
%A Cagnoni, Stefano
%Y Roy, Rajkumar
%Y Köppen, Mario
%Y Ovaska, Seppo
%Y Furuhashi, Takeshi
%Y Hoffmann, Frank
%S Soft Computing and Industry Recent Applications
%D 2001
%8 October
%I Springer-Verlag
%@ 1-85233-539-4
%F adorni:2001:wsc6
%O Published 2002
%K genetic algorithms, genetic programming
%U https://link.springer.com/book/10.1007/978-1-4471-0123-9
%P 387-398

%0 Generic
%T Automated conjecturing of Frobenius numbers via grammatical evolution
%A Adzaga, Nikola
%D 2014
%8 feb  17
%F oai:arXiv.org:1410.0532
%O Comment: 8 pages, 2 tables; added a clear introduction, otherwise reduced text significantly
%X 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.
%K genetic algorithms, genetic programming, grammatical evolution, mathematics, number theory, mathematics, combinatorics
%U http://arxiv.org/abs/1410.0532

%0 Journal Article
%T Automated Conjecturing of Frobenius Numbers via Grammatical Evolution
%A Adzaga, Nikola
%J Experimental Mathematics
%D 2017
%V 26
%N 2
%F Adzaga:2017:EM
%X 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.
%K genetic algorithms, genetic programming, , grammatical evolution, automated conjecturing, Frobenius problem
%9 journal article
%R doi:10.1080/10586458.2016.1175393
%U http://dx.doi.org/doi:10.1080/10586458.2016.1175393
%P 247-252

%0 Conference Proceedings
%T Lexicase selection in learning classifier systems
%A Aenugu, Sneha
%A Spector, Lee
%Y Lopez-Ibanez, Manuel
%Y Stuetzle, Thomas
%Y Auger, Anne
%Y Posik, Petr
%Y Peprez Caceres, Leslie
%Y Sutton, Andrew M.
%Y Veerapen, Nadarajen
%Y Solnon, Christine
%Y Engelbrecht, Andries
%Y Doncieux, Stephane
%Y Risi, Sebastian
%Y Machado, Penousal
%Y Volz, Vanessa
%Y Blum, Christian
%Y Chicano, Francisco
%Y Xue, Bing
%Y Mouret, Jean-Baptiste
%Y Liefooghe, Arnaud
%Y Fieldsend, Jonathan
%Y Lozano, Jose Antonio
%Y Arnold, Dirk
%Y Ochoa, Gabriela
%Y Yu, Tian-Li
%Y Hoos, Holger
%Y Jin, Yaochu
%Y Hu, Ting
%Y Nicolau, Miguel
%Y Purshouse, Robin
%Y Baeck, Thomas
%Y Petke, Justyna
%Y Antoniol, Giuliano
%Y Lengler, Johannes
%Y Lehre, Per Kristian
%S GECCO ’19: Proceedings of the Genetic and Evolutionary Computation Conference
%D 2019
%8 13 17 jul
%I ACM
%C Prague, Czech Republic
%F Aenugu:2019:GECCO
%X 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.
%K genetic algorithms, LCS, Learning Classifier Systems, Parent Selection, Lexicase Selection
%R doi:10.1145/3321707.3321828
%U http://dx.doi.org/doi:10.1145/3321707.3321828
%P 356-364

%0 Conference Proceedings
%T Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms
%A Affenzeller, M.
%A Wagner, S.
%Y Ribeiro, Bernardete
%Y Albrecht, Rudolf F.
%Y Dobnikar, Andrej
%Y Pearson, David W.
%Y Steele, Nigel C.
%S Proceedings of the seventh International Conference Adaptive and Natural Computing Algorithms
%D 2005
%8 21 23 mar
%I Springer
%C Coimbra, Portugal
%F Affenzeller:2005:ICANNGA
%X 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
%K genetic algorithms, genetic programming, OS-GP
%R doi:10.1007/3-211-27389-1_52
%U https://link.springer.com/chapter/10.1007/3-211-27389-1_52
%U http://dx.doi.org/doi:10.1007/3-211-27389-1_52
%P 218-221

%0 Book
%T Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
%A Affenzeller, Michael
%A Winkler, Stephan
%A Wagner, Stefan
%A Beham, Andreas
%S Numerical Insights
%D 2009
%I CRC Press
%C Singapore
%@ 1-58488-629-3
%F Affenzeller:GAGP
%X 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.
%K genetic algorithms, genetic programming
%U http://gagp2009.heuristiclab.com/

%0 Journal Article
%T Effective allele preservation by offspring selection: an empirical study for the TSP
%A Affenzeller, Michael
%A Wagner, Stefan
%A Winkler, Stephan M.
%J Int. J. of Simulation and Process Modelling
%D 2010
%8 apr  11
%V 6
%N 1
%I Inderscience Publishers
%@ 1740-2131
%G eng
%F Affenzeller:2010:IJSPM
%X 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.
%K genetic algorithms, genetic programming, soft computing, evolutionary computation, GAs selection, self adaptation, population genetics, evolution strategies, modelling, allele preservation, offspring selection, travelling salesman problem
%9 journal article
%R doi:10.1504/IJSPM.2010.032655
%U http://www.inderscience.com/link.php?id=32655
%U http://dx.doi.org/doi:10.1504/IJSPM.2010.032655
%P 29-39

%0 Conference Proceedings
%T New Genetic Programming Hypothesis Search Strategies for Improving the Interpretability in Medical Data Mining Applications
%A Affenzeller, M.
%A Fischer, C.
%A Kronberger, G. K.
%A Winkler, S. M.
%A Wagner, S.
%S Proccedings of 23rd IEEE European Modeling & Simulation Symposium EMSS 2011
%D 2011
%8 sep
%C Roma, Italy
%F 2453
%K genetic algorithms, genetic programming
%U http://research.fh-ooe.at/files/publications/2453_EMSS_2011_Affenzeller.pdf

%0 Conference Proceedings
%T Enhanced Confidence Interpretations of GP Based Ensemble Modeling Results
%A Affenzeller, Michael
%A Winkler, Stephan M.
%A Forstenlechner, Stefan
%A Kronberger, Gabriel
%A Kommenda, Michael
%A Wagner, Stefan
%A Stekel, Herbert
%Y Jimenez, Emilio
%Y Sokolov, Boris
%S The 24th European Modeling and Simulation Symposium, EMSS 2012
%D 2012
%8 sep 19 21
%C Vienna, Austria
%F Affenzeller:2012:EMSS
%X 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
%K genetic algorithms, genetic programming, data mining, ensemble modelling, medical data analysis
%U http://research.fh-ooe.at/en/publication/2935
%P 340-345

%0 Conference Proceedings
%T Improving the Accuracy of Cancer Prediction by Ensemble Confidence Evaluation
%A Affenzeller, Michael
%A Winkler, Stephan M.
%A Stekel, Herbert
%A Forstenlechner, Stefan
%A Wagner, Stefan
%Y Moreno-Diaz, Roberto
%Y Pichler, Franz
%Y Quesada-Arencibia, Alexis
%S Computer Aided Systems Theory - EUROCAST 2013
%S Lecture Notes in Computer Science
%D 2013
%8 feb 10 15
%V 8111
%I Springer
%C Las Palmas de Gran Canaria, Spain
%G English
%F Affenzeller:2013:EUROCAST
%O Revised Selected Papers, Part I
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-53856-8_40
%U http://dx.doi.org/10.1007/978-3-642-53856-8_40
%U http://dx.doi.org/doi:10.1007/978-3-642-53856-8_40
%P 316-323

%0 Book Section
%T Gaining Deeper Insights in Symbolic Regression
%A Affenzeller, Michael
%A Winkler, Stephan M.
%A Kronberger, Gabriel
%A Kommenda, Michael
%A Burlacu, Bogdan
%A Wagner, Stefan
%E Riolo, Rick
%E Moore, Jason H.
%E Kotanchek, Mark
%B Genetic Programming Theory and Practice XI
%S Genetic and Evolutionary Computation
%D 2013
%8 September 11 may
%I Springer
%C Ann Arbor, USA
%F Affenzeller:2013:GPTP
%X 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.
%K genetic algorithms, genetic programming, Symbolic regression, Algorithm analysis, Population diversity Building block analysis, Genealogy, Variable networks
%R doi:10.1007/978-1-4939-0375-7_10
%U http://dx.doi.org/doi:10.1007/978-1-4939-0375-7_10
%P 175-190

%0 Conference Proceedings
%T Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals
%A Affenzeller, Michael
%A Burlacu, Bogdan
%A Winkler, Stephan M.
%A Kommenda, Michael
%A Kronberger, Gabriel K.
%A Wagner, Stefan
%Y Moreno-Diaz, Roberto
%Y Pichler, Franz
%Y Quesada-Arencibia, Alexis
%S 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
%S Lecture Notes in Computer Science
%D 2017
%8 feb
%V 10671
%I Springer
%C Las Palmas de Gran Canaria, Spain
%F 6339
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-74718-7_51
%U https://link.springer.com/chapter/10.1007/978-3-319-74718-7_51
%U http://dx.doi.org/doi:10.1007/978-3-319-74718-7_51
%P 424-431

%0 Conference Proceedings
%T Dynamic Observation of Genotypic and Phenotypic Diversity for Different Symbolic Regression GP Variants
%A Affenzeller, Michael
%A Winkler, Stephan M.
%A Burlacu, Bogdan
%A Kronberger, Gabriel
%A Kommenda, Michael
%A Wagner, Stefan
%S Proceedings of the Genetic and Evolutionary Computation Conference Companion
%S GECCO ’17
%D 2017
%8 15 19 jul
%I ACM
%C Berlin, Germany
%F Affenzeller:2017:GECCO
%X 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.
%K genetic algorithms, genetic programming, genetic and phenotypic diversity, offspring selection, population dynamics, symbolic regression
%R doi:10.1145/3067695.3082530
%U http://doi.acm.org/10.1145/3067695.3082530
%U http://dx.doi.org/doi:10.1145/3067695.3082530
%P 1553-1558

%0 Journal Article
%T The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products
%A Afshar, M. H.
%A Yilmaz, M. T.
%J Remote Sensing of Environment
%D 2017
%V 196
%@ 0034-4257
%F Afshar:2017:RSE
%X 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.
%K genetic algorithms, genetic programming, Soil moisture, Rescaling, Linear, Nonlinear, Remote sensing
%9 journal article
%R doi:10.1016/j.rse.2017.05.017
%U http://www.sciencedirect.com/science/article/pii/S003442571730216X
%U http://dx.doi.org/doi:10.1016/j.rse.2017.05.017
%P 224-237

%0 Conference Proceedings
%T A Turing Test for Genetic Improvement
%A Afzal, Afsoon
%A Lacomis, Jeremy
%A Le Goues, Claire
%A Timperley, Christopher Steven
%Y Petke, Justyna
%Y Stolee, Kathryn
%Y Langdon, William B.
%Y Weimer, Westley
%S GI-2018, ICSE workshops proceedings
%D 2018
%8 February
%I ACM
%C Gothenburg, Sweden
%F Timperley:2018:GI
%X 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.
%K genetic algorithms, genetic programming, genetic improvement
%R doi:10.1145/3194810.3194817
%U http://dx.doi.org/10.1145/3194810.3194817
%U http://dx.doi.org/doi:10.1145/3194810.3194817
%P 17-18

%0 Conference Proceedings
%T A Systematic Mapping Study on Non-Functional Search-based Software Testing
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%S Proceedings of the 20th International Conference on Software Engineering and Knowledge Engineering (SEKE ’08)
%D 2008
%8 jul 1 3
%I Knowledge Systems Institute Graduate School
%C San Francisco, CA, USA
%@ 1-891706-22-5
%F AfzalTF08
%X 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.
%K genetic algorithms, genetic programming
%U http://www.torkar.se/resources/A-systematic-mapping-study-on-non-functional-search-based-software-testing.pdf
%P 488-493

%0 Conference Proceedings
%T Suitability of Genetic Programming for Software Reliability Growth Modeling
%A Afzal, Wasif
%A Torkar, Richard
%S The 2008 International Symposium on Computer Science and its Applications (CSA’08)
%D 2008
%8 13 15 oct
%I IEEE Computer Society
%C Hobart, ACT
%F Afzal08e
%X 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.
%K genetic algorithms, genetic programming, software reliability data points, software reliability growth modeling, SBSE
%R doi:10.1109/CSA.2008.13
%U http://dx.doi.org/doi:10.1109/CSA.2008.13
%P 114-117

%0 Conference Proceedings
%T A comparative evaluation of using genetic programming for predicting fault count data
%A Afzal, Wasif
%A Torkar, Richard
%S Proceedings of the Third International Conference on Software Engineering Advances (ICSEA’08)
%D 2008
%8 26 31
%C Sliema, Malta
%F Afzal08d
%X 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.
%K genetic algorithms, genetic programming, prediction, software reliability growth modeling, SBSE
%R doi:10.1109/ICSEA.2008.9
%U http://dx.doi.org/doi:10.1109/ICSEA.2008.9
%P 407-414

%0 Conference Proceedings
%T Prediction of fault count data using genetic programming
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%S Proceedings of the 12th IEEE International Multitopic Conference (INMIC’08)
%D 2008
%8 23 24 dec
%I IEEE
%C Karachi, Pakistan
%F Afzal08b
%X 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.
%K genetic algorithms, genetic programming, SBSE, fault count data, prediction
%R doi:10.1109/INMIC.2008.4777762
%U http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf
%U http://dx.doi.org/doi:10.1109/INMIC.2008.4777762
%P 349-356

%0 Conference Proceedings
%T Search-Based Prediction of Fault Count Data
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%Y Di Penta, Massimiliano
%Y Poulding, Simon
%S Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009
%D 2009
%8 13 15 may
%I IEEE
%C Windsor, UK
%F Afzal:2009:SSBSE
%X 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.
%K genetic algorithms, genetic programming, SBSE, search-based prediction, software fault count data, software reliability growth model, symbolic regression, regression analysis, software fault tolerance
%R doi:10.1109/SSBSE.2009.17
%U http://dx.doi.org/doi:10.1109/SSBSE.2009.17
%P 35-38

%0 Journal Article
%T A systematic review of search-based testing for non-functional system properties
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%J Information and Software Technology
%D 2009
%8 jun
%V 51
%N 6
%@ 0950-5849
%F Afzal2009
%X 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.
%K genetic algorithms, genetic programming, Systematic review, Non-functional system properties, Search-based software testing
%9 journal article
%R doi:10.1016/j.infsof.2008.12.005
%U http://drfeldt.googlepages.com/afzal_submitted0805ist_sysrev_nfr_sb.pdf
%U http://dx.doi.org/doi:10.1016/j.infsof.2008.12.005
%P 957-976

%0 Thesis
%T Search-Based Approaches to Software Fault Prediction and Software Testing
%A Afzal, Wasif
%D 2009
%C Sweden
%C School of Engineering, Dept. of Systems and Software Engineering, Blekinge Institute of Technology
%G eng
%F Afzal:Licentiate
%X 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.
%K genetic algorithms, genetic programming, SBSE, Software Engineering, Computer Science, Artificial Intelligence
%9 Licentiate Dissertation
%9 Masters thesis
%U http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3/$file/Afzal_lic.pdf

%0 Book Section
%T Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%A Gorschek, Tony
%E Chis, Monica
%B Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques
%D 2010
%8 jun
%I IGI Global
%F Afzal:2010:ECoaSE
%X 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.
%K genetic algorithms, genetic programming, SBSE
%R doi:10.4018/978-1-61520-809-8.ch006
%U http://dx.doi.org/doi:10.4018/978-1-61520-809-8.ch006
%P 94-126

%0 Conference Proceedings
%T Search-based Prediction of Fault-slip-through in Large Software Projects
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%A Wikstrand, Greger
%S Second International Symposium on Search Based Software Engineering (SSBSE 2010)
%D 2010
%8 July 9 sep
%C Benevento, Italy
%F Afzal:2010:SSBSE
%X 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.
%K 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
%R doi:10.1109/SSBSE.2010.19
%U http://dx.doi.org/doi:10.1109/SSBSE.2010.19
%P 79-88

%0 Conference Proceedings
%T Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness
%A Afzal, Wasif
%S 17th Asia Pacific Software Engineering Conference (APSEC 2010)
%D 2010
%8 nov 30 dec 3
%F Afzal:2010:APSEC
%X 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.
%K 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
%R doi:10.1109/APSEC.2010.54
%U http://dx.doi.org/doi:10.1109/APSEC.2010.54
%P 414-422

%0 Journal Article
%T On the application of genetic programming for software engineering predictive modeling: A systematic review
%A Afzal, Wasif
%A Torkar, Richard
%J Expert Systems with Applications
%D 2011
%V 38
%N 9
%@ 0957-4174
%F Afzal201111984
%X 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.
%K genetic algorithms, genetic programming, Systematic review, Symbolic regression, Modelling
%9 journal article
%R doi:10.1016/j.eswa.2011.03.041
%U http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c
%U http://dx.doi.org/doi:10.1016/j.eswa.2011.03.041
%P 11984-11997

%0 Thesis
%T Search-Based Prediction of Software Quality: Evaluations And Comparisons
%A Afzal, Wasif
%D 2011
%8 May
%C Sweden
%C School of Computing, Blekinge Institute of Technology
%F Afzal:thesis
%X 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.
%K genetic algorithms, genetic programming, SBSE
%9 Ph.D. thesis
%U http://www.bth.se/fou/forskinfo.nsf/0/dd0dcce8cc126a52c125784500410306/$file/Dis%20Wasif%20Afzal%20thesis.pdf

%0 Journal Article
%T Prediction of faults-slip-through in large software projects: an empirical evaluation
%A Afzal, Wasif
%A Torkar, Richard
%A Feldt, Robert
%A Gorschek, Tony
%J Software Quality Journal
%D 2014
%8 mar
%V 22
%N 1
%I Springer US
%@ 0963-9314
%G English
%F Afzal:2013:SQJ
%X 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.
%K genetic algorithms, genetic programming, SBSE, Prediction, Empirical, Faults-slip-through, Search-based
%9 journal article
%R doi:10.1007/s11219-013-9205-3
%U http://www.bth.se/fou/forskinfo.nsf/all/3d40224f7cbf862dc1257b7800251e66?OpenDocument
%U http://dx.doi.org/doi:10.1007/s11219-013-9205-3
%P 51-86

%0 Book Section
%T Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction
%A Afzal, Wasif
%A Torkar, Richard
%E Pedrycz, Witold
%E Succi, Giancarlo
%E Sillitti, Alberto
%B Computational Intelligence and Quantitative Software Engineering
%S Studies in Computational Intelligence
%D 2016
%V 617
%I Springer
%F Afzal2016
%X 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.
%K genetic algorithms, genetic programming, SBSE, Feature subset selection, Fault prediction, Empirical
%R doi:10.1007/978-3-319-25964-2_3
%U http://dx.doi.org/doi:10.1007/978-3-319-25964-2_3
%P 33-58

%0 Conference Proceedings
%T A Genetic Programming Approach for Constructing Foreground and Background Saliency Features for Salient Object Detection
%A Afzali, Shima
%A Al-Sahaf, Harith
%A Xue, Bing
%A Hollitt, Christopher
%A Zhang, Mengjie
%Y Mitrovic, Tanja
%Y Xue, Bing
%Y Li, Xiaodong
%S Australasian Joint Conference on Artificial Intelligence
%S LNCS
%D 2018
%8 dec 11 14
%V 11320
%I Springer
%C Wellington, New Zealand
%F afzali:2018:AJCAI
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-030-03991-2_21
%U http://link.springer.com/chapter/10.1007/978-3-030-03991-2_21
%U http://dx.doi.org/doi:10.1007/978-3-030-03991-2_21

%0 Conference Proceedings
%T Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection
%A Afzali, Shima
%A Al-Sahaf, Harith
%A Xue, Bing
%A Hollitt, Christopher
%A Zhang, Mengjie
%Y Kaufmann, Paul
%Y Castillo, Pedro A.
%S 22nd International Conference, EvoApplications 2019
%S LNCS
%D 2019
%8 24 26 apr
%V 11454
%I Springer Verlag
%C Leipzig, Germany
%F Afzali:2019:evoapplications
%X 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.
%K genetic algorithms, genetic programming, Salient Object Detection, Feature combination, Feature selection
%R doi:10.1007/978-3-030-16692-2_21
%U http://dx.doi.org/doi:10.1007/978-3-030-16692-2_21
%P 308-324

%0 Conference Proceedings
%T Random Systems with Complete Connections
%A Agapie, Alexandru
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F agapie:1999:RSCC
%K genetic algorithms and classifier systems, poster papers
%U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-862.ps
%P 770

%0 Conference Proceedings
%T Learning Recursive Functions with Object Oriented Genetic Programming
%A Agapitos, Alexandros
%A Lucas, Simon M.
%Y Collet, Pierre
%Y Tomassini, Marco
%Y Ebner, Marc
%Y Gustafson, Steven
%Y Ekárt, Anikó
%S Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%D 2006
%8 October 12 apr
%V 3905
%I Springer
%C Budapest, Hungary
%@ 3-540-33143-3
%F eurogp06:AgapitosLucas
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/11729976_15
%U http://dx.doi.org/doi:10.1007/11729976_15
%P 166-177

%0 Conference Proceedings
%T Evolving Efficient Recursive Sorting Algorithms
%A Agapitos, Alexandros
%A Lucas, Simon M.
%Y Yen, Gary G.
%Y Wang, Lipo
%Y Bonissone, Piero
%Y Lucas, Simon M.
%S Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%8 June 21 jul
%I IEEE Press
%C Vancouver
%@ 0-7803-9487-9
%F Agapitos:2006:CEC
%X 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.
%K 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
%R doi:10.1109/CEC.2006.1688643
%U http://privatewww.essex.ac.uk/~aagapi/papers/AgapitosLucasEvolvingSort.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2006.1688643
%P 9227-9234

%0 Conference Proceedings
%T Evolving a Statistics Class Using Object Oriented Evolutionary Programming
%A Agapitos, Alexandros
%A Lucas, Simon M.
%Y Ebner, Marc
%Y O’Neill, Michael
%Y Ekárt, Anikó
%Y Vanneschi, Leonardo
%Y Esparcia-Alcázar, Anna Isabel
%S Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%D 2007
%8 November 13 apr
%V 4445
%I Springer
%C Valencia, Spain
%@ 3-540-71602-5
%F eurogp07:agapitos1
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-71605-1_27
%U http://dx.doi.org/doi:10.1007/978-3-540-71605-1_27
%P 291-300

%0 Conference Proceedings
%T Evolving Modular Recursive Sorting Algorithms
%A Agapitos, Alexandros
%A Lucas, Simon M.
%Y Ebner, Marc
%Y O’Neill, Michael
%Y Ekárt, Anikó
%Y Vanneschi, Leonardo
%Y Esparcia-Alcázar, Anna Isabel
%S Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%D 2007
%8 November 13 apr
%V 4445
%I Springer
%C Valencia, Spain
%@ 3-540-71602-5
%F eurogp07:agapitos2
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-71605-1_28
%U http://dx.doi.org/doi:10.1007/978-3-540-71605-1_28
%P 301-310

%0 Conference Proceedings
%T Evolving controllers for simulated car racing using object oriented genetic programming
%A Agapitos, Alexandros
%A Togelius, Julian
%A Lucas, Simon Mark
%Y Thierens, Dirk
%Y Beyer, Hans-Georg
%Y Bongard, Josh
%Y Branke, Jurgen
%Y Clark, John Andrew
%Y Cliff, Dave
%Y Congdon, Clare Bates
%Y Deb, Kalyanmoy
%Y Doerr, Benjamin
%Y Kovacs, Tim
%Y Kumar, Sanjeev
%Y Miller, Julian F.
%Y Moore, Jason
%Y Neumann, Frank
%Y Pelikan, Martin
%Y Poli, Riccardo
%Y Sastry, Kumara
%Y Stanley, Kenneth Owen
%Y Stutzle, Thomas
%Y Watson, Richard A.
%Y Wegener, Ingo
%S GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%D 2007
%8 July 11 jul
%V 2
%I ACM Press
%C London
%F 1277271
%X 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.
%K genetic algorithms, genetic programming, evolutionary computer games, evolutionary robotics, homologous uniform crossover, neural networks, object oriented, subtree macro-mutation
%R doi:10.1145/1276958.1277271
%U http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1543.pdf
%U http://dx.doi.org/doi:10.1145/1276958.1277271
%P 1543-1550

%0 Conference Proceedings
%T Multiobjective Techniques for the Use of State in Genetic Programming Applied to Simulated Car Racing
%A Agapitos, Alexandros
%A Togelius, Julian
%A Lucas, Simon M.
%Y Srinivasan, Dipti
%Y Wang, Lipo
%S 2007 IEEE Congress on Evolutionary Computation
%D 2007
%8 25 28 sep
%I IEEE Press
%C Singapore
%@ 1-4244-1340-0
%F Agapitos:2007:cec
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2007.4424659
%U 1977.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2007.4424659
%P 1562-1569

%0 Conference Proceedings
%T Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing
%A Agapitos, Alexandros
%A Dyson, Matthew
%A Lucas, Simon M.
%A Sepulveda, Francisco
%Y Keijzer, Maarten
%Y Antoniol, Giuliano
%Y Congdon, Clare Bates
%Y Deb, Kalyanmoy
%Y Doerr, Benjamin
%Y Hansen, Nikolaus
%Y Holmes, John H.
%Y Hornby, Gregory S.
%Y Howard, Daniel
%Y Kennedy, James
%Y Kumar, Sanjeev
%Y Lobo, Fernando G.
%Y Miller, Julian Francis
%Y Moore, Jason
%Y Neumann, Frank
%Y Pelikan, Martin
%Y Pollack, Jordan
%Y Sastry, Kumara
%Y Stanley, Kenneth
%Y Stoica, Adrian
%Y Talbi, El-Ghazali
%Y Wegener, Ingo
%S GECCO ’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%D 2008
%8 December 16 jul
%I ACM
%C Atlanta, GA, USA
%F Agapitos:2008:gecco
%X 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
%K genetic algorithms, genetic programming, Brain computer interface, classification on Raw signal, stateful representation, statistical signal primitives
%R doi:10.1145/1389095.1389326
%U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1155.pdf
%U http://dx.doi.org/doi:10.1145/1389095.1389326
%P 1155-1162

%0 Conference Proceedings
%T On the genetic programming of time-series predictors for supply chain management
%A Agapitos, Alexandros
%A Dyson, Matthew
%A Kovalchuk, Jenya
%A Lucas, Simon Mark
%Y Keijzer, Maarten
%Y Antoniol, Giuliano
%Y Congdon, Clare Bates
%Y Deb, Kalyanmoy
%Y Doerr, Benjamin
%Y Hansen, Nikolaus
%Y Holmes, John H.
%Y Hornby, Gregory S.
%Y Howard, Daniel
%Y Kennedy, James
%Y Kumar, Sanjeev
%Y Lobo, Fernando G.
%Y Miller, Julian Francis
%Y Moore, Jason
%Y Neumann, Frank
%Y Pelikan, Martin
%Y Pollack, Jordan
%Y Sastry, Kumara
%Y Stanley, Kenneth
%Y Stoica, Adrian
%Y Talbi, El-Ghazali
%Y Wegener, Ingo
%S GECCO ’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%D 2008
%8 December 16 jul
%I ACM
%C Atlanta, GA, USA
%F Agapitos2:2008:gecco
%K genetic algorithms, genetic programming, Iterated single-step prediction, prediction/forecasting, single-step prediction, statistical time-series Features
%R doi:10.1145/1389095.1389327
%U http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1163.pdf
%U http://dx.doi.org/doi:10.1145/1389095.1389327
%P 1163-1170

%0 Conference Proceedings
%T Generating Diverse Opponents with Multiobjective Evolution
%A Agapitos, Alexandros
%A Togelius, Julian
%A Lucas, Simon M.
%A Schmidhuber, Jurgen
%A Konstantinidis, Andreas
%S Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games
%D 2008
%8 dec 15 18
%I IEEE
%C Perth, Australia
%F Agapitos:2008:CIG
%X 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.
%K 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
%R doi:10.1109/CIG.2008.5035632
%U http://julian.togelius.com/Agapitos2008Generating.pdf
%U http://dx.doi.org/doi:10.1109/CIG.2008.5035632
%P 135-142

%0 Conference Proceedings
%T Evolutionary Learning of Technical Trading Rules without Data-mining Bias
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%Y Schaefer, Robert
%Y Cotta, Carlos
%Y Kolodziej, Joanna
%Y Rudolph, Guenter
%S PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%S Lecture Notes in Computer Science
%D 2010
%8 November 15 sep
%V 6238
%I Springer
%C Krakow, Poland
%F agapitos_etal:ppsn2010
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-15844-5_30
%U http://dx.doi.org/doi:10.1007/978-3-642-15844-5_30
%P 294-303

%0 Conference Proceedings
%T Evolutionary Prediction of Total Electron Content over Cyprus
%A Agapitos, Alexandros
%A Konstantinidis, Andreas
%A Haralambous, Haris
%A Papadopoulos, Harris
%Y Papadopoulos, Harris
%Y Andreou, Andreas
%Y Bramer, Max
%S 6th IFIP Advances in Information and Communication Technology AIAI 2010
%S IFIP Advances in Information and Communication Technology
%D 2010
%8 oct 6 7
%V 339
%I Springer
%C Larnaca, Cyprus
%F Agapitos:2010:AIAI
%X 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.
%K genetic algorithms, genetic programming, Evolutionary Algorithms, Global Positioning System, Total Electron Content
%R doi:10.1007/978-3-642-16239-8_50
%U http://dx.doi.org/doi:10.1007/978-3-642-16239-8_50
%P 387-394

%0 Conference Proceedings
%T Promoting the generalisation of genetically induced trading rules
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%Y Kapetanios, G.
%Y Linton, O.
%Y McAleer, M.
%Y Ruiz, E.
%S Proceedings of the 4th International Conference on Computational and Financial Econometrics CFE’10
%D 2010
%8 October 12 dec
%I ERCIM
%C Senate House, University of London, UK
%F agapitosetal:2010:cfe
%X 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.
%K genetic algorithms, genetic programming
%U http://www.cfe-csda.org/cfe10/LondonBoA.pdf
%P E678

%0 Conference Proceedings
%T Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%A Theodoridis, Theodoros
%Y Silva, Sara
%Y Foster, James A.
%Y Nicolau, Miguel
%Y Giacobini, Mario
%Y Machado, Penousal
%S Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%D 2011
%8 27 29 apr
%V 6621
%I Springer Verlag
%C Turin, Italy
%F agapitos:2011:EuroGP
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-20407-4_6
%U http://dx.doi.org/doi:10.1007/978-3-642-20407-4_6
%P 61-72

%0 Conference Proceedings
%T Stateful program representations for evolving technical trading rules
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%Y Krasnogor, Natalio
%Y Lanzi, Pier Luca
%Y Engelbrecht, Andries
%Y Pelta, David
%Y Gershenson, Carlos
%Y Squillero, Giovanni
%Y Freitas, Alex
%Y Ritchie, Marylyn
%Y Preuss, Mike
%Y Gagne, Christian
%Y Ong, Yew Soon
%Y Raidl, Guenther
%Y Gallager, Marcus
%Y Lozano, Jose
%Y Coello-Coello, Carlos
%Y Silva, Dario Landa
%Y Hansen, Nikolaus
%Y Meyer-Nieberg, Silja
%Y Smith, Jim
%Y Eiben, Gus
%Y Bernado-Mansilla, Ester
%Y Browne, Will
%Y Spector, Lee
%Y Yu, Tina
%Y Clune, Jeff
%Y Hornby, Greg
%Y Wong, Man-Leung
%Y Collet, Pierre
%Y Gustafson, Steve
%Y Watson, Jean-Paul
%Y Sipper, Moshe
%Y Poulding, Simon
%Y Ochoa, Gabriela
%Y Schoenauer, Marc
%Y Witt, Carsten
%Y Auger, Anne
%S GECCO ’11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%D 2011
%8 December 16 jul
%I ACM
%C Dublin, Ireland
%F Agapitos:2011:GECCOcomp
%X 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.
%K genetic algorithms, genetic programming: Poster
%R doi:10.1145/2001858.2001969
%U http://dx.doi.org/doi:10.1145/2001858.2001969
%P 199-200

%0 Conference Proceedings
%T Learning Environment Models in Car Racing Using Stateful Genetic Programming
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%A Theodoridis, Theodoros
%S Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games
%D 2011
%8 31 aug  3 sep
%I IEEE
%C Seoul, South Korea
%F Agapitos:2011:CIG
%X 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.
%K 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
%R doi:10.1109/CIG.2011.6032010
%U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf
%U http://dx.doi.org/doi:10.1109/CIG.2011.6032010
%P 219-226

%0 Book Section
%T An Evolutionary Algorithmic Investigation of US Corporate Payout Policy
%A Agapitos, Alexandros
%A Goyal, Abhinav
%A Muckley, Cal
%E Brabazon, Anthony
%E O’Neill, Michael
%E Maringer, Dietmar
%B Natural Computing in Computational Finance (Volume 4)
%S Studies in Computational Intelligence
%D 2012
%V 380
%I Springer
%F Agapitos:NCFE:2011
%X 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.
%K genetic algorithms, genetic programming, US Corporate Payout Policy, Symbolic Regression
%R doi:10.1007/978-3-642-23336-4_7
%U http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23335-7
%U http://dx.doi.org/doi:10.1007/978-3-642-23336-4_7
%P 123-139

%0 Conference Proceedings
%T Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Derivatives
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%Y Di Chio, Cecilia
%Y Agapitos, Alexandros
%Y Cagnoni, Stefano
%Y Cotta, Carlos
%Y Fernandez de Vega, F.
%Y Di Caro, Gianni A.
%Y Drechsler, Rolf
%Y Ekart, Aniko
%Y Esparcia-Alcazar, Anna I.
%Y Farooq, Muddassar
%Y Langdon, William B.
%Y Merelo, Juan J.
%Y Preuss, Mike
%Y Richter, Hendrik
%Y Silva, Sara
%Y Simoes, Anabela
%Y Squillero, Giovanni
%Y Tarantino, Ernesto
%Y Tettamanzi, Andrea G. B.
%Y Togelius, Julian
%Y Urquhart, Neil
%Y Uyar, A. Sima
%Y Yannakakis, Georgios N.
%S Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC
%S LNCS
%D 2011
%8 November 13 apr
%V 7248
%I Springer Verlag
%C Malaga, Spain
%F agapitos:evoapps12
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-29178-4_14
%U http://dx.doi.org/doi:10.1007/978-3-642-29178-4_14
%P 135-144

%0 Book Section
%T Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather Derivatives
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%E Michael, Doumpos
%E Constantin, Zopounidis
%E Panos, Pardalos
%B Financial Decision Making Using Computational Intelligence
%S Springer Optimization and Its Applications
%D 2012
%V 70
%I Springer
%F Agapitos:FDMCI:2012
%O Due: July 31, 2012
%K genetic algorithms, genetic programming, Weather derivatives pricing, Seasonal temperature forecasting, Autoregressive models, Supervised ensemble learning, Generalisation
%U http://www.springer.com/mathematics/applications/book/978-1-4614-3772-7
%P 153-182

%0 Conference Proceedings
%T Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition
%A Agapitos, Alexandros
%A Brabazon, Anthony
%A O’Neill, Michael
%Y Coello Coello, Carlos A.
%Y Cutello, Vincenzo
%Y Deb, Kalyanmoy
%Y Forrest, Stephanie
%Y Nicosia, Giuseppe
%Y Pavone, Mario
%S Parallel Problem Solving from Nature, PPSN XII (part 1)
%S Lecture Notes in Computer Science
%D 2012
%8 sep 1 5
%V 7491
%I Springer
%C Taormina, Italy
%F conf/ppsn/Agapitos12
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-32937-1_44
%U http://dx.doi.org/doi:10.1007/978-3-642-32937-1_44
%P 438-447

%0 Conference Proceedings
%T Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%Y Krawiec, Krzysztof
%Y Moraglio, Alberto
%Y Hu, Ting
%Y Uyar, A. Sima
%Y Hu, Bin
%S Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013
%S LNCS
%D 2013
%8 March 5 apr
%V 7831
%I Springer Verlag
%C Vienna, Austria
%F agapitos:2013:EuroGP
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-37207-0_1
%U http://dx.doi.org/doi:10.1007/978-3-642-37207-0_1
%P 1-12

%0 Conference Proceedings
%T Higher Order Functions for Kernel Regression
%A Agapitos, Alexandros
%A McDermott, James
%A O’Neill, Michael
%A Kattan, Ahmed
%A Brabazon, Anthony
%Y Nicolau, Miguel
%Y Krawiec, Krzysztof
%Y Heywood, Malcolm I.
%Y Castelli, Mauro
%Y Garcia-Sanchez, Pablo
%Y Merelo, Juan J.
%Y Rivas Santos, Victor M.
%Y Sim, Kevin
%S 17th European Conference on Genetic Programming
%S LNCS
%D 2014
%8 23 25 apr
%V 8599
%I Springer
%C Granada, Spain
%F agapitos:2014:EuroGP
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-662-44303-3_1
%U http://dx.doi.org/doi:10.1007/978-3-662-44303-3_1
%P 1-12

%0 Conference Proceedings
%T Ensemble Bayesian Model Averaging in Genetic Programming
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Brabazon, Anthony
%Y Coello Coello, Carlos A.
%S Proceedings of the 2014 IEEE Congress on Evolutionary Computation
%D 2014
%8 June 11 jul
%C Beijing, China
%@ 0-7803-8515-2
%F Agapitos:2014:CEC
%X 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.
%K genetic algorithms, Genetic programming, Data mining, Classification, clustering and data analysis
%R doi:10.1109/CEC.2014.6900567
%U http://dx.doi.org/doi:10.1109/CEC.2014.6900567
%P 2451-2458

%0 Conference Proceedings
%T Deep Evolution of Feature Representations for Handwritten Digit Recognition
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Nicolau, Miguel
%A Fagan, David
%A Kattan, Ahmed
%A Curran, Kathleen
%Y Murata, Yadahiko
%S Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)
%D 2015
%I IEEE Press
%C Sendai, Japan
%F agapitos:cec2015
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2015.7257189
%U http://dx.doi.org/doi:10.1109/CEC.2015.7257189
%P 2452-2459

%0 Conference Proceedings
%T Genetic Programming with Memory For Financial Trading
%A Agapitos, Alexandros
%A Brabazon, Anthony
%A O’Neill, Michael
%Y Squillero, Giovanni
%Y Burelli, Paolo
%S 19th European Conference on the Applications of Evolutionary Computation
%S Lecture Notes in Computer Science
%D 2016
%8 mar 30  apr 1
%V 9597
%I Springer
%C Porto, Portugal
%F EvoBafin16Agapitosetal
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-31204-0_2
%U http://dx.doi.org/10.1007/978-3-319-31204-0_2
%U http://dx.doi.org/doi:10.1007/978-3-319-31204-0_2
%P 19-34

%0 Journal Article
%T Recursion in tree-based genetic programming
%A Agapitos, Alexandros
%A O’Neill, Michael
%A Kattan, Ahmed
%A Lucas, Simon M.
%J Genetic Programming and Evolvable Machines
%D 2017
%8 jun
%V 18
%N 2
%@ 1389-2576
%F Agapitos:2016:GPEM
%X 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.
%K genetic algorithms, genetic programming, Evolutionary program synthesis Recursive programs, Variation operators, Fitness landscape analysis
%9 journal article
%R doi:10.1007/s10710-016-9277-5
%U http://dx.doi.org/doi:10.1007/s10710-016-9277-5
%P 149-183

%0 Journal Article
%T Regularised Gradient Boosting for Financial Time-series Modelling
%A Agapitos, Alexandros
%A Brabazon, Anthony
%A O’Neill, Michael
%J Computational Management Science
%D 2017
%8 jul
%V 14
%N 3
%F Agapitos:2018:CMS
%X 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.
%K 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
%9 journal article
%R doi:10.1007/s10287-017-0280-y
%U http://dx.doi.org/doi:10.1007/s10287-017-0280-y
%P 367-391

%0 Journal Article
%T A Survey of Statistical Machine Learning Elements in Genetic Programming
%A Agapitos, Alexandros
%A Loughran, Roisin
%A Nicolau, Miguel
%A Lucas, Simon
%A O’Neill, Michael
%A Brabazon, Anthony
%J IEEE Transactions on Evolutionary Computation
%D 2019
%8 dec
%V 23
%N 6
%@ 1089-778X
%F Agapitos:ieeeTEC
%X 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.
%K genetic algorithms, genetic programming, Statistical Machine Learning, SML, Generalisation, Overfitting, Classification, Symbolic Regression, Model selection, Regularisation, Model Averaging, Bias-Variance trade-off
%9 journal article
%R doi:10.1109/TEVC.2019.2900916
%U http://dx.doi.org/doi:10.1109/TEVC.2019.2900916
%P 1029-1048

%0 Conference Proceedings
%T Computational Brittleness and the Evolution of Computer Viruses
%A Agapow, Paul-Michael
%Y Voigt, Hans-Michael
%Y Ebeling, Werner
%Y Rechenberg, Ingo
%Y Schwefel, Hans-Paul
%S Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%D 1996
%8 22 26 sep
%V 1141
%I Springer-Verlag
%C Berlin, Germany
%@ 3-540-61723-X
%F agapow:1996:cbecv
%X 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.
%R doi:10.1007/3-540-61723-X_964
%U http://dx.doi.org/doi:10.1007/3-540-61723-X_964
%P 2-11

%0 Book Section
%T Genetic Programming for Wafer Property Prediction After Plasma Enhanced
%A Agarwal, Ashish
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%D 2000
%8 jun
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F agarwal:2000:GPWPPAPE
%K genetic algorithms, genetic programming
%P 16-24

%0 Journal Article
%T A high Performance Algorithm for Solving large scale Travelling Salesman Problem using Distributed Memory Architectures
%A Aggarwal, Khushboo
%A Singh, Sunil Kumar
%A Khattar, Sakar
%J Indian Journal of Computer Science and Engineering
%D 2011
%8 aug  sep
%V 2
%N 4
%@ 2231-3850
%G en
%F Aggarwal:2011:ijcse
%X 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.
%K genetic algorithms, genetic programming, TSP, traveling salesman problem, fitness functions
%9 journal article
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.6369
%P 516-521

%0 Generic
%T Prediction of Protein Secondary Structure using Genetic Programming
%A Aggarwal, Varun
%D 2003
%I Summer Internship Project Report During June-July 2003
%F Aggarwal:intern
%X Project 1:Using SOM and Genetic Programming to predict Protein Secondary structure Project 2: Improving PSIPRED Predictions using Genetic Programming
%K genetic algorithms, genetic programming
%U http://web.mit.edu/varun_ag/www/psspreport.pdf

%0 Conference Proceedings
%T Evolved Matrix Operations for Post-Processing Protein Secondary Structure Predictions
%A Aggarwal, Varun
%A MacCallum, Robert
%Y Keijzer, Maarten
%Y O’Reilly, Una-May
%Y Lucas, Simon M.
%Y Costa, Ernesto
%Y Soule, Terence
%S Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%D 2004
%8 May 7 apr
%V 3003
%I Springer-Verlag
%C Coimbra, Portugal
%@ 3-540-21346-5
%F maccallum:2004:eurogp
%X 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.
%K genetic algorithms, genetic programming: Poster
%R doi:10.1007/978-3-540-24650-3_20
%U http://web.mit.edu/varun_ag/www/aggarwal-eurogp2004.pdf
%U http://dx.doi.org/doi:10.1007/978-3-540-24650-3_20
%P 220-229

%0 Book Section
%T Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms
%A Aggarwal, Varun
%A O’Reilly, Una-May
%E Riolo, Rick L.
%E Soule, Terence
%E Worzel, Bill
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%D 2006
%8 November 13 may
%V 5
%I Springer
%C Ann Arbor
%@ 0-387-33375-4
%F Aggarwal:2006:GPTP
%X 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.
%K genetic algorithms, genetic programming, circuit sizing, symbolic regression, posynomial models, geometric programming
%R doi:10.1007/978-0-387-49650-4_14
%U http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf
%U http://dx.doi.org/doi:10.1007/978-0-387-49650-4_14
%P 219-236

%0 Journal Article
%T 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
%A Aghbashlo, Mortaza
%A Shamshirband, Shahaboddin
%A Tabatabaei, Meisam
%A Yee, Por Lip
%A Larimi, Yaser Nabavi
%J Energy
%D 2016
%V 94
%@ 0360-5442
%F Aghbashlo:2016:Energy
%X 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.
%K genetic algorithms, genetic programming, Biodiesel, DI diesel engine, Exergetic performance parameters, Expanded polystyrene, Cost sensitivity analysis, Extreme learning machine-wavelet (ELM-WT)
%9 journal article
%R doi:10.1016/j.energy.2015.11.008
%U http://www.sciencedirect.com/science/article/pii/S0360544215015327
%U http://dx.doi.org/doi:10.1016/j.energy.2015.11.008
%P 443-456

%0 Journal Article
%T Image classification: an evolutionary approach
%A Agnelli, Davide
%A Bollini, Alessandro
%A Lombardi, Luca
%J Pattern Recognition Letters
%D 2002
%V 23
%N 1-3
%@ 0167-8655
%F agnelli:2002:PRL
%X 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.
%K genetic algorithms, genetic programming, Image classification, Supervised learning
%9 journal article
%R doi:10.1016/S0167-8655(01)00128-3
%U http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656
%U http://dx.doi.org/doi:10.1016/S0167-8655(01)00128-3
%P 303-309

%0 Conference Proceedings
%T Reliability-Centered Maintenance Methodology-Based Fuzzy Classifier System Design for Fault Tolerance
%A Aguilar, Jose L.
%A Cerrada, Mariela
%Y Koza, John R.
%Y Banzhaf, Wolfgang
%Y Chellapilla, Kumar
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max H.
%Y Goldberg, David E.
%Y Iba, Hitoshi
%Y Riolo, Rick
%S Genetic Programming 1998: Proceedings of the Third Annual Conference
%D 1998
%8 22 25 jul
%I Morgan Kaufmann
%C University of Wisconsin, Madison, Wisconsin, USA
%@ 1-55860-548-7
%F aguilar:1998:rcmmcfssdft
%K genetic algorithms, classifiers
%P 621

%0 Conference Proceedings
%T Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems
%A Aguilar, Jose
%A Miranda, Pablo
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F aguilar:1999:ABGAMOP
%K genetic algorithms and classifier systems
%U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-873.pdf
%P 3-10

%0 Conference Proceedings
%T Three Geometric Approaches for representing Decision Rules in a Supervised Learning System
%A Aguilar, Jesus
%A Riquelme, Jose
%A Toro, Miguel
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F aguilar:1999:TGADRSLS
%K genetic algorithms and classifier systems, poster papers
%U http://gpbib.cs.ucl.ac.uk/gecco1999/GA-391.pdf
%P 771

%0 Conference Proceedings
%T Three geometric approaches for representing decision rules in a supervised learning system
%A Aguilar, Jesus
%A Riquelme, Jose
%A Toro, Miguel
%Y Brave, Scott
%Y Wu, Annie S.
%S Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%D 1999
%8 13 jul
%C Orlando, Florida, USA
%F aguilar:1999:T
%X hyperrectangles, rotated hyperrectangles and hyperellipses
%K Genetic Algorithms, data mining, supervised learning, hyper rectangles, rotated hyper rectangles, hyper ellipse
%P 8-15

%0 Conference Proceedings
%T Fuzzy Classifier System and Genetic Programming on System Identification Problems
%A Aguilar, Jose
%A Cerrada, Mariela
%Y Spector, Lee
%Y Goodman, Erik D.
%Y Wu, Annie
%Y Langdon, W. B.
%Y Voigt, Hans-Michael
%Y Gen, Mitsuo
%Y Sen, Sandip
%Y Dorigo, Marco
%Y Pezeshk, Shahram
%Y Garzon, Max H.
%Y Burke, Edmund
%S Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%D 2001
%8 July 11 jul
%I Morgan Kaufmann
%C San Francisco, California, USA
%@ 1-55860-774-9
%F aguilar3:2001:gecco
%K genetic algorithms, genetic programming, real world applications
%U http://gpbib.cs.ucl.ac.uk/gecco2001/d24.pdf
%P 1245-1251

%0 Conference Proceedings
%T Genetic Programming-Based Approach for System Identification Applying Genetic Programming to obtain Separation
%A Aguilar, Jose
%A Cerrada, Mariela
%Y Mastorakis, Nikos E.
%S WSES International Conferences WSEAS NNA-FSFS-EC 2001
%D 2001
%8 feb 11 15
%C Puerto De La Cruz, Tenerife, Spain
%F WSEAS_640_Aguilar
%X 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).
%K genetic algorithms, genetic programming, Genetic Programming, Evolutionary Computation, Identification Systems
%U http://www.wseas.us/e-library/conferences/tenerife2001/papers/640.pdf
%P 6401-6406

%0 Generic
%T A Data Mining Algorithm Based on the Genetic Programming
%A Aguilar, J.
%A Altamiranda, J.
%D 2004
%F Aguilar:2004:sci
%X 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.
%K genetic algorithms, genetic programming, Data Mining, Clustering

%0 Conference Proceedings
%T Data Extrapolation Using Genetic Programming to Matrices Singular Values Estimation
%A Aguilar, Jose
%A Gonzalez, Gilberto
%Y Yen, Gary G.
%Y Lucas, Simon M.
%Y Fogel, Gary
%Y Kendall, Graham
%Y Salomon, Ralf
%Y Zhang, Byoung-Tak
%Y Coello, Carlos A. Coello
%Y Runarsson, Thomas Philip
%S Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%8 16 21 jul
%I IEEE Press
%C Vancouver, BC, Canada
%@ 0-7803-9487-9
%F Aguilar:DEU:cec2006
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2006.1688718
%U http://ieeexplore.ieee.org/servlet/opac?punumber=11108
%U http://dx.doi.org/doi:10.1109/CEC.2006.1688718
%P 3227-3230

%0 Journal Article
%T Genetic algorithms and Darwinian approaches in financial applications: A survey
%A Aguilar-Rivera, Ruben
%A Valenzuela-Rendon, Manuel
%A Rodriguez-Ortiz, J. J.
%J Expert Systems with Applications
%D 2015
%V 42
%N 21
%@ 0957-4174
%F AguilarRivera:2015:ESA
%X 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.
%K genetic algorithms, genetic programming, Evolutionary computation, Finance, Portfolio optimization, Survey
%9 journal article
%R doi:10.1016/j.eswa.2015.06.001
%U http://www.sciencedirect.com/science/article/pii/S0957417415003954
%U http://dx.doi.org/doi:10.1016/j.eswa.2015.06.001
%P 7684-7697

%0 Conference Proceedings
%T A Genetic Programming Approach to Logic Function Synthesis by Means of Multiplexers
%A Aguirre, Arturo Hernandez
%A Coello, Carlos A. Coello
%A Buckles, Bill P.
%Y Stoica, Adrian
%Y Keymeulen, Didier
%Y Lohn, Jason
%S Proceedings of the The First NASA/DOD Workshop on Evolvable Hardware
%D 1999
%8 19 21 jul
%I IEEE Computer Society
%C Pasadena, California
%@ 0-7695-0256-3
%F aguirre:1999:EH
%X 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.
%K 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
%R doi:10.1109/EH.1999.785434
%U http://dx.doi.org/doi:10.1109/EH.1999.785434
%P 46-53

%0 Conference Proceedings
%T Cooperative Crossover and Mutation Operators in Genetic Algorithms
%A Aguirre, Hernan E.
%A Tanaka, Kiyoshi
%A Sugimura, Tatsuo
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F aguirre:1999:CCMOGA
%K genetic algorithms and classifier systems, poster papers
%P 772

%0 Journal Article
%T Evolutionary Synthesis of Logic Circuits Using Information Theory
%A Aguirre, Arturo Hernandez
%A Coello Coello, Carlos A.
%J Artificial Intelligence Review
%D 2003
%V 20
%N 3-4
%I Kluwer Academic Publishers
%@ 0269-2821
%G English
%F Aguirre:2003:AIR
%X 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.
%K genetic algorithms, genetic programming, circuit synthesis, computer-aided design, evolutionary algorithms, evolvable hardware, information theory
%9 journal article
%R doi:10.1023/B:AIRE.0000006603.98023.97
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.9801
%U http://dx.doi.org/doi:10.1023/B:AIRE.0000006603.98023.97
%P 445-471

%0 Conference Proceedings
%T Mutual Information-based Fitness Functions for Evolutionary Circuit Synthesis
%A Hernandez-Aguirre, Arturo
%A Coello-Coello, Carlos
%S Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%8 20 23 jun
%V 2
%I IEEE Press
%C Portland, Oregon
%@ 0-7803-8515-2
%F Hernandez-Aguirre:2004:MIFFfECS
%X 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.
%K genetic algorithms, genetic programming, EHW, Evolutionary Design Automation, Evolutionary design & evolvable hardware
%R doi:10.1109/CEC.2004.1331048
%U http://delta.cs.cinvestav.mx/~ccoello/conferences/cec04-muxmutual.pdf.gz
%U http://dx.doi.org/doi:10.1109/CEC.2004.1331048
%P 1309-1316

%0 Journal Article
%T Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies
%A Agwu, Okorie E.
%A Akpabio, Julius U.
%A Alabi, Sunday B.
%A Dosunmu, Adewale
%J Powder Technology
%D 2018
%V 339
%@ 0032-5910
%F AGWU:2018:PT
%X 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
%K genetic algorithms, genetic programming, Artificial Intelligence, Drill cuttings, Numerical simulations, Settling velocity
%9 journal article
%R doi:10.1016/j.powtec.2018.08.064
%U http://www.sciencedirect.com/science/article/pii/S0032591018307022
%U http://dx.doi.org/doi:10.1016/j.powtec.2018.08.064
%P 728-746

%0 Generic
%T Modeling Time Series of Real Systems using Genetic Programming
%A Ahalpara, Dilip P.
%A Parikh, Jitendra C.
%D 2006
%8 14 jul
%I ArXiv Nonlinear Sciences e-prints
%F nlin/0607029
%O Submitted to Physical Review E
%X 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.
%K genetic algorithms, genetic programming
%U http://arxiv.org/PS_cache/nlin/pdf/0607/0607029v1.pdf

%0 Journal Article
%T Genetic Programming based approach for Modeling Time Series data of real systems
%A Ahalpara, Dilip P.
%A Parikh, Jitendra C.
%J International Journal of Modern Physics C, Computational Physics and Physical Computation
%D 2008
%V 19
%N 1
%F Ahalpara:2008:IJMPC
%X 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.
%K genetic algorithms, genetic programming, Time series analysis, artificial neural networks
%9 journal article
%R doi:10.1142/S0129183108011942
%U http://dx.doi.org/doi:10.1142/S0129183108011942
%P 63-91

%0 Journal Article
%T Characterizing and modelling cyclic behaviour in non-stationary time series through multi-resolution analysis
%A Ahalpara, Dilip P.
%A Verma, Amit
%A Parikh, Jitendra C.
%A Panigrahi, Prasanta K.
%J Pramana
%D 2008
%8 nov
%V 71
%I Springer India, in co-publication with Indian Academy of Sciences
%@ 0304-4289
%F 2008Prama..71..459A
%X 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.
%K 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
%9 journal article
%R doi:10.1007/s12043-008-0125-x
%U http://dx.doi.org/doi:10.1007/s12043-008-0125-x
%P 459-485

%0 Conference Proceedings
%T Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEG
%A Ahalpara, Dilip
%A Arora, Siddharth
%A Santhanam, M.
%Y Vanneschi, Leonardo
%Y Gustafson, Steven
%Y Moraglio, Alberto
%Y De Falco, Ivanoe
%Y Ebner, Marc
%S Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%D 2009
%8 apr 15 17
%V 5481
%I Springer
%C Tuebingen
%F Ahalpara:2009:eurogp
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-01181-8_2
%U http://dx.doi.org/doi:10.1007/978-3-642-01181-8_2
%P 13-24

%0 Conference Proceedings
%T Improved forecasting of time series data of real system using genetic programming
%A Ahalpara, Dilip P.
%Y Branke, Juergen
%Y Pelikan, Martin
%Y Alba, Enrique
%Y Arnold, Dirk V.
%Y Bongard, Josh
%Y Brabazon, Anthony
%Y Butz, Martin V.
%Y Clune, Jeff
%Y Cohen, Myra
%Y Deb, Kalyanmoy
%Y Engelbrecht, Andries P.
%Y Krasnogor, Natalio
%Y Miller, Julian F.
%Y O’Neill, Michael
%Y Sastry, Kumara
%Y Thierens, Dirk
%Y van Hemert, Jano
%Y Vanneschi, Leonardo
%Y Witt, Carsten
%S GECCO ’10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%D 2010
%8 July 11 jul
%I ACM
%C Portland, Oregon, USA
%F Ahalpara:2010:gecco
%X 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.
%K genetic algorithms, genetic programming, Poster
%R doi:10.1145/1830483.1830658
%U http://dx.doi.org/doi:10.1145/1830483.1830658
%P 977-978

%0 Conference Proceedings
%T A Sniffer Technique for an Efficient Deduction of Model Dynamical Equations using Genetic Programming
%A Ahalpara, Dilip
%A Sen, Abhijit
%Y Silva, Sara
%Y Foster, James A.
%Y Nicolau, Miguel
%Y Giacobini, Mario
%Y Machado, Penousal
%S Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%D 2011
%8 27 29 apr
%V 6621
%I Springer Verlag
%C Turin, Italy
%F ahalpara:2011:EuroGP
%X 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.
%K genetic algorithms, genetic programming, local search, hill climbing
%R doi:10.1007/978-3-642-20407-4_1
%U http://dx.doi.org/doi:10.1007/978-3-642-20407-4_1
%P 1-12

%0 Journal Article
%T Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression
%A Ahangar-Asr, Alireza
%A Faramarzi, Asaad
%A Javadi, Akbar A.
%A Giustolisi, Orazio
%J Engineering Computation
%D 2011
%V 28
%N 4
%I Emerald Group Publishing Limited
%@ 0264-4401
%F Ahangar-Asr:2011:EC
%X 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.
%K genetic algorithms, genetic programming, Mechanical & Materials Engineering, Concretes, Mechanical behaviour of materials, Rubbers
%9 journal article
%R doi:10.1108/02644401111131902
%U http://dx.doi.org/doi:10.1108/02644401111131902
%P 492-507

%0 Thesis
%T Application of an Evolutionary Data Mining Technique for Constitutive Modelling of Geomaterials
%A Ahangarasr, Alireza
%D 2012
%8 31 dec
%C UK
%C University of Exeter
%F Ahangar-Asr:thesis
%X 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.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://hdl.handle.net/10871/9925

%0 Conference Proceedings
%T Removal of Mixed Impulse noise and Gaussian noise using genetic programming
%A Aher, R. P.
%A Jodhanle, K. C.
%S Signal Processing (ICSP), 2012 IEEE 11th International Conference on
%D 2012
%V 1
%F Aher:2012:ICSP
%X 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.
%K 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
%R doi:10.1109/ICoSP.2012.6491563
%U http://dx.doi.org/doi:10.1109/ICoSP.2012.6491563
%P 613-618

%0 Conference Proceedings
%T WES: Agent-based User Interaction Simulation on Real Infrastructure
%A Ahlgren, John
%A Berezin, Maria Eugenia
%A Bojarczuk, Kinga
%A Dulskyte, Elena
%A Dvortsova, Inna
%A George, Johann
%A Gucevska, Natalija
%A Harman, Mark
%A Laemmel, Ralf
%A Meijer, Erik
%A Sapora, Silvia
%A Spahr-Summers, Justin
%Y Yoo, Shin
%Y Petke, Justyna
%Y Weimer, Westley
%Y Bruce, Bobby R.
%S GI @ ICSE 2020
%D 2020
%8 March
%I ACM
%C internet
%F Ahlgren:2020:GI
%O Invited Keynote
%X 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.
%K genetic algorithms, genetic programming, genetic improvement, SBSE, social testing, APR, Connectivity, Data Science, Facebook AI Research, Human Computer Interaction, UX Human, Machine Learning
%R doi:10.1145/3387940.3392089
%U https://research.fb.com/wp-content/uploads/2020/04/WES-Agent-based-User-Interaction-Simulation-on-Real-Infrastructure.pdf
%U http://dx.doi.org/doi:10.1145/3387940.3392089
%P 276-284

%0 Unpublished Work
%T Using Genetic Programming to Play Mancala
%A Ahlschwede, John
%D 2000
%F ahlschwede:2000:ugppm
%9 unpublished
%U http://www.corngolem.com/john/gp/index.html

%0 Conference Proceedings
%T Co-Evolving Hierarchical Programs Using Genetic Programming
%A Ahluwalia, Manu
%A Fogarty, Terence C.
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F ahluwalia:1996:ccpGP
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap58.pdf
%P 419

%0 Conference Proceedings
%T Co-evolving Functions in Genetic Programming: A Comparison in ADF Selection Strategies
%A Ahluwalia, Manu
%A Bull, Larry
%A Fogarty, Terence C.
%Y Koza, John R.
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max
%Y Iba, Hitoshi
%Y Riolo, Rick L.
%S Genetic Programming 1997: Proceedings of the Second Annual Conference
%D 1997
%8 13 16 jul
%I Morgan Kaufmann
%C Stanford University, CA, USA
%F Ahluwalia:1997:
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/Ahluwalia_1997_.pdf
%P 3-8

%0 Conference Proceedings
%T Co-evolving Functions in Genetic Programming: An Emergent Approach using ADFs and GLiB
%A Ahluwalia, Manu
%A Bull, Larry
%A Fogarty, Terence C.
%Y Koza, John R.
%S Late Breaking Papers at the 1997 Genetic Programming Conference
%D 1997
%8 13–16 jul
%I Stanford Bookstore
%C Stanford University, CA, USA
%@ 0-18-206995-8
%F ahluwalia:1997:cfGPea
%K genetic algorithms, genetic programming
%P 1-6

%0 Conference Proceedings
%T Co-evolving Functions in Genetic Programming: Dynamic ADF Creation using GLiB
%A Ahluwalia, M.
%A Bull, L.
%Y Porto, V. William
%Y Saravanan, N.
%Y Waagen, D.
%Y Eiben, A. E.
%S Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%D 1998
%8 25 27 mar
%V 1447
%I Springer-Verlag
%C Mission Valley Marriott, San Diego, California, USA
%@ 3-540-64891-7
%F ahluwalia:1998:cfGP:ADF+GLiB
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0040753
%U http://dx.doi.org/doi:10.1007/BFb0040753
%P 809-818

%0 Conference Proceedings
%T A Genetic Programming-based Classifier System
%A Ahluwalia, Manu
%A Bull, Larry
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F ahluwalia:1999:AGPCS
%K genetic algorithms, genetic programming, classifier systems
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco1999/ahluwalia_1999_agpcs.pdf
%P 11-18

%0 Conference Proceedings
%T Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour
%A Ahluwalia, Manu
%A Bull, Larry
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 2
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F ahluwalia:1999:CFGPCK
%K genetic algorithms, genetic programming
%U http://gpbib.cs.ucl.ac.uk/gecco1999/GP-413.ps
%P 947-952

%0 Thesis
%T Co-evolving functions in genetic programming
%A Ahluwalia, Manu
%D 2000
%C UK
%C University of the West of England at Bristol
%F Ahluwalia:thesis
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322427

%0 Journal Article
%T Coevolving functions in genetic programming
%A Ahluwalia, Manu
%A Bull, Larry
%J Journal of Systems Architecture
%D 2001
%8 jul
%V 47
%N 7
%@ 1383-7621
%F Ahluwalia:2001:SA
%X 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.
%K genetic algorithms, genetic programming, ADF, Classification, EDF, Feature selection/extraction, Hierarchical programs, Knn, Speciation
%9 journal article
%R doi:10.1016/S1383-7621(01)00016-9
%U http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3
%U http://dx.doi.org/doi:10.1016/S1383-7621(01)00016-9
%P 573-585

%0 Conference Proceedings
%T Breast cancer detection using cartesian genetic programming evolved artificial neural networks
%A Ahmad, Arbab Masood
%A Khan, Gul Muhammad
%A Mahmud, Sahibzada Ali
%A Miller, Julian Francis
%Y Soule, Terry
%Y Auger, Anne
%Y Moore, Jason
%Y Pelta, David
%Y Solnon, Christine
%Y Preuss, Mike
%Y Dorin, Alan
%Y Ong, Yew-Soon
%Y Blum, Christian
%Y Silva, Dario Landa
%Y Neumann, Frank
%Y Yu, Tina
%Y Ekart, Aniko
%Y Browne, Will
%Y Kovacs, Tim
%Y Wong, Man-Leung
%Y Pizzuti, Clara
%Y Rowe, Jon
%Y Friedrich, Tobias
%Y Squillero, Giovanni
%Y Bredeche, Nicolas
%Y Smith, Stephen L.
%Y Motsinger-Reif, Alison
%Y Lozano, Jose
%Y Pelikan, Martin
%Y Meyer-Nienberg, Silja
%Y Igel, Christian
%Y Hornby, Greg
%Y Doursat, Rene
%Y Gustafson, Steve
%Y Olague, Gustavo
%Y Yoo, Shin
%Y Clark, John
%Y Ochoa, Gabriela
%Y Pappa, Gisele
%Y Lobo, Fernando
%Y Tauritz, Daniel
%Y Branke, Jurgen
%Y Deb, Kalyanmoy
%S GECCO ’12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference
%D 2012
%8 July 11 jul
%I ACM
%C Philadelphia, Pennsylvania, USA
%F Ahmad:2012:GECCO
%X 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).
%K genetic algorithms, genetic programming, real world applications
%R doi:10.1145/2330163.2330307
%U http://dx.doi.org/doi:10.1145/2330163.2330307
%P 1031-1038

%0 Conference Proceedings
%T Bio-signal Processing Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)
%A Ahmad, Arbab Masood
%A Khan, Gul Muhammad
%S Frontiers of Information Technology (FIT), 2012 10th International Conference on
%D 2012
%F Ahmad:2012:FIT
%X 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.
%K 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
%R doi:10.1109/FIT.2012.54
%U http://dx.doi.org/doi:10.1109/FIT.2012.54
%P 261-268

%0 Conference Proceedings
%T Classification of Arrhythmia Types Using Cartesian Genetic Programming Evolved Artificial Neural Networks
%A Ahmad, Arbab Masood
%A Khan, Gul Muhammad
%A Mahmud, Sahibzada Ali
%Y Iliadis, Lazaros S.
%Y Papadopoulos, Harris
%Y Jayne, Chrisina
%S Proceedings of 14th International Conference on Engineering Applications of Neural Networks (EANN 2013), Part I
%S Communications in Computer and Information Science
%D 2013
%8 sep 13 16
%V 383
%I Springer
%C Halkidiki, Greece
%F conf/eann/AhmadKM13
%X 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.
%K genetic algorithms, genetic programming, cartesian genetic programming, CGPANN, artificial neural network, neuro-evolution, CVD, cardiac arrhythmias, classification, fiducial points, LBBB beats, RBBB beats
%R doi:10.1007/978-3-642-41013-0_29
%U http://dx.doi.org/10.1007/978-3-642-41013-0
%U http://dx.doi.org/doi:10.1007/978-3-642-41013-0_29
%P 282-291

%0 Conference Proceedings
%T Classification of Mammograms Using Cartesian Genetic Programming Evolved Artificial Neural Networks
%A Ahmad, Arbab Masood
%A Khan, Gul Muhammad
%A Mahmud, Sahibzada Ali
%Y Iliadis, Lazaros S.
%Y Maglogiannis, Ilias
%Y Papadopoulos, Harris
%S Proceedings 10th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Innovations, AIAI 2014
%S IFIP Advances in Information and Communication Technology
%D 2014
%V 436
%I Springer
%C Rhodes, Greece, September 19-21, 2014
%F conf/ifip12/AhmadKM14
%X 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.
%K genetic algorithms, genetic programming, cartesian genetic programming, mammogram image classification, GLCM, CGPANN, haralick’s parameters
%R doi:10.1007/978-3-662-44654-6_20
%U http://dx.doi.org/10.1007/978-3-662-44654-6_20
%U http://dx.doi.org/doi:10.1007/978-3-662-44654-6_20
%P 203-213

%0 Conference Proceedings
%T A comparison of semantic-based initialization methods for genetic programming
%A Ahmad, Hammad
%A Helmuth, Thomas
%Y Cotta, Carlos
%Y Ray, Tapabrata
%Y Ishibuchi, Hisao
%Y Obayashi, Shigeru
%Y Filipic, Bogdan
%Y Bartz-Beielstein, Thomas
%Y Dick, Grant
%Y Munetomo, Masaharu
%Y Fernandez Alzueta, Silvino
%Y Stuetzle, Thomas
%Y Pellicer, Pablo Valledor
%Y Lopez-Ibanez, Manuel
%Y Tauritz, Daniel R.
%Y Oliveto, Pietro S.
%Y Weise, Thomas
%Y Wrobel, Borys
%Y Zamuda, Ales
%Y Auger, Anne
%Y Bect, Julien
%Y Brockhoff, Dimo
%Y Hansen, Nikolaus
%Y Le Riche, Rodolphe
%Y Picheny, Victor
%Y Derbel, Bilel
%Y Li, Ke
%Y Li, Hui
%Y Li, Xiaodong
%Y Zapotecas, Saul
%Y Zhang, Qingfu
%Y Doncieux, Stephane
%Y Duro, Richard
%Y Auerbach, Joshua
%Y de Vladar, Harold
%Y Fernandez-Leiva, Antonio J.
%Y Merelo, J. J.
%Y Castillo-Valdivieso, Pedro A.
%Y Camacho-Fernandez, David
%Y Chavez de la O, Francisco
%Y Akman, Ozgur
%Y Alyahya, Khulood
%Y Branke, Juergen
%Y Doherty, Kevin
%Y Fieldsend, Jonathan
%Y Marano, Giuseppe Carlo
%Y Lagaros, Nikos D.
%Y Nakayama, Koichi
%Y Oshima, Chika
%Y Wagner, Stefan
%Y Affenzeller, Michael
%Y Naujoks, Boris
%Y Volz, Vanessa
%Y Tusar, Tea
%Y Kerschke, Pascal
%Y Alshammari, Riyad
%Y Makanju, Tokunbo
%Y Alexander, Brad
%Y Haraldsson, Saemundur O.
%Y Wagner, Markus
%Y Woodward, John R.
%Y Yoo, Shin
%Y McCall, John
%Y Sanchez-Pi, Nayat
%Y Marti, Luis
%Y Vasconcellos, Danilo
%Y Nakata, Masaya
%Y Stein, Anthony
%Y Veerapen, Nadarajen
%Y Liefooghe, Arnaud
%Y Verel, Sebastien
%Y Ochoa, Gabriela
%Y Smith, Stephen L.
%Y Cagnoni, Stefano
%Y Patton, Robert M.
%Y La Cava, William
%Y Olson, Randal
%Y Orzechowski, Patryk
%Y Urbanowicz, Ryan
%Y De Falco, Ivanoe
%Y Della Cioppa, Antonio
%Y Tarantino, Ernesto
%Y Scafuri, Umberto
%Y Baltus, P. G. M.
%Y Iacca, Giovanni
%Y Hallawa, Ahmed
%Y Yaman, Anil
%Y Rahat, Alma
%Y Wang, Handing
%Y Jin, Yaochu
%Y Walker, David
%Y Everson, Richard
%Y Oyama, Akira
%Y Shimoyama, Koji
%Y Kumar, Hemant
%Y Chiba, Kazuhisa
%Y Palar, Pramudita Satria
%S GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
%D 2018
%8 15 19 jul
%I ACM
%C Kyoto, Japan
%F Ahmad:2018:GECCOcomp
%X 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
%K genetic algorithms, genetic programming
%R doi:10.1145/3205651.3208218
%U http://dx.doi.org/doi:10.1145/3205651.3208218
%P 1878-1881

%0 Journal Article
%T Genetic Programming In Clusters
%A Ahmad, Ishfaq
%J IEEE Concurrency
%D 2000
%8 jul \slash sep
%V 8
%N 3
%I IEEE Computer Society
%C Los Alamitos, CA, USA
%@ 1092-3063
%F Ahmad:2000:CCGc
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1109/MCC.2000.10016
%U http://csdl.computer.org/comp/mags/pd/2000/03/p3toc.htm
%U http://dx.doi.org/doi:10.1109/MCC.2000.10016
%P 10-11,13

%0 Conference Proceedings
%T Evolving MIMO multi-layered artificial neural networks using grammatical evolution
%A Ahmad, Qadeer
%A Rafiq, Atif
%A Raja, Muhammad Adil
%A Javed, Noman
%Y Hung, Chih-Cheng
%Y Papadopoulos, George A.
%S Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019
%D 2019
%8 apr 8 12
%I ACM
%C Limassol, Cyprus
%F conf/sac/AhmadRRJ19
%K genetic algorithms, genetic programming, grammatical evolution, ANN
%R doi:10.1145/3297280.3297408
%U http://dx.doi.org/doi:10.1145/3297280.3297408
%P 1278-1285

%0 Journal Article
%T Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm
%A Ahmadizar, Fardin
%A Soltanian, Khabat
%A AkhlaghianTab, Fardin
%A Tsoulos, Ioannis
%J Engineering Applications of Artificial Intelligence
%D 2015
%8 mar
%V 39
%@ 0952-1976
%F journals/eaai/AhmadizarSAT15
%X 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.
%K genetic algorithms, genetic programming, grammatical evolution, Neural networks, ANN, Adaptive penalty approach, Classification problems
%9 journal article
%U http://www.sciencedirect.com/science/article/pii/S0952197614002759
%P 1-13

%0 Conference Proceedings
%T Evolutionary fusion of local texture patterns for facial expression recognition
%A Ahmed, Faisal
%A Paul, Padma Polash
%A Gavrilova, Marina L.
%S 2015 IEEE International Conference on Image Processing (ICIP)
%D 2015
%8 sep
%F Ahmed:2015:ieeeICIP
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/ICIP.2015.7350956
%U http://dx.doi.org/doi:10.1109/ICIP.2015.7350956
%P 1031-1035

%0 Conference Proceedings
%T Genetic Programming for Biomarker Detection in Mass Spectrometry Data
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%Y Thielscher, Michael
%Y Zhang, Dongmo
%S 25th Joint Conference Australasian Conference on Artificial Intelligence, AI 2012
%S Lecture Notes in Computer Science
%D 2012
%8 dec 4 7
%V 7691
%I Springer
%C Sydney, Australia
%F DBLP:conf/ausai/AhmedZP12
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-35101-3_23
%U http://dx.doi.org/doi:10.1007/978-3-642-35101-3_23
%P 266-278

%0 Conference Proceedings
%T Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%Y Vanneschi, Leonardo
%Y Bush, William S.
%Y Giacobini, Mario
%S 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2013
%S LNCS
%D 2013
%8 apr 3 5
%V 7833
%I Springer Verlag
%C Vienna, Austria
%F Ahmed:2013:evobio
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-37189-9_5
%U http://dx.doi.org/doi:10.1007/978-3-642-37189-9_5
%P 43-55

%0 Conference Proceedings
%T Enhanced Feature Selection for Biomarker Discovery in LC-MS Data using GP
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%Y de la Fraga, Luis Gerardo
%S 2013 IEEE Conference on Evolutionary Computation
%D 2013
%8 jun 20 23
%V 1
%C Cancun, Mexico
%F Ahmed:2013:CEC
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2013.6557621
%U http://dx.doi.org/doi:10.1109/CEC.2013.6557621
%P 584-591

%0 Conference Proceedings
%T GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%Y Esparcia-Alcazar, Anna Isabel
%Y Mora, Antonio Miguel
%S 17th European Conference on the Applications of Evolutionary Computation
%S LNCS
%D 2014
%8 23 25 apr
%V 8602
%I Springer
%C Granada
%F Ahmed:evoapps14
%X 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
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-662-45523-4_74
%U http://dx.doi.org/doi:10.1007/978-3-662-45523-4_74
%P 915-927

%0 Conference Proceedings
%T A New GP-Based Wrapper Feature Construction Approach to Classification and Biomarker Identification
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%Y Coello Coello, Carlos A.
%S Proceedings of the 2014 IEEE Congress on Evolutionary Computation
%D 2014
%8 June 11 jul
%C Beijing, China
%@ 0-7803-8515-2
%F Ahmed:2014:CEC
%X 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.
%K genetic algorithms, genetic programming, Evolutionary programming, Biometrics, bioinformatics and biomedical applications
%R doi:10.1109/CEC.2014.6900317
%U http://dx.doi.org/doi:10.1109/CEC.2014.6900317
%P 2756-2763

%0 Conference Proceedings
%T Multiple feature construction for effective biomarker identification and classification using genetic programming
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%A Xue, Bing
%Y Igel, Christian
%Y Arnold, Dirk V.
%Y Gagne, Christian
%Y Popovici, Elena
%Y Auger, Anne
%Y Bacardit, Jaume
%Y Brockhoff, Dimo
%Y Cagnoni, Stefano
%Y Deb, Kalyanmoy
%Y Doerr, Benjamin
%Y Foster, James
%Y Glasmachers, Tobias
%Y Hart, Emma
%Y Heywood, Malcolm I.
%Y Iba, Hitoshi
%Y Jacob, Christian
%Y Jansen, Thomas
%Y Jin, Yaochu
%Y Kessentini, Marouane
%Y Knowles, Joshua D.
%Y Langdon, William B.
%Y Larranaga, Pedro
%Y Luke, Sean
%Y Luque, Gabriel
%Y McCall, John A. W.
%Y Montes de Oca, Marco A.
%Y Motsinger-Reif, Alison
%Y Ong, Yew Soon
%Y Palmer, Michael
%Y Parsopoulos, Konstantinos E.
%Y Raidl, Guenther
%Y Risi, Sebastian
%Y Ruhe, Guenther
%Y Schaul, Tom
%Y Schmickl, Thomas
%Y Sendhoff, Bernhard
%Y Stanley, Kenneth O.
%Y Stuetzle, Thomas
%Y Thierens, Dirk
%Y Togelius, Julian
%Y Witt, Carsten
%Y Zarges, Christine
%S GECCO ’14: Proceedings of the 2014 conference on Genetic and evolutionary computation
%D 2014
%8 December 16 jul
%I ACM
%C Vancouver, BC, Canada
%F Ahmed:2014:GECCOa
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/2576768.2598292
%U http://doi.acm.org/10.1145/2576768.2598292
%U http://dx.doi.org/doi:10.1145/2576768.2598292
%P 249-256

%0 Conference Proceedings
%T Prediction of detectable peptides in MS data using genetic programming
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%Y Igel, Christian
%Y Arnold, Dirk V.
%Y Gagne, Christian
%Y Popovici, Elena
%Y Auger, Anne
%Y Bacardit, Jaume
%Y Brockhoff, Dimo
%Y Cagnoni, Stefano
%Y Deb, Kalyanmoy
%Y Doerr, Benjamin
%Y Foster, James
%Y Glasmachers, Tobias
%Y Hart, Emma
%Y Heywood, Malcolm I.
%Y Iba, Hitoshi
%Y Jacob, Christian
%Y Jansen, Thomas
%Y Jin, Yaochu
%Y Kessentini, Marouane
%Y Knowles, Joshua D.
%Y Langdon, William B.
%Y Larranaga, Pedro
%Y Luke, Sean
%Y Luque, Gabriel
%Y McCall, John A. W.
%Y Montes de Oca, Marco A.
%Y Motsinger-Reif, Alison
%Y Ong, Yew Soon
%Y Palmer, Michael
%Y Parsopoulos, Konstantinos E.
%Y Raidl, Guenther
%Y Risi, Sebastian
%Y Ruhe, Guenther
%Y Schaul, Tom
%Y Schmickl, Thomas
%Y Sendhoff, Bernhard
%Y Stanley, Kenneth O.
%Y Stuetzle, Thomas
%Y Thierens, Dirk
%Y Togelius, Julian
%Y Witt, Carsten
%Y Zarges, Christine
%S GECCO Comp ’14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion
%D 2014
%8 December 16 jul
%I ACM
%C Vancouver, BC, Canada
%F Ahmed:2014:GECCOcomp
%X 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.
%K genetic algorithms, genetic programming, biological and biomedical applications: Poster
%R doi:10.1145/2598394.2598421
%U http://doi.acm.org/10.1145/2598394.2598421
%U http://dx.doi.org/doi:10.1145/2598394.2598421
%P 37-38

%0 Journal Article
%T Improving Feature Ranking for Biomarker Discovery in Proteomics Mass Spectrometry Data using Genetic Programming
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%J Connection Science
%D 2014
%V 26
%N 3
%@ 0954-0091
%F Ahmed:2014:CS
%X 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.
%K genetic algorithms, genetic programming, biomarker discovery, feature selection, classification
%9 journal article
%R doi:10.1080/09540091.2014.906388
%U http://dx.doi.org/doi:10.1080/09540091.2014.906388
%P 215-243

%0 Conference Proceedings
%T Genetic Programming for Measuring Peptide Detectability
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%A Xue, Bing
%Y Dick, Grant
%Y Browne, Will N.
%Y Whigham, Peter A.
%Y Zhang, Mengjie
%Y Bui, Lam Thu
%Y Ishibuchi, Hisao
%Y Jin, Yaochu
%Y Li, Xiaodong
%Y Shi, Yuhui
%Y Singh, Pramod
%Y Tan, Kay Chen
%Y Tang, Ke
%S Simulated Evolution and Learning - 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings
%S Lecture Notes in Computer Science
%D 2014
%V 8886
%I Springer
%F conf/seal/AhmedZPX14
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-319-13563-2
%P 593-604

%0 Conference Proceedings
%T A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data
%A Ahmed, Soha
%A Zhang, Mengjie
%A Peng, Lifeng
%A Xue, Bing
%Y Squillero, Giovanni
%Y Burelli, Paolo
%S 19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016
%S Lecture Notes in Computer Science
%D 2016
%8 mar 30 – apr 1
%V 9597
%I Springer
%C Porto, Portugal
%F conf/evoW/AhmedZPX16
%X 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
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-31204-0_8
%U http://dx.doi.org/doi:10.1007/978-3-319-31204-0_8
%P 106-122

%0 Conference Proceedings
%T A Genetic Programming Approach to Data Clustering
%A Ahn, Chang Wook
%A Oh, Sanghoun
%A Oh, Moonyoung
%Y Kim, Tai-Hoon
%Y Adeli, Hojjat
%Y Grosky, William I.
%Y Pissinou, Niki
%Y Shih, Timothy K.
%Y Rothwell, Edward J.
%Y Kang, Byeong Ho
%Y Shin, Seung-Jung
%S Proceedings of the International Conference on Multimedia, Computer Graphics and Broadcasting (MulGraB 2011) Part II
%S Communications in Computer and Information Science
%D 2011
%8 dec 8 10
%V 263
%I Springer
%C Jeju Island, Korea
%F conf/fgit/AhnOO11
%O Held as Part of the Future Generation Information Technology Conference, FGIT 2011, in Conjunction with GDC 2011
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-27186-1_15
%U http://dx.doi.org/doi:10.1007/978-3-642-27186-1_15
%P 123-132

%0 Journal Article
%T A genetic algorithm for fitting Lorentzian line shapes in Mossbauer spectra
%A Ahonen, Hannu
%A de Souza Jr., Paulo A.
%A Garg, Vijayendra Kumar
%J Nuclear Instruments and Methods in Physics Research B
%D 1997
%8 May
%V 124
%@ 0168583X
%F Aho97
%X 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.
%K genetic algorithms
%9 journal article
%P 633-638

%0 Conference Proceedings
%T AutoQP: Genetic Programming for Quantum Programming
%A Ahsan, Usama
%A ul Amir Afsar Minhas, Fayyaz
%S 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST)
%D 2020
%8 jan
%F Ahsan:2020:IBCAST
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/IBCAST47879.2020.9044554
%U http://dx.doi.org/doi:10.1109/IBCAST47879.2020.9044554
%P 378-382

%0 Journal Article
%T A Survey of Genetic Programming and Its Applications
%A Ahvanooey, Milad Taleby
%A Li, Qianmu
%A Wu, Ming
%A Wang, Shuo
%J KSII Trans. Internet Inf. Syst.
%D 2019
%V 13
%N 4
%F DBLP:journals/itiis/AhvanooeyLWW19
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.3837/tiis.2019.04.002
%U https://doi.org/10.3837/tiis.2019.04.002
%U http://dx.doi.org/doi:10.3837/tiis.2019.04.002
%P 1765-1794

%0 Thesis
%T QoS-aware web service composition using genetic algorithms
%A Ai, Lifeng
%D 2011
%8 jun
%C Australia
%C Queensland University of Technology
%F Lifeng_Ai_Thesis
%X 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.
%K genetic algorithms, quality of service, web services, composite web services, optimisation
%9 Ph.D. thesis
%U http://eprints.qut.edu.au/46666/1/Lifeng_Ai_Thesis.pdf

%0 Conference Proceedings
%T Cooperative Co-evolution Inspired Operators for Classical GP Schemes
%A Aichour, Malek
%A Lutton, Evelyne
%Y Krasnogor, Natalio
%Y Nicosia, Giuseppe
%Y Pavone, Mario
%Y Pelta, David
%S Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO ’07)
%S Studies in Computational Intelligence
%D 2007
%8 August 10 nov
%V 129
%I Springer
%C Acireale, Italy
%F Aichour:2007:NICSO
%X 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
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-78987-1_16
%U http://dx.doi.org/doi:10.1007/978-3-540-78987-1_16
%P 169-178

%0 Conference Proceedings
%T Genetic Programming Approaches for Minimum Cost Topology Optimisation of Optical Telecommunication Networks
%A Aiyarak, P.
%A Saket, A. S.
%A Sinclair, M. C.
%Y Zalzala, Ali
%S Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%D 1997
%8 January 4 sep
%I IEE
%C University of Strathclyde, Glasgow, UK
%@ 0-85296-693-8
%F aiyarak:1997:GPtootn
%X 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.
%K genetic algorithms, genetic programming, telecommunication networks, topology
%R doi:10.1049/cp:19971216
%U http://uk.geocities.com/markcsinclair/ps/galesia97_aiy.ps.gz
%U http://dx.doi.org/doi:10.1049/cp:19971216
%P 415-420

%0 Conference Proceedings
%T A novel estimation methodology for tracheal pressure in mechanical ventilation control
%A Ajcevic, Milos
%A De Lorenzo, Andrea
%A Accardo, Agostino
%A Bartoli, Alberto
%A Medvet, Eric
%S 8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013)
%D 2013
%8 April 6 sep
%C Trieste, Italy
%F Ajcevic:2013:ISPA
%X 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.
%K 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
%R doi:10.1109/ISPA.2013.6703827
%U http://dx.doi.org/doi:10.1109/ISPA.2013.6703827
%P 695-699

%0 Thesis
%T Personalized setup of high frequency percussive ventilator by estimation of respiratory system viscoelastic parameters
%A Ajcevic, Milos
%D 2013/2014
%C Italy
%C Universita degli studi di Trieste
%F Ajcevic:thesis
%X 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.
%K genetic algorithms, genetic programming, High Frequency Percussive Ventilation, Respiratory signal processing, Parameter identification
%9 Ph.D. thesis
%U http://hdl.handle.net/10077/10976

%0 Book Section
%T Developing a Computer-Controller Opponent for a First-Person Simulation Game using Genetic Programming
%A Akalin, Frederick R.
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%D 2002
%8 jun
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F akalin:2002:DCOFSGGP
%K genetic algorithms, genetic programming
%P 11-20

%0 Conference Proceedings
%T Derivation of Relational Fuzzy Classification Rules Using Evolutionary Computation
%A Akbarzadeh, Vahab
%A Sadeghian, Alireza
%A dos Santos, Marcus V.
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Akbarzadeh:2008:fuzz
%X 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.
%K 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
%R doi:10.1109/FUZZY.2008.4630598
%U FS0398.pdf
%U http://dx.doi.org/doi:10.1109/FUZZY.2008.4630598
%P 1689-1693

%0 Conference Proceedings
%T Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy
%A Akbarzadeh-T., M.-R.
%A Tunstel, E.
%A Jamshidi, M.
%S Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment
%D 1997
%8 26 29 apr
%C Albuquerque, NM, USA
%F Akbarzadeh:1997:jce
%X 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.
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf
%P 373-377

%0 Conference Proceedings
%T Soft computing paradigms for hybrid fuzzy controllers: experiments and applications
%A Akbarzadeh-T., M. R.
%A Tunstel, E.
%A Kumbla, K.
%A Jamshidi, M.
%S Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%8 May 9 may
%V 2
%I IEEE Press
%C Anchorage, Alaska, USA
%@ 0-7803-4863-X
%F Akbarzadeh:1998:wcci
%X 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.
%K 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
%R doi:10.1109/FUZZY.1998.686289
%U http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/fieee98.pdf
%U http://dx.doi.org/doi:10.1109/FUZZY.1998.686289
%P 1200-1205

%0 Journal Article
%T Soft computing for autonomous robotic systems
%A Akbarzadeh-T., M.-R.
%A Kumbla, K.
%A Tunstel, E.
%A Jamshidi, M.
%J Computers and Electrical Engineering
%D 2000
%V 26
%N 1
%F Akbarzadeh-T:2000:CEE
%X 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.
%K genetic algorithms, genetic programming, Soft computing, Neural networks, Fuzzy logic, Robotic control, Articial intelligence
%9 journal article
%U http://www.sciencedirect.com/science/article/B6V25-3Y6GXY5-2/1/6a6f9ff946815d4e95fe3884c98e74e5
%P 5-32

%0 Conference Proceedings
%T Friendship Modeling for Cooperative Co-Evolutionary Fuzzy Systems: A Hybrid GA-GP Algorithm
%A Akbarzadeh-T., M.-R.
%A Mosavat, I.
%A Abbasi, S.
%S Proceedings of the 22nd International Conference of North American Fuzzy Information Processing Society, NAFIPS 2003
%D 2003
%8 24 26 jul
%F Akbarzadeh:2003:ICNAFIPS
%X 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.
%K 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
%R doi:10.1109/NAFIPS.2003.1226756
%U http://dx.doi.org/doi:10.1109/NAFIPS.2003.1226756
%P 61-66

%0 Conference Proceedings
%T Multiple-Organisms Learning and Evolution by Genetic Programming
%A Akira, Yoshida
%Y McKay, Bob
%Y Tsujimura, Yasuhiro
%Y Sarker, Ruhul
%Y Namatame, Akira
%Y Yao, Xin
%Y Gen, Mitsuo
%S Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems
%D 1999
%8 22 25 nov
%C School of Computer Science Australian Defence Force Academy, Canberra, Australia
%F Akira:1999:AJ
%K genetic algorithms, genetic programming

%0 Conference Proceedings
%T Intraspecific Evolution of Learning by Genetic Programming
%A Akira, Yoshida
%Y Poli, Riccardo
%Y Banzhaf, Wolfgang
%Y Langdon, William B.
%Y Miller, Julian F.
%Y Nordin, Peter
%Y Fogarty, Terence C.
%S Genetic Programming, Proceedings of EuroGP’2000
%S LNCS
%D 2000
%8 15 16 apr
%V 1802
%I Springer-Verlag
%C Edinburgh
%@ 3-540-67339-3
%F akira:2000:moelGP
%X 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.
%K genetic algorithms, genetic programming: Poster
%R doi:10.1007/978-3-540-46239-2_15
%U http://dx.doi.org/doi:10.1007/978-3-540-46239-2_15
%P 209-224

%0 Journal Article
%T Software Defect Prediction Using Genetic Programming and Neural Networks
%A Akour, Mohammed
%A Melhem, Wasen Yahya
%J International Journal of Open Source Software and Processes
%D 2017
%V 8
%N 4
%@ 1942-3926
%F journals/ijossp/AkourM17
%X 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.
%K genetic algorithms, genetic programming, ANN, SBSE
%9 journal article
%R doi:10.4018/IJOSSP.2017100102
%U http://dx.doi.org/doi:10.4018/IJOSSP.2017100102
%P 32-51

%0 Journal Article
%T Using Multi Expression Programming in Software Effort Estimation
%A AL-Saati, Najla Akram
%A Alreffaee, Taghreed Riyadh
%J International Journal of Recent Research and Review
%D 2017
%8 jun
%V X
%N 2
%@ 2277-8322
%F Akram:2017:ijrr
%X 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.
%K genetic algorithms, genetic programming, Multi Expression Programming, SBSE, Software Effort, Estimation, Software Engineering
%9 journal article
%U http://www.ijrrr.com/papers10-2/paper1-Using%20Multi%20Expression%20Programming%20in%20Software%20Effort%20Estimation.pdf
%P 1-10

%0 Generic
%T Using Multi Expression Programming in Software Effort Estimation
%A Al-Saati, Najla Akram
%A Alreffaee, Taghreed Riyadh
%D 2018
%8 30 apr
%I arXiv
%F Akram:2018:arxiv
%X 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.
%K genetic algorithms, genetic programming, SBSE, ANN, software effort, estimation, multi expression programming
%U http://arxiv.org/abs/1805.00090

%0 Generic
%T Applying Gene Expression Programming for Solving One-Dimensional Bin-Packing Problems
%A Al-Saati, Najla Akram
%D 2020
%8 nov
%I arXiv
%F journals/corr/abs-2001-09923
%K genetic algorithms, genetic programming, gene expression programming
%U https://arxiv.org/abs/2001.09923

%0 Journal Article
%T Quality by Design Approach: Application of Artificial Intelligence Techniques of Tablets Manufactured by Direct Compression
%A Aksu, Buket
%A Paradkar, Anant
%A Matas, Marcel
%A Ozer, Ozgen
%A Guneri, Tamer
%A York, Peter
%J AAPS PharmSciTech
%D 2012
%8 sep  06
%V 13
%N 4
%I American Association of Pharmaceutical Scientists
%G English
%F Aksu:2012:AAPS
%X 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.
%K genetic algorithms, genetic programming, gene expression programming, artificial neural networks, ANNs, GEP, optimisation, quality by design (qbd)
%9 journal article
%R doi:10.1208/s12249-012-9836-x
%U http://dx.doi.org/10.1208/s12249-012-9836-x
%U http://dx.doi.org/doi:10.1208/s12249-012-9836-x
%P 1138-1146

%0 Conference Proceedings
%T A Genetic Programming Classifier Design Approach for Cell Images
%A Akyol, Aydin
%A Yaslan, Yusuf
%A Erol, Osman Kaan
%Y Mellouli, Khaled
%S Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU
%S Lecture Notes in Computer Science
%D 2007
%8 oct 31  nov 2
%V 4724
%I Springer
%C Hammamet, Tunisia
%F conf/ecsqaru/AkyolYE07
%X 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.
%K genetic algorithms, genetic programming, cell classification, classifier design, pollen classification
%R doi:10.1007/978-3-540-75256-1_76
%U http://dx.doi.org/doi:10.1007/978-3-540-75256-1_76
%P 878-888

%0 Thesis
%T Image Reconstructing in Electrical Capacitance Tomography of Manufacturing Processes Using Genetic Programming
%A Al-Afeef, Ala’ S.
%D 2010
%8 jul
%C Al-Salt, Jordan
%C Al-Balqa Applied University
%F Al-Afeef:mastersthesis
%X 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.
%K genetic algorithms, genetic programming, Image Reconstructing, Electrical Capacitance Tomography
%9 Masters thesis
%U https://sites.google.com/site/alaaalfeef/home/Alaa_afeef_Thesis_Final.pdf

%0 Conference Proceedings
%T Image reconstruction of a metal fill industrial process using Genetic Programming
%A Al-Afeef, Alaa
%A Sheta, Alaa F.
%A Al-Rabea, Adnan
%S 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010
%D 2010
%8 29 nov  1 dec
%C Cairo
%F Al-Afeef:2010:ISDA
%X 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.
%K 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
%R doi:10.1109/ISDA.2010.5687299
%U http://sites.google.com/site/alaaalfeef/home/8.pdf
%U http://dx.doi.org/doi:10.1109/ISDA.2010.5687299
%P 12-17

%0 Book
%T Image Reconstruction of a Manufacturing Process: A Genetic Programming Approach
%A Al-Afeef, Alaa
%A Sheta, Alaa
%A Rabea, Adnan
%D 2011
%8 apr
%7 1
%I Lambert Academic Publishing
%F AfeefBook2011
%X 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.
%K genetic algorithms, genetic programming
%U https://www.morebooks.de/store/gb/book/image-reconstruction-of-a-manufacturing-process/isbn/978-3-8443-2569-0

%0 Journal Article
%T GADS and Reusability
%A Al-Bastaki, Y.
%A Awad, W.
%J Journal of Artificial Intelligence
%D 2010
%V 3
%N 2
%I Asian Network for Scientific Information
%@ 19945450
%G eng
%F Al-Bastaki:2010:JAI
%X 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.
%K genetic algorithms, genetic programming, GADS, reusability
%9 journal article
%U http://docsdrive.com/pdfs/ansinet/jai/2010/67-72.pdf
%P 67-77

%0 Conference Proceedings
%T An evolutionary computing approach for estimating global solar radiation
%A Al-Hajj, Rami
%A Assi, Ali
%A Batch, Farhan
%S 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA)
%D 2016
%8 20 23 nov
%C Birmingham, UK
%F Al-Hajj:2016:ICRERA
%X 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.
%K genetic algorithms, genetic programming, Decision support systems, Evolutionary computation, Hand-held computers, climatological data, evolutionary computation, global solar radiation
%R doi:10.1109/ICRERA.2016.7884553
%U http://dx.doi.org/doi:10.1109/ICRERA.2016.7884553
%P 285-290

%0 Conference Proceedings
%T Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%Y Palaiahnakote, Shivakumara
%Y di Baja, Gabriella Sanniti
%Y Wang, Liang
%Y Yan, Wei Qi
%S Pattern Recognition - 5th Asian Conference, ACPR 2019, Auckland, New Zealand, November 26-29, 2019, Revised Selected Papers, Part II
%S Lecture Notes in Computer Science
%D 2019
%V 12047
%I Springer
%F DBLP:conf/acpr/Al-HelaliCXZ19
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-030-41299-9_44
%U https://doi.org/10.1007/978-3-030-41299-9_44
%U http://dx.doi.org/doi:10.1007/978-3-030-41299-9_44
%P 566-579

%0 Conference Proceedings
%T Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%Y Liu, Jixue
%Y Bailey, James
%S AI 2019: Advances in Artificial Intelligence - 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2-5, 2019, Proceedings
%S Lecture Notes in Computer Science
%D 2019
%V 11919
%I Springer
%F DBLP:conf/ausai/Al-Helali00Z19
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-030-35288-2_42
%U https://doi.org/10.1007/978-3-030-35288-2_42
%U http://dx.doi.org/doi:10.1007/978-3-030-35288-2_42
%P 523-535

%0 Conference Proceedings
%T A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%S 2019 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2019
%8 dec
%F Al-Helali:2019:SSCI
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/SSCI44817.2019.9002861
%U http://dx.doi.org/doi:10.1109/SSCI44817.2019.9002861
%P 2395-2402

%0 Conference Proceedings
%T Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%Y Hu, Ting
%Y Lourenco, Nuno
%Y Medvet, Eric
%S EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming
%S LNCS
%D 2020
%8 15 17 apr
%V 12101
%I Springer Verlag
%C Seville, Spain
%F Al-Helali:2020:EuroGP
%X 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.
%K genetic algorithms, genetic programming, Symbolic regression, Incomplete data, Feature selection, Imputation, Model complexity
%R doi:10.1007/978-3-030-44094-7_1
%U http://dx.doi.org/doi:10.1007/978-3-030-44094-7_1
%P 1-17

%0 Conference Proceedings
%T Genetic Programming with Noise Sensitivity for Imputation Predictor Selection in Symbolic Regression with Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%Y Jin, Yaochu
%S 2020 IEEE Congress on Evolutionary Computation, CEC 2020
%D 2020
%8 19 24 jul
%I IEEE Press
%C internet
%F Al-Helali:2020:CEC
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC48606.2020.9185526
%U http://dx.doi.org/doi:10.1109/CEC48606.2020.9185526
%P paperid24344

%0 Conference Proceedings
%T Multi-Tree Genetic Programming-based Transformation for Transfer Learning in Symbolic Regression with Highly Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%Y Jin, Yaochu
%S 2020 IEEE Congress on Evolutionary Computation, CEC 2020
%D 2020
%8 19 24 jul
%I IEEE Press
%C internet
%F Al-Helali:2020:CEC2
%X 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 utilize 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC48606.2020.9185670
%U http://dx.doi.org/doi:10.1109/CEC48606.2020.9185670
%P paperid24250

%0 Conference Proceedings
%T Multi-Tree Genetic Programming for Feature Construction-Based Domain Adaptation in Symbolic Regression with Incomplete Data
%A Al-Helali, Baligh
%A Chen, Qi
%A Xue, Bing
%A Zhang, Mengjie
%Y Coello Coello, Carlos Artemio
%Y Aguirre, Arturo Hernandez
%Y Uribe, Josu Ceberio
%Y Fabre, Mario Garza
%Y Toscano Pulido, Gregorio
%Y Rodriguez-Vazquez, Katya
%Y Wanner, Elizabeth
%Y Veerapen, Nadarajen
%Y Montes, Efren Mezura
%Y Allmendinger, Richard
%Y Marin, Hugo Terashima
%Y Wagner, Markus
%Y Bartz-Beielstein, Thomas
%Y Filipic, Bogdan
%Y Trautmann, Heike
%Y Tang, Ke
%Y Koza, John
%Y Goodman, Erik
%Y Langdon, William B.
%Y Nicolau, Miguel
%Y Zarges, Christine
%Y Volz, Vanessa
%Y Tusar, Tea
%Y Naujoks, Boris
%Y Bosman, Peter A. N.
%Y Whitley, Darrell
%Y Solnon, Christine
%Y Helbig, Marde
%Y Doncieux, Stephane
%Y Wilson, Dennis G.
%Y Fernandez de Vega, Francisco
%Y Paquete, Luis
%Y Chicano, Francisco
%Y Xue, Bing
%Y Bacardit, Jaume
%Y Mostaghim, Sanaz
%Y Fieldsend, Jonathan
%Y Schuetze, Oliver
%Y Arnold, Dirk
%Y Ochoa, Gabriela
%Y Segura, Carlos
%Y Cotta, Carlos
%Y Emmerich, Michael
%Y Zhang, Mengjie
%Y Purshouse, Robin
%Y Ray, Tapabrata
%Y Petke, Justyna
%Y Ishikawa, Fuyuki
%Y Lengler, Johannes
%Y Neumann, Frank
%S Proceedings of the 2020 Genetic and Evolutionary Computation Conference
%S GECCO ’20
%D 2020
%8 jul 8 12
%I Association for Computing Machinery
%C internet
%F Al-Helali:2020:GECCO
%X 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.
%K genetic algorithms, genetic programming, transfer tearning, incomplete data, symbolic regression
%R doi:10.1145/3377930.3390160
%U https://doi.org/10.1145/3377930.3390160
%U http://dx.doi.org/doi:10.1145/3377930.3390160
%P 913-921

%0 Thesis
%T Itemset size-sensitive interestingness measures for association rule mining and link prediction
%A Aljandal, Waleed A.
%D 2009
%8 may
%C Manhattan, Kansas, USA
%C Department of Computing and Information Sciences, Kansas State University
%F WaleedAljandal2009
%X 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.
%K genetic algorithms, data Mining, Association Rule, Interestingness Measures, Link Prediction
%9 Ph.D. thesis
%U https://krex.k-state.edu/dspace/handle/2097/1245

%0 Journal Article
%T Thunderstorms Prediction using Genetic Programming
%A Al-Jundi, Ruba
%A Yasen, Mais
%A Al-Madi, Nailah
%J International Journal of Information Systems and Computer Sciences
%D 2018
%V 7
%N 1
%I WARSE
%@ 2319-7595
%F ThunderStormGP
%O Special Issue of ICSIC 2017, Held during 23-24 September 2017 in Amman Arab University, Amman, Jordan
%X 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.
%K genetic algorithms, genetic programming, Evolutionary computation, Machine Learning, Weather Prediction.
%9 journal article
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Thunderstorm_Prediction.pdf

%0 Conference Proceedings
%T Adaptive genetic programming applied to classification in data mining
%A Al-Madi, N.
%A Ludwig, S. A.
%S Proceedings of the Fourth World Congress on Nature and Biologically Inspired Computing, NaBIC 2012
%D 2012
%F Al-Madi:2012:NaBIC
%X 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.
%K 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
%R doi:10.1109/NaBIC.2012.6402243
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Adaptive_Genetic_Programming_applied_to_Classification_in_Data_Mining.pdf
%U http://dx.doi.org/doi:10.1109/NaBIC.2012.6402243
%P 79-85

%0 Conference Proceedings
%T Improving genetic programming classification for binary and multiclass datasets
%A Al-Madi, Nailah
%A Ludwig, Simone A.
%Y Hammer, Barbara
%Y Zhou, Zhi-Hua
%Y Wang, Lipo
%Y Chawla, Nitesh
%S IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013
%D 2013
%8 16 19 apr
%C Singapore
%F Al-Madi:2013:SSCI
%X 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.
%K genetic algorithms, genetic programming, Evolutionary Computation, Classification, Multiclass, Binary Classification
%R doi:10.1109/CIDM.2013.6597232
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/improving_GP.pdf
%U http://dx.doi.org/doi:10.1109/CIDM.2013.6597232
%P 166-173

%0 Conference Proceedings
%T Segment-based genetic programming
%A Al-Madi, Nailah
%A Ludwig, Simone A.
%Y Blum, Christian
%Y Alba, Enrique
%Y Bartz-Beielstein, Thomas
%Y Loiacono, Daniele
%Y Luna, Francisco
%Y Mehnen, Joern
%Y Ochoa, Gabriela
%Y Preuss, Mike
%Y Tantar, Emilia
%Y Vanneschi, Leonardo
%Y McClymont, Kent
%Y Keedwell, Ed
%Y Hart, Emma
%Y Sim, Kevin
%Y Gustafson, Steven
%Y Vladislavleva, Ekaterina
%Y Auger, Anne
%Y Bischl, Bernd
%Y Brockhoff, Dimo
%Y Hansen, Nikolaus
%Y Mersmann, Olaf
%Y Posik, Petr
%Y Trautmann, Heike
%Y Iqbal, Muhammad
%Y Shafi, Kamran
%Y Urbanowicz, Ryan
%Y Wagner, Stefan
%Y Affenzeller, Michael
%Y Walker, David
%Y Everson, Richard
%Y Fieldsend, Jonathan
%Y Stonedahl, Forrest
%Y Rand, William
%Y Smith, Stephen L.
%Y Cagnoni, Stefano
%Y Patton, Robert M.
%Y Pappa, Gisele L.
%Y Woodward, John
%Y Swan, Jerry
%Y Krawiec, Krzysztof
%Y Tantar, Alexandru-Adrian
%Y Bosman, Peter A. N.
%Y Vega-Rodriguez, Miguel
%Y Chaves-Gonzalez, Jose M.
%Y Gonzalez-Alvarez, David L.
%Y Santander-Jimenez, Sergio
%Y Spector, Lee
%Y Keijzer, Maarten
%Y Holladay, Kenneth
%Y Tusar, Tea
%Y Naujoks, Boris
%S GECCO ’13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion
%D 2013
%8 June 10 jul
%I ACM
%C Amsterdam, The Netherlands
%F AL-Madi:2013:GECCOcomp
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/2464576.2464648
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Segment-Based_Genetic_Programming.pdf
%U http://dx.doi.org/doi:10.1145/2464576.2464648
%P 133-134

%0 Conference Proceedings
%T Scaling Genetic Programming for Data Classification using MapReduce Methodology
%A Al-Madi, Nailah
%A Ludwig, Simone A.
%Y Ludwig, Simone
%Y Melin, Patricia
%Y Abraham, Ajith
%Y Madureira, Ana Maria
%Y Nygard, Kendall
%Y Castillo, Oscar
%Y Muda, Azah Kamilah
%Y Ma, Kun
%Y Corchado, Emilio
%S 5th World Congress on Nature and Biologically Inspired Computing
%D 2013
%8 December 14 aug
%I IEEE
%C Fargo, USA
%F Al-Madi:2013:nabic
%X 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
%K genetic algorithms, genetic programming, Evolutionary computation, data classification, Parallel Processing, MapReduce, Hadoop
%R doi:10.1109/NaBIC.2013.6617851
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/MRGP.pdf
%U http://dx.doi.org/doi:10.1109/NaBIC.2013.6617851
%P 132-139

%0 Thesis
%T Improved genetic programming techniques for data classification
%A Al-Madi, Nailah Shikri
%D 2013
%8 dec
%C Fargo, North Dakota, USA
%C Computer Science, North Dakota State University
%F Al-Madi:thesis
%X 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.
%K genetic algorithms, genetic programming, Artificial intelligence, Computer science, Applied sciences, Data classification, Data mining, MRGP
%9 Ph.D. thesis
%U https://library.ndsu.edu/ir/handle/10365/27097

%0 Journal Article
%T Mike Preuss: Multimodal optimization by means of evolutionary algorithms
%A Al-Madi, Nailah
%J Genetic Programming and Evolvable Machines
%D 2016
%8 sep
%V 17
%N 3
%@ 1389-2576
%F Al-Madi:2016:GPEM
%O Book review
%K genetic algorithms
%9 journal article
%R doi:10.1007/s10710-016-9272-x
%U http://dx.doi.org/doi:10.1007/s10710-016-9272-x
%P 315-316

%0 Journal Article
%T Genetic Programming Approach to Hierarchical Production Rule Discovery
%A Al-Maqaleh, Basheer M.
%A Bharadwaj, Kamal K.
%J International Science Index
%D 2007
%V 1
%N 11
%I World Academy of Science, Engineering and Technology
%@ 1307-6892
%G en
%F Al-Maqaleh:2007:isi
%X 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.
%K genetic algorithms, genetic programming, hierarchy, knowledge discovery in database, subsumption matrix. k
%9 journal article
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.1481
%P 531-534

%0 Conference Proceedings
%T Genetic Algorithm Approach to Automated Discovery of Comprehensible Production Rules
%A Al-Maqaleh, Basheer Mohamad Ahmad
%S Second International Conference on Advanced Computing Communication Technologies (ACCT 2012)
%D 2012
%8 jan
%F Al-Maqaleh:2012:ACCT
%X 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.
%K 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
%R doi:10.1109/ACCT.2012.57
%U http://dx.doi.org/doi:10.1109/ACCT.2012.57
%P 69-71

%0 Journal Article
%T A New Software Reliability Growth Model: Genetic-Programming-Based Approach
%A Al-Rahamneh, Zainab
%A Reyalat, Mohammad
%A Sheta, Alaa F.
%A Bani-Ahmad, Sulieman
%A Al-Oqeili, Saleh
%J Journal of Software Engineering and Applications
%D 2011
%8 aug
%V 4
%N 8
%I Scientific Research Publishing
%@ 19453116
%G eng
%F Al-Rahamneh:2011:JSEA
%X 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.
%K genetic algorithms, genetic programming, SBSE, software reliability, modelling, software faults
%9 journal article
%R doi:10.4236/jsea.2011.48054
%U http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jsea.2011.48054
%U http://dx.doi.org/doi:10.4236/jsea.2011.48054
%P 476-481

%0 Conference Proceedings
%T Hybrid Multi-Agent Architecture (HMAA) for meeting scheduling
%A Al-Ratrout, Serein
%A Siewe, Francois
%A Al-Dabbas, Omar
%A Al-Fawair, Mai
%S 2010 7th International Multi- Conference on Systems, Signals and Devices
%D 2010
%8 27 30 jun
%I IEEE
%C Amman, Jordan
%G en
%F Al-Ratrout:2010:SSD
%X 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.
%K genetic algorithms, genetic programming, multiagent, meeting scheduling, heuristic
%R doi:10.1109/SSD.2010.5585505
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.3891
%U http://dx.doi.org/doi:10.1109/SSD.2010.5585505

%0 Conference Proceedings
%T A genetic programming approach to feature selection and construction for ransomware, phishing and spam detection
%A Al-Sahaf, Harith.
%A Welch, Ian
%Y Allmendinger, Richard
%Y Cotta, Carlos
%Y Doerr, Carola
%Y Oliveto, Pietro S.
%Y Weise, Thomas
%Y Zamuda, Ales
%Y Auger, Anne
%Y Brockhoff, Dimo
%Y Hansen, Nikolaus
%Y Tusar, Tea
%Y Varelas, Konstantinos
%Y Camacho-Fernandez, David
%Y Vasile, Massimiliano
%Y Riccardi, Annalisa
%Y Derbel, Bilel
%Y Li, Ke
%Y Li, Xiaodong
%Y Zapotecas, Saul
%Y Zhang, Qingfu
%Y Akman, Ozgur
%Y Alyahya, Khulood
%Y Branke, Juergen
%Y Fieldsend, Jonathan
%Y Chugh, Tinkle
%Y Hakanen, Jussi
%Y Ceberio Uribe, Josu
%Y Santucci, Valentino
%Y Baioletti, Marco
%Y McCall, John
%Y Hart, Emma
%Y Tauritz, Daniel R.
%Y Woodward, John R.
%Y Nakayama, Koichi
%Y Oshima, Chika
%Y Wagner, Stefan
%Y Affenzeller, Michael
%Y Osaba, Eneko
%Y Del Ser, Javier
%Y Kerschke, Pascal
%Y Naujoks, Boris
%Y Volz, Vanessa
%Y Esparcia-Alcazar, Anna I.
%Y Alshammari, Riyad
%Y Hemberg, Erik
%Y Makanju, Tokunbo
%Y Alexander, Brad
%Y Haraldsson, Saemundur O.
%Y Wagner, Markus
%Y Fernandez Alzueta, Silvino
%Y Valledor Pellicer, Pablo
%Y Stuetzle, Thomas
%Y Walker, David
%Y Johns, Matt
%Y Ross, Nick
%Y Keedwell, Ed
%Y Nakata, Masaya
%Y Stein, Anthony
%Y Tatsumi, Takato
%Y Veerapen, Nadarajen
%Y Liefooghe, Arnaud
%Y Verel, Sebastien
%Y Ochoa, Gabriela
%Y Smith, Stephen
%Y Cagnoni, Stefano
%Y Patton, Robert M.
%Y La Cava, William
%Y Olson, Randal
%Y Orzechowski, Patryk
%Y Urbanowicz, Ryan
%Y Oyama, Akira
%Y Shimoyama, Koji
%Y Singh, Hemant Kumar
%Y Chiba, Kazuhisa
%Y Palar, Pramudita Satria
%Y Rahat, Alma
%Y Everson, Richard
%Y Wang, Handing
%Y Jin, Yaochu
%Y Gallagher, Marcus
%Y Preuss, Mike
%Y Teytaud, Olivier
%Y Lezama, Fernando
%Y Soares, Joao
%Y Vale, Zita
%S GECCO ’19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
%D 2019
%8 13 17 jul
%I ACM
%C Prague, Czech Republic
%F Al-Sahaf:2019:GECCOcomp
%K genetic algorithms, genetic programming
%R doi:10.1145/3319619.3322083
%U http://dx.doi.org/doi:10.1145/3319619.3322083
%P 332-333

%0 Journal Article
%T A survey on evolutionary machine learning
%A Al-Sahaf, Harith
%A Bi, Ying
%A Chen, Qi
%A Lensen, Andrew
%A Mei, Yi
%A Sun, Yanan
%A Tran, Binh
%A Xue, Bing
%A Zhang, Mengjie
%J Journal of the Royal Society of New Zealand
%D 2019
%V 49
%N 2
%I Taylor & Francis
%F Al-Sahaf:2019:JRSNZ
%O The 2019 Annual Collection of Reviews
%X 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.
%K genetic algorithms, genetic programming, AI, ANN, EML, GPU, EMO, autoML, TPOT, artificial intelligence, machine learning, evolutionary computation, classification, regression, clustering, combinatorial optimisation, deep learning, transfer learning, ensemble learning
%9 journal article
%R doi:10.1080/03036758.2019.1609052
%U https://doi.org/10.1080/03036758.2019.1609052
%U http://dx.doi.org/doi:10.1080/03036758.2019.1609052
%P 205-228

%0 Conference Proceedings
%T The Influence of Input Data Standardization Methods on the Prediction Accuracy of Genetic Programming Generated Classifiers
%A Al Shorman, Amaal R.
%A Faris, Hossam
%A Castillo, Pedro A.
%A Guervos, Juan Julian Merelo
%A Al-Madi, Nailah
%Y Sabourin, Christophe
%Y Guervos, Juan Julian Merelo
%Y Linares-Barranco, Alejandro
%Y Madani, Kurosh
%Y Warwick, Kevin
%S Proceedings of the 10th International Joint Conference on Computational Intelligence, IJCCI 2018, Seville, Spain, September 18-20, 2018
%D 2018
%I SciTePress
%F DBLP:conf/ijcci/ShormanFCGA18
%K genetic algorithms, genetic programming
%R doi:10.5220/0006959000790085
%U https://doi.org/10.5220/0006959000790085
%U http://dx.doi.org/doi:10.5220/0006959000790085
%P 79-85

%0 Conference Proceedings
%T Intrinsic Evolution of Large Digital Circuits Using a Modular Approach
%A Alagesan, Shri Vidhya
%A Kannan, Sruthi
%A Shanthi, G.
%A Shanthi, A. P.
%A Parthasarathi, Ranjani
%S NASA/ESA Conference on Adaptive Hardware and Systems, AHS ’08
%D 2008
%8 jun
%F Alagesan:2008:AHS
%X 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.
%K 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
%R doi:10.1109/AHS.2008.52
%U http://dx.doi.org/doi:10.1109/AHS.2008.52
%P 19-26

%0 Report
%T An Indexed Bibliography of Genetic Programming
%A Alander, Jarmo T.
%D 1995
%N 94-1-GP
%I Department of Information Technology and Industrial Management, University of Vaasa
%C Finland
%F Alander:1995:ibGP
%X 220 references. Indexed by subject, publication type and author
%K genetic algorithms, genetic programming
%9 Report Series no
%U ftp://ftp.uwasa.fi/cs/report94-1/gaGPbib.ps.Z

%0 Book
%T An Indexed Bibliography of Genetic Algorithms: Years 1957–1993
%A Alander, Jarmo T.
%D 1994
%I Art of CAD ltd
%C Vaasa, Finland
%F Alander:1994:bib
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.4481&rep=rep1&type=pdf

%0 Conference Proceedings
%T 2nd order equation
%A Alander, Jarmo T.
%A Moghadampour, Ghodrat
%A Ylinen, Jari
%Y Alander, Jarmo T.
%S Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)
%S Proceedings of the University of Vaasa, Nro. 13
%D 1996
%8 19. 23.  aug
%I University of Vaasa
%C Vaasa (Finland)
%F ga96fAlander
%X In this work we have tried to use genetic programming to solve the simple second order equation
%K genetic algorithms, genetic programming, mathematics, algebra
%U ftp://ftp.uwasa.fi/cs/2NWGA/Ghodrat2.ps.Z
%P 215-218

%0 Conference Proceedings
%T Hybrid evolutionary designer of modular robots
%A Alattas, R.
%S 2016 Annual Connecticut Conference on Industrial Electronics, Technology Automation (CT-IETA)
%D 2016
%8 oct
%F Alattas:2016:CT-IETA
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CT-IETA.2016.7868256
%U http://dx.doi.org/doi:10.1109/CT-IETA.2016.7868256

%0 Conference Proceedings
%T Soft Computing Based Approaches for High Performance Concrete
%A Alavi, A. H.
%A Heshmati, A. A.
%A Salehzadeh, H.
%A Gandomi, A. H.
%A Askarinejad, A.
%Y Papadrakakis, M.
%Y Topping, B. H. V.
%S Proceedings of the Sixth International Conference on Engineering Computational Technology
%S Civil-Comp Proceedings
%D 2008
%8 February 5 sep
%V 89
%I Civil-Comp Press
%C Athens
%F Alavi:2008:ICECT
%X 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.
%K genetic algorithms, genetic programming, linear genetic programming, high performance concrete, multilayer perceptron, compressive strength, workability, mix design
%R doi:10.4203/ccp.89.86
%U http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3
%U http://dx.doi.org/doi:10.4203/ccp.89.86
%P Paper86

%0 Conference Proceedings
%T Utilisation of Computational Intelligence Techniques for Stabilised Soil
%A Alavi, A. H.
%A Heshmati, A. A.
%A Gandomi, A. H.
%A Askarinejad, A.
%A Mirjalili, M.
%Y Papadrakakis, M.
%Y Topping, B. H. V.
%S Proceedings of the Sixth International Conference on Engineering Computational Technology
%S Civil-Comp Proceedings
%D 2008
%8 February 5 sep
%V 89
%I Civil-Comp Press
%C Athens
%F Alavi:2008:ICECT2
%X 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.
%K genetic algorithms, genetic programming, linear genetic programming, stabilised soil, multilayer perceptron, textural properties of soil, cement, lime, asphalt, unconfined compressive strength
%R doi:10.4203/ccp.89.175
%U http://www.civil-comp.com/pubs/catalog.htm?t=contents&f=26_3
%U http://dx.doi.org/doi:10.4203/ccp.89.175
%P Paper175

%0 Journal Article
%T Comment on ’Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological Processes 22: 623-628’
%A Alavi, A. H.
%A Gandomi, A. H.
%A Gandomi, M.
%J Hydrological Processes
%D 2010
%8 15 mar
%V 24
%N 6
%I John Wiley & Sons, Ltd.
%@ 1099-1085
%F Alavi:2010:HP
%K genetic algorithms, genetic programming, AIMGP, Discipulus
%9 journal article
%R doi:10.1002/hyp.7511
%U http://onlinelibrary.wiley.com/doi/10.1002/hyp.7511/abstract
%U http://dx.doi.org/doi:10.1002/hyp.7511
%P 798-799

%0 Journal Article
%T Multi Expression Programming: A New Approach to Formulation of Soil Classification
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%A Sahab, Mohammad Ghasem
%A Gandomi, Mostafa
%J Engineering with Computers
%D 2010
%8 apr
%V 26
%N 2
%F Alavi:2010:EwC
%X 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.
%K genetic algorithms, genetic programming, Multi expression programming, Soil classification, Formulation
%9 journal article
%R doi:10.1007/s00366-009-0140-7
%U http://dx.doi.org/doi:10.1007/s00366-009-0140-7
%P 111-118

%0 Journal Article
%T High-Precision Modeling of Uplift Capacity of Suction Caissons Using a Hybrid Computational Method
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%A Mousavi, Mehdi
%A Mollahasani, Ali
%J Geomechanics and Engineering
%D 2010
%8 dec
%V 2
%N 4
%F Alavi:2010:GeoMechEng
%X 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.
%K genetic algorithms, genetic programming, suction caissons, uplift capacity, simulated annealing, nonlinear modelling
%9 journal article
%R doi:10.12989/gae.2010.2.4.253
%U http://technopress.kaist.ac.kr/?page=container&journal=gae&volume=2&num=4
%U http://dx.doi.org/doi:10.12989/gae.2010.2.4.253
%P 253-280

%0 Journal Article
%T A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%J International Journal of Computer Aided Methods in Engineering-Engineering Computations
%D 2011
%V 28
%N 3
%@ 0264-4401
%F Alavi:2010:ijcamieec
%X 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.
%K 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
%9 journal article
%R doi:10.1108/02644401111118132
%U http://www.emeraldinsight.com/journals.htm?articleid=1912293
%U http://dx.doi.org/doi:10.1108/02644401111118132
%P 242-274

%0 Conference Proceedings
%T Nonlinear Modeling of Liquefaction Behavior of Sand-Silt Mixtures in terms of Strain Energy
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%Y Scinteie, Rodian
%Y Plescan, Costel
%S Proceedings of the 8th International Symposium on Highway and Bridge Engineering, Technology and Innovation in Transportation Infrastructure, 2010
%D 2010
%8 October
%C Iasi, Romania
%F Alavi:2010:HBE
%K genetic algorithms, genetic programming, GPLAB, Discipulus, simulated annealing, capacity energy, Matlab
%U http://www.intersections.ro/Conferences/HBE2010.pdf
%P 50-69

%0 Journal Article
%T Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method
%A Alavi, Amir Hossein
%A Ameri, Mahmoud
%A Gandomi, Amir Hossein
%A Mirzahosseini, Mohammad Reza
%J Construction and Building Materials
%D 2011
%8 mar
%V 25
%N 3
%@ 0950-0618
%F Alavi:2010:CBM
%X 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.
%K genetic algorithms, genetic programming, Asphalt concrete mixture, Flow number, Simulated annealing, Marshall mix design, Regression analysis
%9 journal article
%R doi:10.1016/j.conbuildmat.2010.09.010
%U http://dx.doi.org/doi:10.1016/j.conbuildmat.2010.09.010
%P 1338-1355

%0 Journal Article
%T Discussion on ’Soft computing approach for real-time estimation of missing wave heights’ by S.N. Londhe [Ocean Engineering 35 (2008) 1080-1089]
%A Alavi, A. H.
%A Gandomi, A. H.
%A Heshmati, A. A. R.
%J Ocean Engineering
%D 2010
%8 sep
%V 37
%N 13
%@ 0029-8018
%F Alavi20101239
%X The paper studied by Londhe (2008) \citeLondhe20081080 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.
%K genetic algorithms, genetic programming, Linear genetic programming, Tree structure, Wave forecasts
%9 journal article
%R doi:10.1016/j.oceaneng.2010.06.003
%U http://www.sciencedirect.com/science/article/B6V4F-50DXD90-1/2/b2489a1aebf49e771abca1b27d3b24b4
%U http://dx.doi.org/doi:10.1016/j.oceaneng.2010.06.003
%P 1239-1240

%0 Journal Article
%T Genetic-based modeling of uplift capacity of suction caissons
%A Alavi, Amir Hossein
%A Aminian, Pejman
%A Gandomi, Amir Hossein
%A Arab Esmaeili, Milad
%J Expert Systems with Applications
%D 2011
%8 15 sep
%V 38
%N 10
%@ 0957-4174
%F Alavi2011
%X 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.
%K genetic algorithms, genetic programming, Gene expression programming, Suction caissons, Uplift capacity, Formulation
%9 journal article
%R doi:10.1016/j.eswa.2011.04.049
%U http://www.sciencedirect.com/science/article/pii/S0957417411005653
%U http://dx.doi.org/doi:10.1016/j.eswa.2011.04.049
%P 12608-12618

%0 Journal Article
%T New Ground-Motion Prediction Equations Using Multi Expression Programing
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%A Modaresnezhad, Minoo
%A Mousavi, Mehdi
%J Journal of Earthquake Engineering
%D 2011
%V 15
%N 4
%@ 1363-2469
%F Alavi:2011:JEQE
%X 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.
%K genetic algorithms, genetic programming, Multi-Expression Programming, Time-Domain Ground-Motion Parameters, Attenuation Relationship, Nonlinear Modelling
%9 journal article
%R doi:10.1080/13632469.2010.526752
%U http://www.tandfonline.com/doi/abs/10.1080/13632469.2010.526752#.UlMR6NKc_G0
%U http://dx.doi.org/doi:10.1080/13632469.2010.526752
%P 511-536

%0 Journal Article
%T Energy-based numerical models for assessment of soil liquefaction
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%J Geoscience Frontiers
%D 2012
%V 3
%N 4
%@ 1674-9871
%F Alavi2012541
%X 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.
%K genetic algorithms, genetic programming, Soil liquefaction, Capacity energy, Multi expression programming, Sand, Formulation
%9 journal article
%R doi:10.1016/j.gsf.2011.12.008
%U http://www.sciencedirect.com/science/article/pii/S167498711100137X
%U http://dx.doi.org/doi:10.1016/j.gsf.2011.12.008
%P 541-555

%0 Book Section
%T A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%A Mollahasani, Ali
%E Chiong, Raymond
%E Weise, Thomas
%E Michalewicz, Zbigniew
%B Variants of Evolutionary Algorithms for Real-World Applications
%D 2012
%I Springer
%F books/sp/chiong2012/AlaviGM12
%X 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.
%K genetic algorithms, genetic programming, Chemical stabilisation, Simulated annealing, Nonlinear modelling
%R doi:10.1007/978-3-642-23424-8_11
%U http://dx.doi.org/doi:10.1007/978-3-642-23424-8_11
%P 343-376

%0 Book Section
%T Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%A Mollahasani, Ali
%A Bolouri Bazaz, Jafar
%E Yang, Xin-She
%E Gandomi, Amir Hossein
%E Talatahari, Siamak
%E Alavi, Amir Hossein
%B Metaheuristics in Water, Geotechnical and Transport Engineering
%D 2013
%I Elsevier
%C Oxford
%F Alavi:2013:MWGTE
%X 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
%K genetic algorithms, genetic programming, Tree-based genetic programming, linear genetic programming, geotechnical engineering, prediction
%R doi:10.1016/B978-0-12-398296-4.00012-X
%U http://www.sciencedirect.com/science/article/pii/B978012398296400012X
%U http://dx.doi.org/doi:10.1016/B978-0-12-398296-4.00012-X
%P 289-310

%0 Journal Article
%T Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems
%A Alavi, Amir Hossein
%A Gandomi, Amir Hossein
%A Chahkandi Nejad, Hadi
%A Mollahasani, Ali
%A Rashed, Azadeh
%J Neural Computing and Applications
%D 2013
%8 nov
%V 23
%N 6
%I Springer-Verlag
%@ 0941-0643
%G English
%F Alavi:2014:NCA
%X 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.
%K genetic algorithms, genetic programming, gene expression programming, Soil deformation modulus, Expression programming techniques, Pressure meter test, Soil physical properties
%9 journal article
%R doi:10.1007/s00521-012-1144-6
%U http://link.springer.com/article/10.1007%2Fs00521-012-1144-6
%U http://dx.doi.org/doi:10.1007/s00521-012-1144-6
%P 1771-1786

%0 Journal Article
%T New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses
%A Alavi, Amir H.
%A Sadrossadat, Ehsan
%J Geoscience Frontiers
%D 2014
%@ 1674-9871
%F Alavi:2014:GF
%X 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.
%K genetic algorithms, genetic programming, Rock mass properties, Ultimate bearing capacity, Shallow foundation, Prediction, Evolutionary computation
%9 journal article
%R doi:10.1016/j.gsf.2014.12.005
%U http://www.sciencedirect.com/science/article/pii/S1674987114001625
%U http://dx.doi.org/doi:10.1016/j.gsf.2014.12.005

%0 Journal Article
%T Progress of machine learning in geosciences: Preface
%A Alavi, Amir H.
%A Gandomi, Amir H.
%A Lary, David J.
%J Geoscience Frontiers
%D 2016
%V 7
%N 1
%@ 1674-9871
%F Alavi:2016:GSF
%O Editorial
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1016/j.gsf.2015.10.006
%U http://www.sciencedirect.com/science/article/pii/S1674987115001243
%U http://dx.doi.org/doi:10.1016/j.gsf.2015.10.006
%P 1-2

%0 Journal Article
%T A new approach for modeling of flow number of asphalt mixtures
%A Alavi, Amir H.
%A Hasni, Hassene
%A Zaabar, Imen
%A Lajnef, Nizar
%J Archives of Civil and Mechanical Engineering
%D 2017
%V 17
%N 2
%@ 1644-9665
%F Alavi:2017:ACME
%X 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.
%K genetic algorithms, genetic programming, Asphalt mixture, Flow number, Marshall mix design
%9 journal article
%R doi:10.1016/j.acme.2016.06.004
%U http://www.sciencedirect.com/science/article/pii/S1644966516300814
%U http://dx.doi.org/doi:10.1016/j.acme.2016.06.004
%P 326-335

%0 Conference Proceedings
%T Type-Constrained Genetic Programming for Rule-Base Definition in Fuzzy Logic Controllers
%A Alba, Enrique
%A Cotta, Carlos
%A Troya, Jose M.
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F alba:1996:tGPrdflc
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap31.pdf
%P 255-260

%0 Conference Proceedings
%T Entropic and Real-Time Analysis of the Search with Panmictic, Structured, and Parallel Distributed Genetic Algorithms
%A Alba, Enrique
%A Cotta, Carlos
%A Troya, Jose M.
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 1
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F alba:1999:ERASPSPDGA
%K genetic algorithms and classifier systems, poster papers
%U http://gpbib.cs.ucl.ac.uk/gecco1999/Ga-808.pdf
%P 773

%0 Conference Proceedings
%T Tackling epistasis with panmictic and structured genetic algorithms
%A Alba, Enrique
%A Troya, Jose M.
%Y Brave, Scott
%Y Wu, Annie S.
%S Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%D 1999
%8 13 jul
%C Orlando, Florida, USA
%F alba:1999:T
%K Genetic Algorithms, NK
%P 1-7

%0 Journal Article
%T Evolutionary Design of Fuzzy Logic Controllers Using Strongly-Typed GP
%A Alba, Enrique
%A Cotta, Carlos
%A Troya, Jose M.
%J Mathware & Soft Computing
%D 1999
%V 6
%N 1
%F alba:1999:edflcSGP
%X 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.
%K genetic algorithms, genetic programming, Type System, Fuzzy Logic Controller, Cart-Centering Problem
%9 journal article
%U http://docto-si.ugr.es/Mathware/v6n1/PS/7-alba.ps.gz
%P 109-124

%0 Book
%T Parallel Metaheuristics: A New Class of Algorithms
%A Alba, Enrique
%D 2005
%8 aug
%I John Wiley & Sons
%C NJ, USA
%@ 0-471-67806-6
%F Alba05
%X 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.
%K genetic algorithms, genetic programming, book, text, general computer engineering
%U https://www.amazon.com/Parallel-Metaheuristics-New-Class-Algorithms/dp/0471678066/ref=sr_1_1

%0 Conference Proceedings
%T Learning to Combine Spectral Indices with Genetic Programming
%A Hernandez Albarracin, Juan Felipe
%A dos Santos, Jefersson Alex
%A da S. Torres, Ricardo
%S 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
%D 2016
%8 oct
%F Albarracin:2016:SIBGRAPI
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/SIBGRAPI.2016.063
%U http://dx.doi.org/doi:10.1109/SIBGRAPI.2016.063
%P 408-415

%0 Conference Proceedings
%T A Study of Semantic Geometric Crossover Operators in Regression Problems
%A Albinati, Julio
%A Pappa, Gisele L.
%A Otero, Fernando E. B.
%A Oliveira, Luiz Otavio V. B.
%Y Johnson, Colin
%Y Krawiec, Krzysztof
%Y Moraglio, Alberto
%Y O’Neill, Michael
%S Semantic Methods in Genetic Programming
%D 2014
%8 13 sep
%C Ljubljana, Slovenia
%F Albinati:2014:SMGP
%O Workshop at Parallel Problem Solving from Nature 2014 conference
%K genetic algorithms, genetic programming
%U http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Albinati.pdf

%0 Conference Proceedings
%T The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems
%A Albinati, Julio
%A Pappa, Gisele L.
%A Otero, Fernando E. B.
%A Oliveira, Luiz Otavio V. B.
%Y Machado, Penousal
%Y Heywood, Malcolm I.
%Y McDermott, James
%Y Castelli, Mauro
%Y Garcia-Sanchez, Pablo
%Y Burelli, Paolo
%Y Risi, Sebastian
%Y Sim, Kevin
%S 18th European Conference on Genetic Programming
%S LNCS
%D 2015
%8 August 10 apr
%V 9025
%I Springer
%C Copenhagen
%F Albinati:2015:EuroGP
%X 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.
%K genetic algorithms, genetic programming, Crossover, Crossover mask optimisation
%R doi:10.1007/978-3-319-16501-1
%U http://dx.doi.org/doi:10.1007/978-3-319-16501-1
%P 3-15

%0 Book Section
%T Evolutionary Computation and Parallel Processing Applied to the Design of Multilayer Perceptrons
%A Albuquerque, Ana Claudia M. L.
%A Melo, Jorge D.
%A Doria Neto, Adriao D.
%E Nedjah, Nadia
%E de Macedo Mourelle, Luiza
%B Evolvable Machines: Theory & Practice
%S Studies in Fuzziness and Soft Computing
%D 2004
%V 161
%I Springer
%C Berlin
%@ 3-540-22905-1
%F Albuquerque:2004:EMTP
%K genetic algorithms
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%P 181-203

%0 Conference Proceedings
%T On the Impact of the Representation on Fitness Landscapes
%A Albuquerque, Paul
%A Chopard, Bastien
%A Mazza, Christian
%A Tomassini, Marco
%Y Poli, Riccardo
%Y Banzhaf, Wolfgang
%Y Langdon, William B.
%Y Miller, Julian F.
%Y Nordin, Peter
%Y Fogarty, Terence C.
%S Genetic Programming, Proceedings of EuroGP’2000
%S LNCS
%D 2000
%8 15 16 apr
%V 1802
%I Springer-Verlag
%C Edinburgh
%@ 3-540-67339-3
%F albuquerque:2000:irfl
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-46239-2_1
%U http://dx.doi.org/doi:10.1007/978-3-540-46239-2_1
%P 1-15

%0 Conference Proceedings
%T Lightweight Symbolic Regression with the Interaction-Transformation Representation
%A Aldeia, Guilherme
%A de Franca, Fabricio
%Y Vellasco, Marley
%S 2018 IEEE Congress on Evolutionary Computation (CEC)
%D 2018
%8 August 13 jul
%I IEEE
%C Rio de Janeiro, Brazil
%F Aldeia:2018:CEC
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2018.8477951
%U http://dx.doi.org/doi:10.1109/CEC.2018.8477951

%0 Conference Proceedings
%T A Parametric Study of Interaction-Transformation Evolutionary Algorithm for Symbolic Regression
%A Aldeia, Guilherme
%A de Franca, Fabricio
%Y Jin, Yaochu
%S 2020 IEEE Congress on Evolutionary Computation, CEC 2020
%D 2020
%8 19 24 jul
%I IEEE Press
%C internet
%F Aldeia:2020:CEC
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC48606.2020.9185521
%U http://dx.doi.org/doi:10.1109/CEC48606.2020.9185521
%P paperid24027

%0 Book Section
%T Toward a Technique for Cooperative Network Design Using Evolutionary Methods
%A Alderson, David
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%D 1999
%8 15 mar
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F alderson:1999:TTCNDUEM
%K genetic algorithms
%P 1-10

%0 Conference Proceedings
%T A new framework for scalable genetic programming
%A Aleb, Nassima
%A Kechid, Samir
%Y Gustafson, Steven
%Y Vladislavleva, Ekaterina
%S GECCO 2012 Symbolic regression and modeling workshop
%D 2012
%8 July 11 jul
%I ACM
%C Philadelphia, Pennsylvania, USA
%F Aleb:2012:GECCOcomp
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/2330784.2330859
%U http://dx.doi.org/doi:10.1145/2330784.2330859
%P 487-492

%0 Conference Proceedings
%T An interpolation based crossover operator for genetic programming
%A Aleb, Nassima
%A Kechid, Samir
%Y Blum, Christian
%Y Alba, Enrique
%Y Bartz-Beielstein, Thomas
%Y Loiacono, Daniele
%Y Luna, Francisco
%Y Mehnen, Joern
%Y Ochoa, Gabriela
%Y Preuss, Mike
%Y Tantar, Emilia
%Y Vanneschi, Leonardo
%Y McClymont, Kent
%Y Keedwell, Ed
%Y Hart, Emma
%Y Sim, Kevin
%Y Gustafson, Steven
%Y Vladislavleva, Ekaterina
%Y Auger, Anne
%Y Bischl, Bernd
%Y Brockhoff, Dimo
%Y Hansen, Nikolaus
%Y Mersmann, Olaf
%Y Posik, Petr
%Y Trautmann, Heike
%Y Iqbal, Muhammad
%Y Shafi, Kamran
%Y Urbanowicz, Ryan
%Y Wagner, Stefan
%Y Affenzeller, Michael
%Y Walker, David
%Y Everson, Richard
%Y Fieldsend, Jonathan
%Y Stonedahl, Forrest
%Y Rand, William
%Y Smith, Stephen L.
%Y Cagnoni, Stefano
%Y Patton, Robert M.
%Y Pappa, Gisele L.
%Y Woodward, John
%Y Swan, Jerry
%Y Krawiec, Krzysztof
%Y Tantar, Alexandru-Adrian
%Y Bosman, Peter A. N.
%Y Vega-Rodriguez, Miguel
%Y Chaves-Gonzalez, Jose M.
%Y Gonzalez-Alvarez, David L.
%Y Santander-Jimenez, Sergio
%Y Spector, Lee
%Y Keijzer, Maarten
%Y Holladay, Kenneth
%Y Tusar, Tea
%Y Naujoks, Boris
%S GECCO ’13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion
%D 2013
%8 June 10 jul
%I ACM
%C Amsterdam, The Netherlands
%F Aleb:2013:GECCOcomp
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/2464576.2482689
%U http://dx.doi.org/doi:10.1145/2464576.2482689
%P 1107-1112

%0 Journal Article
%T The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior
%A Aleksandrov, A. V.
%A Kazakov, S. V.
%A Sergushichev, A. A.
%A Tsarev, F. N.
%A Shalyto, A. A.
%J Journal of Computer and Systems Sciences International
%D 2013
%8 may
%V 52
%N 3
%I SP MAIK Nauka/Interperiodica
%@ 1064-2307
%G English
%F Aleksandrov:2013:JCSSI
%X 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.
%K genetic algorithms, genetic programming, FSM
%9 journal article
%R doi:10.1134/S1064230713020020
%U http://dx.doi.org/doi:10.1134/S1064230713020020
%P 410-425

%0 Journal Article
%T Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions
%A Alekseeva, Natalia
%A Tanev, Ivan
%A Shimohara, Katsunori
%J Algorithms
%D 2018
%8 jul
%V 11
%N 7
%@ 1999-4893
%F Alekseeva:2018:Algorithms
%O Special Issue Algorithms for PID Controller
%X 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.
%K genetic algorithms, genetic programming, autonomous vehicles, automated steering, slippery road conditions, PID controllers
%9 journal article
%R doi:10.3390/a11070108
%U http://www.mdpi.com/1999-4893/11/7/108
%U http://dx.doi.org/doi:10.3390/a11070108
%P 108

%0 Conference Proceedings
%T On the Emergence of Oscillations in the Evolved Autosteering of a Car on Slippery Roads
%A Alekseeva, Natalia
%A Tanev, Ivan
%A Shimohara, Katsunori
%S 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
%D 2019
%8 jul
%F Alekseeva:2019:AIM
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/AIM.2019.8868610
%U http://dx.doi.org/doi:10.1109/AIM.2019.8868610
%P 1371-1378

%0 Journal Article
%T PD Steering Controller Utilizing the Predicted Position on Track for Autonomous Vehicles Driven on Slippery Roads
%A Alekseeva, Natalia
%A Tanev, Ivan
%A Shimohara, Katsunori
%J Algorithms
%D 2020
%8 feb
%V 13
%N 2
%F DBLP:journals/algorithms/AlekseevaTS20
%X 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.
%K genetic algorithms, genetic programming, autonomous vehicles, automated steering, slippery road conditions, PD controllers, predictive model
%9 journal article
%R doi:10.3390/a13020048
%U https://www.mdpi.com/1999-4893/13/2/48/pdf
%U http://dx.doi.org/doi:10.3390/a13020048
%P id48

%0 Journal Article
%T Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming
%A Alemdag, S.
%A Gurocak, Z.
%A Cevik, A.
%A Cabalar, A. F.
%A Gokceoglu, C.
%J Engineering Geology
%D 2016
%V 203
%@ 0013-7952
%F Alemdag:2016:EG
%O Special Issue on Probabilistic and Soft Computing Methods for Engineering Geology
%X 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.
%K genetic algorithms, genetic programming, Deformation modulus, Rock mass, Neural network, Neuro fuzzy
%9 journal article
%R doi:10.1016/j.enggeo.2015.12.002
%U http://www.sciencedirect.com/science/article/pii/S0013795215300971
%U http://dx.doi.org/doi:10.1016/j.enggeo.2015.12.002
%P 70-82

%0 Conference Proceedings
%T 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
%A Aler, Ricardo
%Y Banzhaf, Wolfgang
%Y Poli, Riccardo
%Y Schoenauer, Marc
%Y Fogarty, Terence C.
%S Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%D 1998
%8 14 15 apr
%V 1391
%I Springer-Verlag
%C Paris
%@ 3-540-64360-5
%F aler:1998:5parity
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0055928
%U http://dx.doi.org/doi:10.1007/BFb0055928
%P 60-70

%0 Conference Proceedings
%T Evolved Heuristics for Planning
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%Y Porto, V. William
%Y Saravanan, N.
%Y Waagen, D.
%Y Eiben, A. E.
%S Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%D 1998
%8 25 27 mar
%V 1447
%I Springer-Verlag
%C Mission Valley Marriott, San Diego, California, USA
%@ 3-540-64891-7
%F aler:1998:ehp
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0040753
%U http://dx.doi.org/doi:10.1007/BFb0040753
%P 745-754

%0 Conference Proceedings
%T Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%Y Shavlik, Jude
%S Proceedings of the Fifteenth International Conference on Machine Learning, ICML’98
%D 1998
%8 jul
%I Morgan Kaufmann
%C Madison, Wisconsin, USA
%@ 1-55860-556-8
%F icml98-ricardo
%X 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.
%K genetic algorithms, genetic programming, Learning in Planning, Multistrategy learning
%U http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz
%P 10-18

%0 Thesis
%T Programacion Genetica de Heuristicas para Planificacion
%A Mur, Ricardo Aler
%D 1999
%8 jul
%C Spain
%C Facultad de Informatica de la Universidad Politecnica de Madrid
%F aler:thesis
%X 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.
%K genetic algorithms, genetic programming, Planning, Problem Solving, Rule Based System
%9 Ph.D. thesis
%U http://oa.upm.es/1101/1/10199907.pdf

%0 Conference Proceedings
%T GP fitness functions to evolve heuristics for planning
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%Y Middendorf, Martin
%S Evolutionary Methods for AI Planning
%D 2000
%8 August
%C Las Vegas, Nevada, USA
%F aler:2000:G
%X 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
%K genetic algorithms, genetic programming
%U http://scalab.uc3m.es/~dborrajo/papers/gecco00.ps.gz
%P 189-195

%0 Conference Proceedings
%T Knowledge Representation Issues in Control Knowledge Learning
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%Y Langley, Pat
%S Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000)
%D 2000
%8 jun 29  jul 2
%I Morgan Kaufmann
%C Stanford University, Standord, CA, USA
%@ 1-55860-707-2
%G en
%F oai:CiteSeerPSU:341634
%X 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).
%K genetic algorithms, genetic programming, EBL, HAMLET, EVOCK
%U http://scalab.uc3m.es/~dborrajo/papers/icml00.ps.gz
%P 1-8

%0 Conference Proceedings
%T Grammars for Learning Control Knowledge with GP
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%S Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%8 27 30 may
%I IEEE Press
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%@ 0-7803-6658-1
%F aler:2001:glckg
%X 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
%K 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
%R doi:10.1109/CEC.2001.934330
%U http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz
%U http://dx.doi.org/doi:10.1109/CEC.2001.934330
%P 1220-1227

%0 Journal Article
%T Learning to Solve Planning Problems Efficiently by Means of Genetic Programming
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%J Evolutionary Computation
%D 2001
%8 Winter
%V 9
%N 4
%@ 1063-6560
%F aler:2001:ECJ
%X 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.
%K genetic algorithms, genetic programming, genetic planning, evolving heuristics, planning, search. EvoCK, STGP, blocks world, logistics, Prodigy4.0, STRIPS, PDL40.
%9 journal article
%R doi:10.1162/10636560152642841
%U http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841
%U http://dx.doi.org/doi:10.1162/10636560152642841
%P 387-420

%0 Journal Article
%T Using genetic programming to learn and improve control knowledge
%A Aler, Ricardo
%A Borrajo, Daniel
%A Isasi, Pedro
%J Artificial Intelligence
%D 2002
%8 oct
%V 141
%N 1-2
%F aler:2002:AI
%X 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).
%K genetic algorithms, genetic programming, Speedup learning, Multi-strategy learning, Planning
%9 journal article
%R doi:10.1016/S0004-3702(02)00246-1
%U http://scalab.uc3m.es/~dborrajo/papers/aij-evock.ps.gz
%U http://dx.doi.org/doi:10.1016/S0004-3702(02)00246-1
%P 29-56

%0 Conference Proceedings
%T Cost-benefit Analysis of Using Heuristics in ACGP
%A Aleshunas, John
%A Janikow, Cezary
%Y Smith, Alice E.
%S Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%D 2011
%8 May 8 jun
%I IEEE Press
%C New Orleans, USA
%@ 0-7803-8515-2
%F Aleshunas:2011:CAoUHiA
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2011.5949749
%U http://dx.doi.org/doi:10.1109/CEC.2011.5949749
%P 1177-1183

%0 Conference Proceedings
%T Constructing an Optimisation Phase Using Grammatical Evolution
%A Alexander, B. J.
%A Gratton, M. J.
%Y Tyrrell, Andy
%S 2009 IEEE Congress on Evolutionary Computation
%D 2009
%8 18 21 may
%I IEEE Press
%C Trondheim, Norway
%F Alexander:2009:cec
%X 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.
%K 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
%R doi:10.1109/CEC.2009.4983083
%U P395.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2009.4983083
%P 1209-1216

%0 Conference Proceedings
%T Boosting Search for Recursive Functions Using Partial Call-Trees
%A Alexander, Brad
%A Zacher, Brad
%Y Bartz-Beielstein, Thomas
%Y Brank, Juergen
%Y Smith, Jim
%S 13th International Conference on Parallel Problem Solving from Nature
%S Lecture Notes in Computer Science
%D 2014
%8 13 17 sep
%V 8672
%I Springer
%C Ljubljana, Slovenia
%F alexander2014boosting
%X 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.
%K genetic algorithms, genetic programming, grammatical evolution, Recursion, Call-Tree, Adaptive Grammar
%R doi:10.1007/978-3-319-10762-2_38
%U http://dx.doi.org/doi:10.1007/978-3-319-10762-2_38
%P 384-393

%0 Book Section
%T Discussion on Automatic Fault Localisation and Repair
%A Alexander, Bradley
%E Mei, Hong
%E Minku, Leandro
%E Neumann, Frank
%E Yao, Xin
%B Computational Intelligence for Software Engineering
%D 2014
%8 oct 20 23
%I National Institute of Informatics
%C Japan
%F Alexander:2014:shonan
%O NII Shonan Meeting Report: No. 2014-13
%K genetic algorithms, genetic programming, genetic improvement, APR
%U http://shonan.nii.ac.jp/seminar/reports/wp-content/uploads/sites/56/2015/01/No.2014-13.pdf
%P 16-19

%0 Conference Proceedings
%T Using Scaffolding with Partial Call-Trees to Improve Search
%A Alexander, Brad
%A Pyromallis, Connie
%A Lorenzetti, George
%A Zacher, Brad
%Y Handl, Julia
%Y Hart, Emma
%Y Lewis, Peter R.
%Y Lopez-Ibanez, Manuel
%Y Ochoa, Gabriela
%Y Paechter, Ben
%S 14th International Conference on Parallel Problem Solving from Nature
%S LNCS
%D 2016
%8 17 21 sep
%V 9921
%I Springer
%C Edinburgh
%F Alexander:2016:PPSN
%X 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.
%K genetic algorithms, genetic programming, Grammatical evolution, Recursion
%R doi:10.1007/978-3-319-45823-6_3
%U http://dx.doi.org/doi:10.1007/978-3-319-45823-6_3
%P 324-334

%0 Generic
%T A Preliminary Exploration of Floating Point Grammatical Evolution
%A Alexander, Brad
%D 2018
%8 September
%I arXiv
%F Alexander:2018:arxiv
%X 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.
%K genetic algorithms, genetic programming, grammatical evolution
%U http://arxiv.org/abs/1806.03455

%0 Conference Proceedings
%T Temperature Forecasting in the Concept of Weather Derivatives: a Comparison between Wavelet Networks and Genetic Programming
%A Alexandiris, Antonios K.
%A Kampouridis, Michael
%Y Iliadis, Lazaros S.
%Y Papadopoulos, Harris
%Y Jayne, Chrisina
%S Proceedings of 14th International Conference on Engineering Applications of Neural Networks (EANN 2013), Part I
%S Communications in Computer and Information Science
%D 2013
%8 sep 13 16
%V 383
%I Springer
%C Halkidiki, Greece
%F conf/eann/AlexandirisK13
%X 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.
%K genetic algorithms, genetic programming, weather derivatives, wavelet networks, temperature derivatives
%R doi:10.1007/978-3-642-41013-0_2
%U http://dx.doi.org/10.1007/978-3-642-41013-0
%U http://dx.doi.org/doi:10.1007/978-3-642-41013-0_2
%P 12-21

%0 Journal Article
%T A comparison of wavelet networks and genetic programming in the context of temperature derivatives
%A Alexandridis, Antonis K.
%A Kampouridis, Michael
%A Cramer, Sam
%J International Journal of Forecasting
%D 2017
%V 33
%N 1
%@ 0169-2070
%F Alexandridis:2017:IJF
%X 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.
%K genetic algorithms, genetic programming, Weather derivatives, Wavelet networks, Temperature derivatives, Modelling, Forecasting
%9 journal article
%R doi:10.1016/j.ijforecast.2016.07.002
%U http://www.sciencedirect.com/science/article/pii/S0169207016300711
%U http://dx.doi.org/doi:10.1016/j.ijforecast.2016.07.002
%P 21-47

%0 Thesis
%T Optimisation of Time Domain Controllers for Supply Ships Using Genetic Algorithms and Genetic Programming
%A Alfaro Cid, Maria Eva
%D 2003
%8 oct
%C Glasgow, UK
%C The University of Glasgow
%F Alfaro-Cid:thesis
%X 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.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://casnew.iti.es/papers/ThesisEva.pdf

%0 Conference Proceedings
%T Clasificación de Senales de Electroencefalograma Usando Programación Genética
%A Alfaro-Cid, Eva
%A Esparcia-Alcázar, Anna
%A Sharman, Ken
%S Actas del IV Congreso Espanol sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB’05)
%D 2005
%8 sep
%C Granada, Spain
%F alfespshar05
%K genetic algorithms, genetic programming
%U http://www.iti.upv.es/cas/nade/data/maeb05vfinal.pdf

%0 Conference Proceedings
%T Evolution of a Strategy for Ship Guidance Using Two Implementations of Genetic Programming
%A Alfaro-Cid, Eva
%A McGookin, Euan William
%A Murray-Smith, David James
%Y Keijzer, Maarten
%Y Tettamanzi, Andrea
%Y Collet, Pierre
%Y van Hemert, Jano I.
%Y Tomassini, Marco
%S Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%D 2005
%8 30 mar  1 apr
%V 3447
%I Springer
%C Lausanne, Switzerland
%@ 3-540-25436-6
%F eurogp:Alfaro-CidMM05
%X 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.
%K genetic algorithms, genetic programming: Poster
%R doi:10.1007/978-3-540-31989-4_22
%U http://dx.doi.org/doi:10.1007/978-3-540-31989-4_22
%P 250-260

%0 Conference Proceedings
%T Using distributed genetic programming to evolve classifiers for a brain computer interface
%A Alfaro-Cid, Eva
%A Esparcia-Alcázar, Anna
%A Sharman, Ken
%Y Verleysen, Michel
%S ESANN’2006 proceedings - European Symposium on Artificial Neural Networks
%D 2006
%8 26 28 apr
%C Bruges, Belgium
%@ 2-930307-06-4
%F conf/esann/Alfaro-CidES06
%X 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
%K genetic algorithms, genetic programming
%U http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-44.pdf
%P 59-66

%0 Conference Proceedings
%T Evolving a Learning Machine by Genetic Programming
%A Alfaro-Cid, Eva
%A Sharman, Ken
%A Esparcia-Alcazar, Anna I.
%Y Yen, Gary G.
%Y Wang, Lipo
%Y Bonissone, Piero
%Y Lucas, Simon M.
%S Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%8 June 21 jul
%I IEEE Press
%C Vancouver
%@ 0-7803-9487-9
%F Alfaro-Cid:2006:CEC
%X 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.
%K genetic algorithms, genetic programming, simulated annealing, function set, learning machine, learning node, optimization algorithm, simulated annealing
%R doi:10.1109/CEC.2006.1688316
%U http://dx.doi.org/doi:10.1109/CEC.2006.1688316
%P 958-962

%0 Conference Proceedings
%T Predicción de quiebra empresarial usando programación genética
%A Alfaro Cid, Eva
%A Sharman, Ken
%A Esparcia Alcázar, Anna I.
%Y Rodriguez, Francisco Almeida
%Y Batista, Maria Belen Melian
%Y Perez, Jose Andres Moreno
%Y Vega, Jose Marcos Moreno
%S Actas del V Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB’07)
%D 2007
%8 Febrero
%I La Laguna
%C Tenerife, Spain
%F alshaes2007a
%K genetic algorithms, genetic programming
%U https://dialnet.unirioja.es/servlet/articulo?codigo=4142085
%P 703-710

%0 Conference Proceedings
%T Aprendizaje automático con programación genética
%A Alfaro Cid, Eva
%A Sharman, Ken
%A Esparcia Alcázar, Anna I.
%A Cuesta Cañada, Alberto
%S Actas del V Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB’07)
%D 2007
%8 Febrero
%I La Laguna
%C Tenerife, Spain
%F alshaescu2007a
%K genetic algorithms, genetic programming
%U https://dialnet.unirioja.es/servlet/articulo?codigo=4148339
%P 819-826

%0 Conference Proceedings
%T A genetic programming approach for bankruptcy prediction using a highly unbalanced database
%A Alfaro-Cid, Eva
%A Sharman, Ken
%A Esparcia-Alcàzar, Anna I.
%Y Giacobini, Mario
%Y Brabazon, Anthony
%Y Cagnoni, Stefano
%Y Di Caro, Gianni A.
%Y Drechsler, Rolf
%Y Farooq, Muddassar
%Y Fink, Andreas
%Y Lutton, Evelyne
%Y Machado, Penousal
%Y Minner, Stefan
%Y O’Neill, Michael
%Y Romero, Juan
%Y Rothlauf, Franz
%Y Squillero, Giovanni
%Y Takagi, Hideyuki
%Y Uyar, A. Sima
%Y Yang, Shengxiang
%S Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%D 2007
%8 November 13 apr
%V 4448
%I Springer Verlag
%C Valencia, Spain
%F alfaro-cid:evows07
%X 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.
%K genetic algorithms, genetic programming, SVM
%R doi:10.1007/978-3-540-71805-5_19
%U http://dx.doi.org/doi:10.1007/978-3-540-71805-5_19
%P 169-178

%0 Conference Proceedings
%T A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem
%A Alfaro-Cid, Eva
%A Mora, Antonio Miguel
%A Guervós, Juan Julián Merelo
%A Esparcia-Alcázar, Anna
%A Sharman, Ken
%Y Giacobini, Mario
%Y Brabazon, Anthony
%Y Cagnoni, Stefano
%Y Di Caro, Gianni
%Y Drechsler, Rolf
%Y Ekárt, Anikó
%Y Esparcia-Alcázar, Anna
%Y Farooq, Muddassar
%Y Fink, Andreas
%Y McCormack, Jon
%Y O’Neill, Michael
%Y Romero, Juan
%Y Rothlauf, Franz
%Y Squillero, Giovanni
%Y Uyar, Sima
%Y Yang, Shengxiang
%S Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%D 2008
%8 26 28 mar
%V 4974
%I Springer
%C Naples
%F conf/evoW/Alfaro-CidMGES08
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-78761-7_13
%U http://dx.doi.org/doi:10.1007/978-3-540-78761-7_13
%P 123-132

%0 Conference Proceedings
%T Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction
%A Alfaro-Cid, E.
%A Castillo, P. A.
%A Esparcia, A.
%A Sharman, K.
%A Merelo, J. J.
%A Prieto, A.
%A Laredo, J. L. J.
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Alfaro-Cid:2008:cec
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2008.4631188
%U EC0649.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2008.4631188
%P 2902-2908

%0 Journal Article
%T Genetic Programming for the Automatic Design of Controllers for a Surface Ship
%A Alfaro-Cid, Eva
%A McGookin, Euan W.
%A Murray-Smith, David J.
%A Fossen, Thor I.
%J IEEE Transactions on Intelligent Transportation Systems
%D 2008
%8 jun
%V 9
%N 2
%@ 1524-9050
%F Alfaro-Cid:2008:ieeeITS
%X 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.
%K genetic algorithms, genetic programming, control system synthesis, navigation, propulsion, ships CyberShip II, automatic design, controller structure, navigation controllers, propulsion controllers, supply ship, surface ship
%9 journal article
%R doi:10.1109/TITS.2008.922932
%U http://results.ref.ac.uk/Submissions/Output/2145080
%U http://dx.doi.org/doi:10.1109/TITS.2008.922932
%P 311-321

%0 Conference Proceedings
%T Prune and Plant: A New Bloat Control Method for Genetic Programming
%A Alfaro-Cid, Eva
%A Esparcia-Alcazar, Anna
%A Sharman, Ken
%A Fernandez de Vega, Francisco
%A Merelo, J. J.
%S Eighth International Conference on Hybrid Intelligent Systems, HIS ’08
%D 2008
%8 sep
%F Alfaro-Cid:2008:HIS
%X 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.
%K genetic algorithms, genetic programming, bloat control method, genetic operator, prune and plant, time consumption, tree size reduction, mathematical operators, trees (mathematics)
%R doi:10.1109/HIS.2008.127
%U http://dx.doi.org/doi:10.1109/HIS.2008.127
%P 31-35

%0 Book Section
%T Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming
%A Alfaro-Cid, Eva
%A Cuesta-Canada, Alberto
%A Sharman, Ken
%A Esparcia-Alcazar, Anna
%E Brabazon, Anthony
%E O’Neill, Michael
%B Natural Computing in Computational Finance
%S Studies in Computational Intelligence
%D 2008
%V 100
%I Springer
%F series/sci/Alfaro-CidCSE08
%X 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.
%K genetic algorithms, genetic programming, STGP, SVM
%R doi:10.1007/978-3-540-77477-8_9
%U http://dx.doi.org/doi:10.1007/978-3-540-77477-8_9
%P 161-185

%0 Conference Proceedings
%T Modeling Pheromone Dispensers Using Genetic Programming
%A Alfaro-Cid, Eva
%A Esparcia-Alcázar, Anna I.
%A Moya, Pilar
%A Femenia-Ferrer, Beatriu
%A Sharman, Ken
%A Merelo, J. J.
%Y Giacobini, Mario
%Y Brabazon, Anthony
%Y Cagnoni, Stefano
%Y Caro, Gianni A. Di
%Y Ekárt, Anikó
%Y Esparcia-Alcázar, Anna
%Y Farooq, Muddassar
%Y Fink, Andreas
%Y Machado, Penousal
%Y McCormack, Jon
%Y O’Neill, Michael
%Y Neri, Ferrante
%Y Preuss, Mike
%Y Rothlauf, Franz
%Y Tarantino, Ernesto
%Y Yang, Shengxiang
%S Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
%S Lecture Notes in Computer Science
%D 2009
%8 apr 15 17
%V 5484
%I Springer
%C Tubingen, Germany
%F Alfaro-Cid:2009:evonum
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-01129-0_73
%U http://dx.doi.org/doi:10.1007/978-3-642-01129-0_73
%P 635-644

%0 Conference Proceedings
%T Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenser
%A Alfaro-Cid, Eva
%A Esparcia-Alcazar, Anna
%A Moya, Pilar
%A Merelo, J. J.
%A Femenia-Ferrer, Beatriu
%A Sharman, Ken
%A Primo, Jaime
%Y Esparcia, Anna I.
%Y Chen, Ying-ping
%Y Ochoa, Gabriela
%Y Ozcan, Ender
%Y Schoenauer, Marc
%Y Auger, Anne
%Y Beyer, Hans-Georg
%Y Hansen, Nikolaus
%Y Finck, Steffen
%Y Ros, Raymond
%Y Whitley, Darrell
%Y Wilson, Garnett
%Y Harding, Simon
%Y Langdon, W. B.
%Y Wong, Man Leung
%Y Merkle, Laurence D.
%Y Moore, Frank W.
%Y Ficici, Sevan G.
%Y Rand, William
%Y Riolo, Rick
%Y Kharma, Nawwaf
%Y Buckley, William R.
%Y Miller, Julian
%Y Stanley, Kenneth
%Y Bacardit, Jaume
%Y Browne, Will
%Y Drugowitsch, Jan
%Y Beume, Nicola
%Y Preuss, Mike
%Y Smith, Stephen L.
%Y Cagnoni, Stefano
%Y DeLeo, Jim
%Y Floares, Alexandru
%Y Baughman, Aaron
%Y Gustafson, Steven
%Y Keijzer, Maarten
%Y Kordon, Arthur
%Y Congdon, Clare Bates
%S GECCO-2009 Symbolic regression and modeling workshop (SRM)
%D 2009
%8 August 12 jul
%I ACM
%C Montreal
%F DBLP:conf/gecco/Alfaro-CidEMMFSP09
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/1570256.1570309
%U http://dx.doi.org/doi:10.1145/1570256.1570309
%P 2225-2230

%0 Journal Article
%T Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study
%A Alfaro-Cid, Eva
%A Merelo, J. J.
%A Fernandez de Vega, Francisco
%A Esparcia-Alcazar, Anna I.
%A Sharman, Ken
%J Evolutionary Computation
%D 2010
%8 Summer
%V 18
%N 2
%@ 1063-6560
%F Alfaro-Cid:2010:EC
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1162/evco.2010.18.2.18206
%U http://dx.doi.org/doi:10.1162/evco.2010.18.2.18206
%P 305-332

%0 Journal Article
%T Genetic programming and serial processing for time series classification
%A Alfaro-Cid, Eva
%A Sharman, Ken
%A Esparcia-Alcazar, Anna I.
%J Evolutionary Computation
%D 2014
%8 Summer
%V 22
%N 2
%@ 1063-6560
%F Alfaro-Cid:2014:EC
%X 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.
%K genetic algorithms, genetic programming, Classification, time series, serial data processing, real world applications
%9 journal article
%R doi:10.1162/EVCO_a_00110
%U http://dx.doi.org/doi:10.1162/EVCO_a_00110
%P 265-285

%0 Journal Article
%T Book Review: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
%A Alfonseca, Manuel
%A Ortega, Alfonso
%J Genetic Programming and Evolvable Machines
%D 2004
%8 dec
%V 5
%N 4
%@ 1389-2576
%F alfonseca:2004:GPEM
%K genetic algorithms, genetic programming, grammatical evolution
%9 journal article
%R doi:10.1023/B:GENP.0000036057.27304.5b
%U http://dx.doi.org/doi:10.1023/B:GENP.0000036057.27304.5b
%P 393

%0 Journal Article
%T Evolving an ecology of mathematical expressions with grammatical evolution
%A Alfonseca, Manuel
%A Gil, Francisco Jose Soler
%J Biosystems
%D 2013
%V 111
%N 2
%F journals/biosystems/AlfonsecaG13
%K genetic algorithms, genetic programming, grammatical evolution
%9 journal article
%U http://dx.doi.org/10.1016/j.biosystems.2012.12.004
%P 111-119

%0 Journal Article
%T Evolving a predator-prey ecosystem of mathematical expressions with grammatical evolution
%A Alfonseca, Manuel
%A Gil, Francisco Jose Soler
%J Complexity
%D 2015
%V 20
%N 3
%F journals/complexity/AlfonsecaG15
%K genetic algorithms, genetic programming, grammatical evolution
%9 journal article
%U http://dx.doi.org/10.1002/cplx.21507
%P 66-83

%0 Conference Proceedings
%T Toward Human-Like Summaries Generated from Heterogeneous Software Artefacts
%A Alghamdi, Mahfouth
%A Treude, Christoph
%A Wagner, Markus
%Y Alexander, Brad
%Y Haraldsson, Saemundur O.
%Y Wagner, Markus
%Y Woodward, John R.
%S 7th edition of GI @ GECCO 2019
%D 2019
%8 jul 13 17
%I ACM
%C Prague, Czech Republic
%F Alghamdi:2019:GI7
%X 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.
%K genetic algorithms, genetic programming, genetic improvement, SBSE, Heterogeneous software artefacts, extractive summarisation, human-like summaries
%R doi:10.1145/3319619.3326814
%U https://arxiv.org/abs/1905.02258
%U http://dx.doi.org/doi:10.1145/3319619.3326814
%P 1701-1702

%0 Conference Proceedings
%T Development of 2D curve-fitting genetic/gene-expression programming technique for efficient time-series financial forecasting
%A Alghieth, Manal
%A Yang, Yingjie
%A Chiclana, Francisco
%S 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)
%D 2015
%8 sep
%F Alghieth:2015:INISTA
%X 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.
%K genetic algorithms, genetic programming, gene expression programming
%R doi:10.1109/INISTA.2015.7276734
%U http://dx.doi.org/doi:10.1109/INISTA.2015.7276734

%0 Conference Proceedings
%T Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets
%A Alghieth, Manal
%A Yang, Yingjie
%A Chiclana, Francisco
%Y Ong, Yew-Soon
%S Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)
%D 2016
%8 24 29 jul
%I IEEE Press
%C Vancouver
%F Alghieth:2016:CEC
%X 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.
%K genetic algorithms, genetic programming, gene expressing programming, Stock market, Time series financial forecasting
%R doi:10.1109/CEC.2016.7744083
%U https://www.dora.dmu.ac.uk/handle/2086/11896
%U http://dx.doi.org/doi:10.1109/CEC.2016.7744083
%P 2381-2388

%0 Conference Proceedings
%T Evolving diverse Ms. Pac-Man playing agents using genetic programming
%A Alhejali, Atif M.
%A Lucas, Simon M.
%S UK Workshop on Computational Intelligence (UKCI 2010)
%D 2010
%8 August 10 sep
%F Alhejali:2010:UKCI
%X 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.
%K genetic algorithms, genetic programming, Ms PacMan game, reactive agents, computer games, learning (artificial intelligence), software agents
%R doi:10.1109/UKCI.2010.5625586
%U http://dx.doi.org/doi:10.1109/UKCI.2010.5625586
%P 1-6

%0 Conference Proceedings
%T Using a Training Camp with Genetic Programming to Evolve Ms Pac-Man Agents
%A Alhejali, Atif M.
%A Lucas, Simon M.
%S Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games
%D 2011
%8 31 aug  3 sep
%I IEEE
%C Seoul, South Korea
%F Alhejali:2011:CIG
%X 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.
%K genetic algorithms, genetic programming, Pac-Man, Evolving Controllers, Decomposition learning, Training camp
%R doi:10.1109/CIG.2011.6031997
%U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper31.pdf
%U http://dx.doi.org/doi:10.1109/CIG.2011.6031997
%P 118-125

%0 Conference Proceedings
%T Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent
%A Alhejali, Atif M.
%A Lucas, Simon M.
%S IEEE Conference on Computational Intelligence in Games (CIG 2013)
%D 2013
%8 aug
%F Alhejali:2013:CIG
%X 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.
%K 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
%R doi:10.1109/CIG.2013.6633639
%U http://dx.doi.org/doi:10.1109/CIG.2013.6633639

%0 Thesis
%T Genetic Programming and the Evolution of Games Playing Agents
%A Alhejali, Atif Mansour
%D 2013
%C UK
%C Computing and Electronic Systems, University of Essex
%F Alhejali:thesis
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://www.essex.ac.uk/csee/news_and_seminars/newsEvent.aspx?e_id=5796

%0 Journal Article
%T Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits
%A Ali, B.
%A Almaini, A. E. A.
%A Kalganova, T.
%J Genetic Programming and Evolvable Machines
%D 2004
%8 mar
%V 5
%N 1
%@ 1389-2576
%F ali:2004:GPEM
%X 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.
%K genetic algorithms, evolvable hardware, sequential circuits, state assignment
%9 journal article
%R doi:10.1023/B:GENP.0000017009.11392.e2
%U http://dx.doi.org/doi:10.1023/B:GENP.0000017009.11392.e2

%0 Book Section
%T Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework
%A Ali, Mostafa Z.
%A Reynolds, Robert G.
%A Che, Xiangdong
%E Riolo, Rick L.
%E Soule, Terence
%E Worzel, Bill
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%D 2008
%8 15 17 may
%I Springer
%C Ann Arbor
%F Ali:2008:GPTP
%K genetic algorithms, genetic programming
%R doi:10.1007/978-0-387-87623-8_16
%U http://dx.doi.org/doi:10.1007/978-0-387-87623-8_16
%P 249-269

%0 Journal Article
%T Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach
%A Ali, Mumtaz
%A Deo, Ravinesh C.
%A Downs, Nathan J.
%A Maraseni, Tek
%J Agricultural and Forest Meteorology
%D 2018
%V 263
%@ 0168-1923
%F ALI:2018:AFM
%X 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
%K genetic algorithms, genetic programming, Crop yield prediction, Cotton yield, Climate data, Markov Chain Monte Carlo based copula model
%9 journal article
%R doi:10.1016/j.agrformet.2018.09.002
%U http://www.sciencedirect.com/science/article/pii/S0168192318302971
%U http://dx.doi.org/doi:10.1016/j.agrformet.2018.09.002
%P 428-448

%0 Book Section
%T Chapter 2 - Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression
%A Ali, Mumtaz
%A Deo, Ravinesh C.
%E Samui, Pijush
%E Tien Bui, Dieu
%E Chakraborty, Subrata
%E Deo, Ravinesh C.
%B Handbook of Probabilistic Models
%D 2020
%I Butterworth-Heinemann
%F ALI:2020:HPM
%X 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
%K genetic algorithms, genetic programming, Agricultural precision, Artificial neural network, Minimax probability machine regression, Wheat yield model
%R doi:10.1016/B978-0-12-816514-0.00002-3
%U http://www.sciencedirect.com/science/article/pii/B9780128165140000023
%U http://dx.doi.org/doi:10.1016/B978-0-12-816514-0.00002-3
%P 37-87

%0 Journal Article
%T Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences
%A Ali, Safdar
%A Majid, Abdul
%J Journal of Biomedical Informatics
%D 2015
%8 apr
%V 54
%@ 1532-0464
%F Ali:2015:JBI
%X 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.
%K genetic algorithms, genetic programming, Breast cancer, Amino acids, Physicochemical properties, Stacking ensemble
%9 journal article
%R doi:10.1016/j.jbi.2015.01.004
%U http://www.sciencedirect.com/science/article/pii/S1532046415000064
%U http://dx.doi.org/doi:10.1016/j.jbi.2015.01.004
%P 256-269

%0 Journal Article
%T A Systematic Review of the Application and Empirical Investigation of Search-Based Test-Case Generation
%A Ali, Shaukat
%A Briand, Lionel C.
%A Hemmati, Hadi
%A Panesar-Walawege, Rajwinder K.
%J IEEE Transactions on Software Engineering
%D 2010
%8 nov  dec
%V 36
%N 6
%@ 0098-5589
%F Ali:2010:ieeeTSE
%X 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.
%K genetic algorithms, genetic programming, SBSE
%9 journal article
%R doi:10.1109/TSE.2009.52
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463
%U http://dx.doi.org/doi:10.1109/TSE.2009.52
%P 742-762

%0 Conference Proceedings
%T Critical analysis of swarm intelligence based routing protocols in adhoc and sensor wireless networks
%A Ali, Zulfiqar
%A Shahzad, Waseem
%S International Conference on Computer Networks and Information Technology (ICCNIT 2011)
%D 2011
%8 November 13 jul
%C Abbottabad
%F Ali:2011:ICCNIT
%X 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.
%K 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
%R doi:10.1109/ICCNIT.2011.6020945
%U http://dx.doi.org/doi:10.1109/ICCNIT.2011.6020945
%P 287-292

%0 Conference Proceedings
%T Miner for OACCR: Case of medical data analysis in knowledge discovery
%A Ali, Samaher Hussein
%S 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2012)
%D 2012
%F Ali:2012:SETIT
%X 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).
%K 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
%R doi:10.1109/SETIT.2012.6482043
%U http://dx.doi.org/doi:10.1109/SETIT.2012.6482043
%P 962-975

%0 Conference Proceedings
%T Symbolic method for deriving policy in reinforcement learning
%A Alibekov, Eduard
%A Kubalik, Jiri
%A Babuska, Robert
%S 2016 IEEE 55th Conference on Decision and Control (CDC)
%D 2016
%8 dec
%F Alibekov:2016:CDC
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CDC.2016.7798684
%U http://dx.doi.org/doi:10.1109/CDC.2016.7798684
%P 2789-2795

%0 Conference Proceedings
%T Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study
%A Aliehyaei, R.
%A Khan, S.
%S 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)
%D 2014
%8 dec
%F Aliehyaei:2014:SKIMA
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/SKIMA.2014.7083391
%U http://dx.doi.org/doi:10.1109/SKIMA.2014.7083391

%0 Journal Article
%T Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks
%A Ghorbani, Mohammad Ali
%A Khatibi, Rahman
%A Aytek, Ali
%A Makarynskyy, Oleg
%A Shiri, Jalal
%J Computer & Geosciences
%D 2010
%V 36
%N 5
%@ 0098-3004
%F AliGhorbani2010620
%X 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.
%K genetic algorithms, genetic programming, Sea-level variations, Forecasting, Artificial Neural Networks, Comparative studies
%9 journal article
%R doi:10.1016/j.cageo.2009.09.014
%U http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9
%U http://dx.doi.org/doi:10.1016/j.cageo.2009.09.014
%P 620-627

%0 Book Section
%T Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models
%A Ghorbani, M. A.
%A Khatibi, R.
%A Asadi, H.
%A Yousefi, P.
%E Ventura, Sebastian
%B Genetic Programming - New Approaches and Successful Applications
%D 2012
%I InTech
%F AliGhorbani:2012:GPnew
%K genetic algorithms, genetic programming, Gene Expression Programming, GEP, ANN, MLR, Chaos
%R doi:10.5772/47801
%U http://dx.doi.org/doi:10.5772/47801
%P 255-284

%0 Conference Proceedings
%T A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains
%A Alissa, Mohamad
%A Sim, Kevin
%A Hart, Emma
%Y Coello Coello, Carlos Artemio
%Y Aguirre, Arturo Hernandez
%Y Uribe, Josu Ceberio
%Y Fabre, Mario Garza
%Y Toscano Pulido, Gregorio
%Y Rodriguez-Vazquez, Katya
%Y Wanner, Elizabeth
%Y Veerapen, Nadarajen
%Y Montes, Efren Mezura
%Y Allmendinger, Richard
%Y Marin, Hugo Terashima
%Y Wagner, Markus
%Y Bartz-Beielstein, Thomas
%Y Filipic, Bogdan
%Y Trautmann, Heike
%Y Tang, Ke
%Y Koza, John
%Y Goodman, Erik
%Y Langdon, William B.
%Y Nicolau, Miguel
%Y Zarges, Christine
%Y Volz, Vanessa
%Y Tusar, Tea
%Y Naujoks, Boris
%Y Bosman, Peter A. N.
%Y Whitley, Darrell
%Y Solnon, Christine
%Y Helbig, Marde
%Y Doncieux, Stephane
%Y Wilson, Dennis G.
%Y Fernandez de Vega, Francisco
%Y Paquete, Luis
%Y Chicano, Francisco
%Y Xue, Bing
%Y Bacardit, Jaume
%Y Mostaghim, Sanaz
%Y Fieldsend, Jonathan
%Y Schuetze, Oliver
%Y Arnold, Dirk
%Y Ochoa, Gabriela
%Y Segura, Carlos
%Y Cotta, Carlos
%Y Emmerich, Michael
%Y Zhang, Mengjie
%Y Purshouse, Robin
%Y Ray, Tapabrata
%Y Petke, Justyna
%Y Ishikawa, Fuyuki
%Y Lengler, Johannes
%Y Neumann, Frank
%S Proceedings of the 2020 Genetic and Evolutionary Computation Conference
%S GECCO ’20
%D 2020
%8 jul 8 12
%I Association for Computing Machinery
%C internet
%F Alissa:2020:GECCO
%X 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.
%K genetic algorithms, deep learning, algorithm selection problem, bin-packing
%R doi:10.1145/3377930.3390224
%U https://doi.org/10.1145/3377930.3390224
%U http://dx.doi.org/doi:10.1145/3377930.3390224
%P 157-165

%0 Conference Proceedings
%T Kernel evolution for support vector classification
%A Alizadeh, Mehrdad
%A Ebadzadeh, Mohammad Mehdi
%S IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS 2011)
%D 2011
%8 November 15 apr
%C Paris
%F Alizadeh:2011:EAIS
%X 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.
%K 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
%R doi:10.1109/EAIS.2011.5945924
%U http://dx.doi.org/doi:10.1109/EAIS.2011.5945924
%P 93-99

%0 Conference Proceedings
%T Software effort estimation by tuning COOCMO model parameters using differential evolution
%A Aljahdali, Sultan
%A Sheta, Alaa F.
%S 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)
%D 2010
%8 16 19 may
%C Hammamet, Tunisia
%F Aljahdali:2010:AICCSA
%X 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.
%K 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
%R doi:10.1109/AICCSA.2010.5586985
%U http://dx.doi.org/doi:10.1109/AICCSA.2010.5586985

%0 Journal Article
%T Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic
%A Aljahdali, Sultan
%J Journal of Computer Science
%D 2011
%V 7
%N 10
%I Science Publications
%@ 15493636
%G eng
%F Aljahdali:2011:Jcomputerscience
%X 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.
%K software reliability growth models (SRGM), takagi-sugeno technique, fuzzy logic (FL), artificial neural net-works (ANN), model structure, linear regression model, NASA space
%9 journal article
%R doi:10.3844/jcssp.2011.1574.1580
%U http://www.thescipub.com/pdf/10.3844/jcssp.2011.1574.1580
%U http://dx.doi.org/doi:10.3844/jcssp.2011.1574.1580
%P 1574-1580

%0 Journal Article
%T Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming
%A Aljahdali, Sultan
%A Sheta, Alaa
%J International Journal of Advanced Research in Artificial Intelligence
%D 2013
%V 2
%N 12
%I The Science and Information (SAI) Organization
%G eng
%F Aljahdali:2013:IJARAI
%X 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.
%K genetic algorithms, genetic programming, SBSE
%9 journal article
%U http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf
%P 52-57

%0 Thesis
%T Modelling pile capacity and load-settlement behaviour of piles embedded in sand & mixed soils using artificial intelligence
%A Alkroosh, Iyad Salim Jabor
%D 2011
%8 may
%C Australia
%C Curtin University, Faculty of Engineering and Computing, Department of Civil Engineering
%G en
%F Alkroosh:thesis
%X 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.
%K 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
%9 Ph.D. thesis
%U http://espace.library.curtin.edu.au/Modelling.pdf

%0 Journal Article
%T Predicting pile dynamic capacity via application of an evolutionary algorithm
%A Alkroosh, I.
%A Nikraz, H.
%J Soils and Foundations
%D 2014
%V 54
%N 2
%@ 0038-0806
%F Alkroosh:2014:SF
%X 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.
%K genetic algorithms, genetic programming, gene expression programming
%9 journal article
%R doi:10.1016/j.sandf.2014.02.013
%U http://www.sciencedirect.com/science/article/pii/S0038080614000213
%U http://dx.doi.org/doi:10.1016/j.sandf.2014.02.013
%P 233-242

%0 Journal Article
%T High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
%A Allen, Jess
%A Davey, Hazel M.
%A Broadhurst, David
%A Heald, Jim K.
%A Rowland, Jem J.
%A Oliver, Stephen G.
%A Kell, Douglas B.
%J Nature Biotechnology
%D 2003
%8 jun
%V 21
%N 6
%F Allen:2003:NB
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1038/nbt823
%U http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf
%U http://dx.doi.org/doi:10.1038/nbt823
%P 692-696

%0 Journal Article
%T Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting
%A Allen, Jess
%A Davey, Hazel M.
%A Broadhurst, David
%A Rowland, Jem J.
%A Oliver, Stephen G.
%A Kell, Douglas B.
%J Applied and Environmental Microbiology
%D 2004
%8 oct
%V 70
%N 10
%F Allen:2004:AEM
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1128/AEM.70.10.6157-6165.2004
%U http://dx.doi.org/doi:10.1128/AEM.70.10.6157-6165.2004
%P 6157-6165

%0 Conference Proceedings
%T Evolving reusable 3D packing heuristics with genetic programming
%A Allen, Sam
%A Burke, Edmund K.
%A Hyde, Matthew R.
%A Kendall, Graham
%Y Raidl, Guenther
%Y Rothlauf, Franz
%Y Squillero, Giovanni
%Y Drechsler, Rolf
%Y Stuetzle, Thomas
%Y Birattari, Mauro
%Y Congdon, Clare Bates
%Y Middendorf, Martin
%Y Blum, Christian
%Y Cotta, Carlos
%Y Bosman, Peter
%Y Grahl, Joern
%Y Knowles, Joshua
%Y Corne, David
%Y Beyer, Hans-Georg
%Y Stanley, Ken
%Y Miller, Julian F.
%Y van Hemert, Jano
%Y Lenaerts, Tom
%Y Ebner, Marc
%Y Bacardit, Jaume
%Y O’Neill, Michael
%Y Di Penta, Massimiliano
%Y Doerr, Benjamin
%Y Jansen, Thomas
%Y Poli, Riccardo
%Y Alba, Enrique
%S GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%D 2009
%8 August 12 jul
%I ACM
%C Montreal
%F DBLP:conf/gecco/AllenBHK09
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/1569901.1570029
%U http://dx.doi.org/doi:10.1145/1569901.1570029
%P 931-938

%0 Thesis
%T Algorithms and data structures for three-dimensional packing
%A Allen, Sam D.
%D 2011
%8 jul
%C UK
%C School of Computer Science, University of Nottingham
%F Allen:thesis
%X 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.
%K genetic algorithms, genetic programming, packing, shipment, business, operations research
%9 Ph.D. thesis
%U http://etheses.nottingham.ac.uk/2779/1/thesis_nicer.pdf

%0 Book Section
%T Content Diversity in Genetic Programming and its Correlation with Fitness
%A Almal, A.
%A Worzel, W. P.
%A Wollesen, E. A.
%A MacLean, C. D.
%E Yu, Tina
%E Riolo, Rick L.
%E Worzel, Bill
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%D 2005
%8 December 14 may
%V 9
%I Springer
%C Ann Arbor
%@ 0-387-28110-X
%F Almal:2005:GPTP
%X 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.
%K genetic algorithms, genetic programming, diversity, chaos game, fitness correlation, visualisation
%R doi:10.1007/0-387-28111-8_12
%U http://dx.doi.org/doi:10.1007/0-387-28111-8_12
%P 177-190

%0 Conference Proceedings
%T Using genetic programming to classify node positive patients in bladder cancer
%A Almal, Arpit A.
%A Mitra, Anirban P.
%A Datar, Ram H.
%A Lenehan, Peter F.
%A Fry, David W.
%A Cote, Richard J.
%A Worzel, William P.
%Y Keijzer, Maarten
%Y Cattolico, Mike
%Y Arnold, Dirk
%Y Babovic, Vladan
%Y Blum, Christian
%Y Bosman, Peter
%Y Butz, Martin V.
%Y Coello Coello, Carlos
%Y Dasgupta, Dipankar
%Y Ficici, Sevan G.
%Y Foster, James
%Y Hernandez-Aguirre, Arturo
%Y Hornby, Greg
%Y Lipson, Hod
%Y McMinn, Phil
%Y Moore, Jason
%Y Raidl, Guenther
%Y Rothlauf, Franz
%Y Ryan, Conor
%Y Thierens, Dirk
%S GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%D 2006
%8 August 12 jul
%V 1
%I ACM Press
%C Seattle, Washington, USA
%@ 1-59593-186-4
%F 1144040
%K 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
%R doi:10.1145/1143997.1144040
%U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p239.pdf
%U http://dx.doi.org/doi:10.1145/1143997.1144040
%P 239-246

%0 Book Section
%T Program Structure-Fitness Disconnect and Its Impact On Evolution In GP
%A Almal, A. A.
%A MacLean, C. D.
%A Worzel, W. P.
%E Riolo, Rick L.
%E Soule, Terence
%E Worzel, Bill
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%D 2007
%8 17 19 may
%I Springer
%C Ann Arbor
%F Almal:2007:GPTP
%X 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.
%K genetic algorithms, genetic programming, phenotype, genotype, evolutionary dynamics, GP structure, GP content, speciation, population, fitness
%R doi:10.1007/978-0-387-76308-8_9
%U http://dx.doi.org/doi:10.1007/978-0-387-76308-8_9
%P 143-158

%0 Book Section
%T A Population Based Study of Evolutionary Dynamics in Genetic Programming
%A Almal, A. A.
%A MacLean, C. D.
%A Worzel, W. P.
%E Riolo, Rick L.
%E Soule, Terence
%E Worzel, Bill
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%D 2008
%8 15 17 may
%I Springer
%C Ann Arbor
%F Almal:2008:GPTP
%K genetic algorithms, genetic programming
%R doi:10.1007/978-0-387-87623-8_2
%U http://dx.doi.org/doi:10.1007/978-0-387-87623-8_2
%P 19-29

%0 Conference Proceedings
%T On the Detection of Community Smells using Genetic Programming-based Ensemble Classifier Chain
%A Almarimi, Nuri
%A Ouni, Ali
%A Chouchen, Moataz
%A Saidani, Islem
%A Mkaouer, Mohamed Wiem
%S 15th IEEE/ACM International Conference on Global Software Engineering (ICGSE)
%D 2020
%8 26 jun
%C internet
%F almarimi2020community
%X 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.
%K genetic algorithms, genetic programming, SBSE, community smells, social debt, socio-technical factors, search-based software engineering, multi-label learning
%R doi:10.1145/3372787.3390439
%U https://conf.researchr.org/details/icgse-2020/icgse-2020-research-papers/6/On-the-Detection-of-Community-Smells-using-Genetic-Programming-based-Ensemble-Classif
%U http://dx.doi.org/doi:10.1145/3372787.3390439
%P 43-54

%0 Journal Article
%T Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions
%A Almeida, Alexandre E.
%A da S. Torres, Ricardo
%J IEEE Geoscience and Remote Sensing Letters
%D 2017
%8 sep
%V 14
%N 9
%@ 1545-598X
%F Almeida:2017:ieeeGRSL
%X 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.
%K genetic algorithms, genetic programming, remote sensing, time series similarity
%9 journal article
%R doi:10.1109/LGRS.2017.2719033
%U http://dx.doi.org/doi:10.1109/LGRS.2017.2719033
%P 1499-1503

%0 Journal Article
%T Deriving vegetation indices for phenology analysis using genetic programming
%A Almeida, Jurandy
%A dos Santos, Jefersson A.
%A Miranda, Waner O.
%A Alberton, Bruna
%A Morellato, Leonor Patricia C.
%A da S. Torres, Ricardo
%J Ecological Informatics
%D 2015
%V 26, Part 3
%@ 1574-9541
%F Almeida:2015:EI
%X 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.
%K genetic algorithms, genetic programming, Remote phenology, Digital cameras, Image analysis, Vegetation indices
%9 journal article
%R doi:10.1016/j.ecoinf.2015.01.003
%U http://www.sciencedirect.com/science/article/pii/S1574954115000114
%U http://dx.doi.org/doi:10.1016/j.ecoinf.2015.01.003
%P 61-69

%0 Conference Proceedings
%T A Genetically Programmable Hybrid Virtual Reconfigurable Architecture for Image Filtering Applications
%A Almeida, M. A.
%A Pedrino, E. C.
%A Nicoletti, M. C.
%S 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
%D 2016
%8 oct
%F Almeida:2016:SIBGRAPI
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/SIBGRAPI.2016.029
%U http://dx.doi.org/doi:10.1109/SIBGRAPI.2016.029
%P 152-157

%0 Journal Article
%T Hybrid Evolvable Hardware for automatic generation of image filters
%A Almeida, M. A.
%A Pedrino, E. C.
%J Integrated Computer-Aided Engineering
%D 2018
%V 25
%N 3
%@ 1069-2509
%F Almeida:2018:ICAE
%X 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).
%K genetic algorithms, genetic programming, Evolvable Hardware, FPGA, virtual reconfigurable architecture
%9 journal article
%R doi:10.3233/ICA-180561
%U http://dx.doi.org/doi:10.3233/ICA-180561
%P 289-303

%0 Book Section
%T Communicating Agents Developed with Genetic Programming
%A Almgren, Magnus
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%D 2000
%8 jun
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F almgren:2000:CADGP
%K genetic algorithms, genetic programming
%P 25-32

%0 Journal Article
%T Strength of Cfrp-steel double strap joints under impact loads using genetic programming
%A Al-Mosawe, Alaa
%A Kalfat, Robin
%A Al-Mahaidi, Riadh
%J Composite Structures
%D 2017
%V 160
%@ 0263-8223
%F AlMosawe:2017:CS
%X 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.
%K genetic algorithms, genetic programming, Carbon fibre, Genetic programing, Impact behaviour, Joint strength, CFRP-steel joint
%9 journal article
%R doi:10.1016/j.compstruct.2016.11.016
%U http://www.sciencedirect.com/science/article/pii/S0263822316317767
%U http://dx.doi.org/doi:10.1016/j.compstruct.2016.11.016
%P 1205-1211

%0 Conference Proceedings
%T Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction
%A Al-Mulla, M. R.
%A Sepulveda, F.
%A Colley, M.
%A Kattan, A.
%S Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009
%D 2009
%8 February 6 sep
%C Minneapolis, Minnesota, USA
%F Al-Mulla:2009:EMBC
%X 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.
%K 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
%R doi:10.1109/IEMBS.2009.5335368
%U http://dx.doi.org/doi:10.1109/IEMBS.2009.5335368
%P 2633-2638

%0 Journal Article
%T Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue
%A Al-Mulla, Mohamed R.
%A Sepulveda, Francisco
%A Colley, M.
%J Medical Engineering and Physics
%D 2011
%8 may
%V 33
%N 4
%F Al-Mulla:2011:MEP
%X 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
%K genetic algorithms, Localized muscle fatigue, sEMG, Wavelet analysis, matlab
%9 journal article
%R doi:10.1016/j.medengphy.2010.11.008
%U http://dx.doi.org/doi:10.1016/j.medengphy.2010.11.008
%P 411-417

%0 Conference Proceedings
%T Straight Line Programs: A New Linear Genetic Programming Approach
%A Alonso, Cesar L.
%A Puente, Jorge
%A Montana, Jose Luis
%S 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI ’08
%D 2008
%8 nov
%V 2
%F Alonso:2008:ieeeICTAI
%X 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.
%K 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
%R doi:10.1109/ICTAI.2008.14
%U http://dx.doi.org/doi:10.1109/ICTAI.2008.14
%P 517-524

%0 Journal Article
%T A new Linear Genetic Programming approach based on straight line programs: some Theoretical and Experimental Aspects
%A Alonso, Cesar L.
%A Montana, Jose Luis
%A Puente, Jorge
%A Borges, Cruz Enrique
%J International Journal on Artificial Intelligence Tools
%D 2009
%V 18
%N 5
%F Alonso:2009:IJAIT
%X 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.
%K genetic algorithms, genetic programming, slp, Vapnik-Chervonenkis dimension, VC
%9 journal article
%R doi:10.1142/S0218213009000391
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.3133
%U http://dx.doi.org/doi:10.1142/S0218213009000391
%P 757-781

%0 Conference Proceedings
%T Evolution Strategies for Constants Optimization in Genetic Programming
%A Alonso, Cesar L.
%A Montana, Jose Luis
%A Borges, Cruz Enrique
%S 21st International Conference on Tools with Artificial Intelligence, ICTAI ’09
%D 2009
%8 nov
%F Alonso:2009:ICTAI
%X 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.
%K genetic algorithms, genetic programming, computer program, constants optimization, evolutionary computation methods, learning problems, linear genetic programming approach, symbolic regression problem, regression analysis
%R doi:10.1109/ICTAI.2009.35
%U http://dx.doi.org/doi:10.1109/ICTAI.2009.35
%P 703-707

%0 Conference Proceedings
%T Model Complexity Control in Straight Line Program Genetic Programming
%A Alonso, Cesar Luis
%A Montana, Jose Luis
%A Borges, Cruz Enrique
%Y Rosa, Agostinho C.
%Y Dourado, Antonio
%Y Correia, Kurosh Madani
%Y Filipe, Joaquim
%Y Kacprzyk, Janusz
%S IJCCI 2013 - Proceedings of the 5th International Joint Conference on Computational Intelligence, Vilamoura, Algarve, Portugal, 20-22 September, 2013
%D 2013
%I SciTePress
%F conf/ijcci/AlonsoMB13
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.5220/0004554100250036
%P 25-36

%0 Conference Proceedings
%T Modelling Medical Time Series Using Grammar-Guided Genetic Programming
%A Alonso, Fernando
%A Martinez, Loic
%A Perez-Perez, Aurora
%A Santamaria, Agustin
%A Valente, Juan Pedro
%Y Perner, Petra
%S 8th Industrial Conference in Data Mining, Medical Applications, E-Commerce, Marketing and Theoretical Aspects, ICDM 2008
%S Lecture Notes in Computer Science
%D 2008
%8 jul 16 18
%V 5077
%I Springer
%C Leipzig, Germany
%F conf/incdm/AlonsoMPSV08
%X 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.
%K genetic algorithms, genetic programming, Time series characterization, isokinetics, symbolic distance, information extraction, reference model, text mining
%R doi:10.1007/978-3-540-70720-2_3
%U http://dx.doi.org/doi:10.1007/978-3-540-70720-2_3
%P 32-46

%0 Conference Proceedings
%T GGGP-based method for modeling time series: operator selection, parameter optimization and expert evaluation
%A Alonso, Fernando
%A Martinez, Loic
%A Santamaria, Agustin
%A Perez, Aurora
%A Valente, Juan Pedro
%Y Branke, Juergen
%Y Pelikan, Martin
%Y Alba, Enrique
%Y Arnold, Dirk V.
%Y Bongard, Josh
%Y Brabazon, Anthony
%Y Butz, Martin V.
%Y Clune, Jeff
%Y Cohen, Myra
%Y Deb, Kalyanmoy
%Y Engelbrecht, Andries P.
%Y Krasnogor, Natalio
%Y Miller, Julian F.
%Y O’Neill, Michael
%Y Sastry, Kumara
%Y Thierens, Dirk
%Y van Hemert, Jano
%Y Vanneschi, Leonardo
%Y Witt, Carsten
%S GECCO ’10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%D 2010
%8 July 11 jul
%I ACM
%C Portland, Oregon, USA
%F Alonso:2010:gecco
%X 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.
%K genetic algorithms, genetic programming, grammar-guided genetic programming, Poster
%R doi:10.1145/1830483.1830664
%U http://dx.doi.org/doi:10.1145/1830483.1830664
%P 989-990

%0 Thesis
%T Modelling the High-Frequency FX Market: An Agent-Based Approach
%A Aloud, Monira Essa
%D 2013
%8 apr
%C United Kingdom
%C Department of Computing and Electronic Systems, University of Essex
%F MoniraAloud-Ph.D.Thesis
%X 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.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://fac.ksu.edu.sa/sites/default/files/MoniraAloud-Ph.D.Thesis.pdf

%0 Journal Article
%T 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.
%A Al-Rabadi, Anas N.
%J The Computer Journal
%D 2006
%8 jan
%V 49
%N 1
%@ 0010-4620
%F Al-Rabadi:2006:EPB
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1093/comjnl/bxh134
%U http://comjnl.oxfordjournals.org/cgi/content/full/49/1/129
%U http://dx.doi.org/doi:10.1093/comjnl/bxh134
%P 129-130

%0 Conference Proceedings
%T A smart agent to trade and predict foreign exchange market
%A Alrefaie, Mohamed Taher
%A Hamouda, Alaa-Aldine
%A Ramadan, Rabie
%S IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES 2013)
%D 2013
%8 apr
%F Alrefaie:2013:CIES
%X 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.
%K 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
%R doi:10.1109/CIES.2013.6611741
%U http://dx.doi.org/doi:10.1109/CIES.2013.6611741
%P 141-148

%0 Journal Article
%T Utilization of magnetic water in cementitious adhesive for near-surface mounted CFRP strengthening system
%A Al-Safy, Rawaa
%A Al-Mosawe, Alaa
%A Al-Mahaidi, Riadh
%J Construction and Building Materials
%D 2019
%V 197
%@ 0950-0618
%F ALSAFY:2019:CBM
%X 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
%K genetic algorithms, genetic programming, Magnetic water, Cement-based adhesive, NSM, CFRP, Concrete, GP modelling
%9 journal article
%R doi:10.1016/j.conbuildmat.2018.11.219
%U http://www.sciencedirect.com/science/article/pii/S0950061818329143
%U http://dx.doi.org/doi:10.1016/j.conbuildmat.2018.11.219
%P 474-488

%0 Conference Proceedings
%T Automatic feature extraction and image classification using genetic programming
%A Al-Sahaf, Harith
%A Neshatian, Kourosh
%A Zhang, Mengjie
%S 5th International Conference on Automation, Robotics and Applications (ICARA 2011)
%D 2011
%8 June 8 dec
%C Wellington, New Zealand
%F Al-Sahaf:2011:ICARA
%X 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.
%K genetic algorithms, genetic programming, feature extraction, human-domain expert intervention, image classification, multilayer domain-independent GP-based approach, feature extraction, image classification
%R doi:10.1109/ICARA.2011.6144874
%U http://dx.doi.org/doi:10.1109/ICARA.2011.6144874
%P 157-162

%0 Conference Proceedings
%T Extracting Image Features for Classification By Two-Tier Genetic Programming
%A Al-Sahaf, Harith
%A Song, Andy
%A Neshatian, Kourosh
%A Zhang, Mengjie
%Y Li, Xiaodong
%S Proceedings of the 2012 IEEE Congress on Evolutionary Computation
%D 2012
%8 October 15 jun
%C Brisbane, Australia
%@ 0-7803-8515-2
%F Al-Sahaf:2012:CEC
%X 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.
%K genetic algorithms, genetic programming, Evolutionary Computer Vision
%R doi:10.1109/CEC.2012.6256412
%U http://dx.doi.org/doi:10.1109/CEC.2012.6256412
%P 1630-1637

%0 Journal Article
%T Two-Tier genetic programming: towards raw pixel-based image classification
%A Al-Sahaf, Harith
%A Song, Andy
%A Neshatian, Kourosh
%A Zhang, Mengjie
%J Expert Systems with Applications
%D 2012
%V 39
%N 16
%@ 0957-4174
%F AlSahaf2012
%X 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.
%K genetic algorithms, genetic programming, Evolutionary computation, Feature extraction, Feature selection, Image classification
%9 journal article
%R doi:10.1016/j.eswa.2012.02.123
%U http://www.sciencedirect.com/science/article/pii/S0957417412003867
%U http://dx.doi.org/doi:10.1016/j.eswa.2012.02.123
%P 12291-12301

%0 Conference Proceedings
%T Hybridisation of Genetic Programming and Nearest Neighbour for Classification
%A Al-Sahaf, Harith
%A Song, Andy
%A Zhang, Mengjie
%Y de la Fraga, Luis Gerardo
%S 2013 IEEE Conference on Evolutionary Computation
%D 2013
%8 jun 20 23
%V 1
%C Cancun, Mexico
%F Al-Sahaf:2013:CEC
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2013.6557889
%U http://dx.doi.org/doi:10.1109/CEC.2013.6557889
%P 2650-2657

%0 Conference Proceedings
%T Binary image classification using genetic programming based on local binary patterns
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%S 28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)
%D 2013
%8 nov
%I IEEE Press
%C Wellington
%F Al-Sahaf:2013:IVCNZ
%X 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.
%K 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
%R doi:10.1109/IVCNZ.2013.6727019
%U http://dx.doi.org/doi:10.1109/IVCNZ.2013.6727019
%P 220-225

%0 Conference Proceedings
%T A One-Shot Learning Approach to Image Classification Using Genetic Programming
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%Y Cranefield, Stephen
%Y Nayak, Abhaya
%S Proceedings of the 26th Australasian Joint Conference on Artificial Intelligence (AI2013)
%S LNAI
%D 2013
%8 January 6 dec
%V 8272
%I Springer
%C Dunedin, New Zealand
%F Al-Sahaf:2013:AI
%X 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.
%K genetic algorithms, genetic programming, Local Binary Patterns, Image Classification, One-shot Learning
%R doi:10.1007/978-3-319-03680-9_13
%U http://dx.doi.org/10.1007/978-3-319-03680-9_13
%U http://dx.doi.org/doi:10.1007/978-3-319-03680-9_13
%P 110-122

%0 Conference Proceedings
%T Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%Y Cree, Michael J.
%Y Streeter, Lee V.
%Y Perrone, John
%Y Mayo, Michael
%Y Blake, Anthony M.
%S Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014
%D 2014
%8 nov 19 21
%I ACM
%C Hamilton, New Zealand
%F conf/ivcnz/Al-SahafZJ14
%X 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.
%K genetic algorithms, genetic programming, Multiclass classification, Textures
%R doi:10.1145/2683405.2683418
%U http://dl.acm.org/citation.cfm?id=2683405
%U http://dx.doi.org/doi:10.1145/2683405.2683418
%P 84-89

%0 Conference Proceedings
%T Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%Y Dick, Grant
%Y Browne, Will N.
%Y Whigham, Peter A.
%Y Zhang, Mengjie
%Y Bui, Lam Thu
%Y Ishibuchi, Hisao
%Y Jin, Yaochu
%Y Li, Xiaodong
%Y Shi, Yuhui
%Y Singh, Pramod
%Y Tan, Kay Chen
%Y Tang, Ke
%S Simulated Evolution and Learning - 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings
%S Lecture Notes in Computer Science
%D 2014
%V 8886
%I Springer
%F conf/seal/Al-SahafZJ14
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-319-13563-2
%P 335-346

%0 Conference Proceedings
%T Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%A Verma, Brijesh
%Y Murata, Yadahiko
%S Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)
%D 2015
%8 25 28 may
%I IEEE Press
%C Sendai, Japan
%F Al-Sahaf:2015:CEC
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2015.7257190
%U http://dx.doi.org/doi:10.1109/CEC.2015.7257190
%P 2460-2467

%0 Conference Proceedings
%T Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%Y Silva, Sara
%Y Esparcia-Alcazar, Anna I.
%Y Lopez-Ibanez, Manuel
%Y Mostaghim, Sanaz
%Y Timmis, Jon
%Y Zarges, Christine
%Y Correia, Luis
%Y Soule, Terence
%Y Giacobini, Mario
%Y Urbanowicz, Ryan
%Y Akimoto, Youhei
%Y Glasmachers, Tobias
%Y Fernandez de Vega, Francisco
%Y Hoover, Amy
%Y Larranaga, Pedro
%Y Soto, Marta
%Y Cotta, Carlos
%Y Pereira, Francisco B.
%Y Handl, Julia
%Y Koutnik, Jan
%Y Gaspar-Cunha, Antonio
%Y Trautmann, Heike
%Y Mouret, Jean-Baptiste
%Y Risi, Sebastian
%Y Costa, Ernesto
%Y Schuetze, Oliver
%Y Krawiec, Krzysztof
%Y Moraglio, Alberto
%Y Miller, Julian F.
%Y Widera, Pawel
%Y Cagnoni, Stefano
%Y Merelo, J. J.
%Y Hart, Emma
%Y Trujillo, Leonardo
%Y Kessentini, Marouane
%Y Ochoa, Gabriela
%Y Chicano, Francisco
%Y Doerr, Carola
%S GECCO ’15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
%D 2015
%8 November 15 jul
%I ACM
%C Madrid, Spain
%F Al-Sahaf:2015:GECCO
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/2739480.2754661
%U http://doi.acm.org/10.1145/2739480.2754661
%U http://dx.doi.org/doi:10.1145/2739480.2754661
%P 975-982

%0 Journal Article
%T Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Johnston, Mark
%J Evolutionary Computation
%D 2016
%8 Spring
%V 24
%N 1
%@ 1063-6560
%F Al-Sahaf:2015:EC
%X 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
%K genetic algorithms, genetic programming, Local Binary Patterns, One-shot Learning, Image Classification
%9 journal article
%R doi:10.1162/EVCO_a_00146
%U http://dx.doi.org/doi:10.1162/EVCO_a_00146
%P 143-182

%0 Journal Article
%T Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming
%A Al-Sahaf, Harith
%A Al-Sahaf, Ausama
%A Xue, Bing
%A Johnston, Mark
%A Zhang, Mengjie
%J IEEE Transactions on Evolutionary Computation
%D 2017
%8 feb
%V 21
%N 1
%F Al-Sahaf:2016:ieeeTEC
%9 journal article
%R doi:10.1109/TEVC.2016.2577548
%U http://dx.doi.org/doi:10.1109/TEVC.2016.2577548
%P 83-101

%0 Journal Article
%T Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming
%A Al-Sahaf, Harith
%A Al-Sahaf, Ausama
%A Xue, Bing
%A Johnston, Mark
%A Zhang, Mengjie
%J IEEE Transactions on Evolutionary Computation
%D 2017
%8 feb
%V 21
%N 1
%@ 1089-778X
%F Al-Sahaf:2017a:ieeeTEC
%X 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.
%K genetic algorithms, genetic programming, Classification, feature extraction, image descriptor, keypoint detection
%9 journal article
%R doi:10.1109/TEVC.2016.2577548
%U http://dx.doi.org/doi:10.1109/TEVC.2016.2577548
%P 83-101

%0 Conference Proceedings
%T Evolving Texture Image Descriptors Using a Multitree Genetic Programming Representation
%A Al-Sahaf, Harith
%A Xue, Bing
%A Zhang, Mengjie
%S Proceedings of the Genetic and Evolutionary Computation Conference Companion
%S GECCO ’17
%D 2017
%8 15 19 jul
%I ACM
%C Berlin, Germany
%F Al-Sahaf:2017:GECCO
%X 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.
%K genetic algorithms, genetic programming, multiclass classification, multitree, textures
%R doi:10.1145/3067695.3076039
%U http://doi.acm.org/10.1145/3067695.3076039
%U http://dx.doi.org/doi:10.1145/3067695.3076039
%P 219-220

%0 Conference Proceedings
%T A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors
%A Al-Sahaf, Harith
%A Xue, Bing
%A Zhang, Mengjie
%Y Shi, Yuhui
%Y Tan, Kay Chen
%Y Zhang, Mengjie
%Y Tang, Ke
%Y Li, Xiaodong
%Y Zhang, Qingfu
%Y Tan, Ying
%Y Middendorf, Martin
%Y Jin, Yaochu
%S Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017
%S Lecture Notes in Computer Science
%D 2017
%8 nov 10 13
%V 10593
%I Springer
%C Shenzhen, China
%F conf/seal/Al-SahafXZ17
%X 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.
%K genetic algorithms, genetic programming, Multitree, Image classification, Feature extraction
%R doi:10.1007/978-3-319-68759-9_41
%U http://dx.doi.org/doi:10.1007/978-3-319-68759-9_41
%P 499-511

%0 Journal Article
%T Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors
%A Al-Sahaf, Harith
%A Zhang, Mengjie
%A Al-Sahaf, Ausama
%A Johnston, Mark
%J IEEE Transactions on Evolutionary Computation
%D 2017
%8 dec
%V 21
%N 6
%@ 1089-778X
%F Al-Sahaf:2017:ieeeTEC
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1109/TEVC.2017.2685639
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7885048
%U http://dx.doi.org/doi:10.1109/TEVC.2017.2685639
%P 825-844

%0 Journal Article
%T Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances
%A Al-Sahaf, Harith
%A Al-Sahaf, Ausama
%A Xue, Bing
%A Zhang, Mengjie
%J Evolutionary Computation
%@ 1063-6560
%F Al-Sahaf:EC
%O Forthcoming
%X 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, e.g., corners, line-segments and shapes, in an image and extracting features from those key points. In this paper, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by using a multi-tree 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 f
%K genetic algorithms, genetic programming, ANN, image descriptor, multi-tree, image classification, feature extraction
%9 journal article
%R doi:10.1162/evco_a_00284
%U https://doi.org/10.1162/evco_a_00284
%U http://dx.doi.org/doi:10.1162/evco_a_00284

%0 Conference Proceedings
%T Automated Re-invention of a Previously Patented Optical Lens System Using Genetic Programming
%A Al-Sakran, Sameer H.
%A Koza, John R.
%A Jones, Lee W.
%Y Keijzer, Maarten
%Y Tettamanzi, Andrea
%Y Collet, Pierre
%Y van Hemert, Jano I.
%Y Tomassini, Marco
%S Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%D 2005
%8 30 mar  1 apr
%V 3447
%I Springer
%C Lausanne, Switzerland
%@ 3-540-25436-6
%F eurogp:Al-SakranKJ05
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-31989-4_3
%U http://dx.doi.org/doi:10.1007/978-3-540-31989-4_3
%P 25-37

%0 Conference Proceedings
%T Genetic Programming Testing Model
%A Al Sallami, Nada M. A.
%Y Ao, S. I.
%Y Gelman, Len
%Y Hukins, David WL
%Y Hunter, Andrew
%Y Korsunsky, A. M.
%S Proceedings of the World Congress on Engineering (WCE’12)
%S Lecture Notes in Engineering and Computer Science
%D 2012
%8 jul 4 6
%I Newswood Limited
%C London, UK
%F Al_Sallami:2012:wce
%X 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.
%K genetic algorithms, genetic programming, SBSE, model-based testing, test generator, finite state machine
%U http://www.iaeng.org/publication/WCE2012/WCE2012_pp737-741.pdf
%P 737-741

%0 Journal Article
%T A new 3D molecular structure representation using quantum topology with application to structure-property relationships
%A Alsberg, Bjorn K.
%A Marchand-Geneste, Nathalie
%A King, Ross D.
%J Chemometrics and Intelligent Laboratory Systems
%D 2000
%V 54
%N 2
%@ 0169-7439
%F Alsberg:2000:CILS
%X 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.
%K 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
%9 journal article
%R doi:10.1016/S0169-7439(00)00101-5
%U http://www.sciencedirect.com/science/article/B6TFP-426XTF7-1/2/36265a259de8f80d4918ee6612612218
%U http://dx.doi.org/doi:10.1016/S0169-7439(00)00101-5
%P 75-91

%0 Conference Proceedings
%T Deploying Search Based Software Engineering with Sapienz at Facebook
%A Alshahwan, Nadia
%A Gao, Xinbo
%A Harman, Mark
%A Jia, Yue
%A Mao, Ke
%A Mols, Alexander
%A Tei, Taijin
%A Zorin, Ilya
%Y Colanzi, Thelma Elita
%Y McMinn, Phil
%S SSBSE 2018
%S LNCS
%D 2018
%8 August 9 sep
%V 11036
%I Springer
%C Montpellier, France
%F Alshahwan:2018:SSBSE
%X 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.
%K genetic algorithms, genetic programming, genetic improvement, SBSE
%R doi:10.1007/978-3-319-99241-9_1
%U http://dx.doi.org/doi:10.1007/978-3-319-99241-9_1
%P 3-45

%0 Conference Proceedings
%T Industrial experience of Genetic Improvement in Facebook
%A Alshahwan, Nadia
%Y Petke, Justyna
%Y Tan, Shin Hwei
%Y Langdon, William B.
%Y Weimer, Westley
%S GI-2019, ICSE workshops proceedings
%D 2019
%8 28 may
%I IEEE
%C Montreal
%F Alshahwan:2019:GI
%O Invited Keynote
%X 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.
%K genetic algorithms, genetic programming, genetic improvement, APR
%R doi:10.1109/GI.2019.00010
%U https://doi.org/10.1109/GI.2019.00010
%U http://dx.doi.org/doi:10.1109/GI.2019.00010
%P 1

%0 Conference Proceedings
%T Classifying SSH encrypted traffic with minimum packet header features using genetic programming
%A Alshammari, Riyad
%A Lichodzijewski, Peter
%A Heywood, Malcolm I.
%A Zincir-Heywood, A. Nur
%Y Esparcia, Anna I.
%Y Chen, Ying-ping
%Y Ochoa, Gabriela
%Y Ozcan, Ender
%Y Schoenauer, Marc
%Y Auger, Anne
%Y Beyer, Hans-Georg
%Y Hansen, Nikolaus
%Y Finck, Steffen
%Y Ros, Raymond
%Y Whitley, Darrell
%Y Wilson, Garnett
%Y Harding, Simon
%Y Langdon, W. B.
%Y Wong, Man Leung
%Y Merkle, Laurence D.
%Y Moore, Frank W.
%Y Ficici, Sevan G.
%Y Rand, William
%Y Riolo, Rick
%Y Kharma, Nawwaf
%Y Buckley, William R.
%Y Miller, Julian
%Y Stanley, Kenneth
%Y Bacardit, Jaume
%Y Browne, Will
%Y Drugowitsch, Jan
%Y Beume, Nicola
%Y Preuss, Mike
%Y Smith, Stephen L.
%Y Cagnoni, Stefano
%Y DeLeo, Jim
%Y Floares, Alexandru
%Y Baughman, Aaron
%Y Gustafson, Steven
%Y Keijzer, Maarten
%Y Kordon, Arthur
%Y Congdon, Clare Bates
%S GECCO-2009 Defense applications of computational intelligence workshop
%D 2009
%8 August 12 jul
%I ACM
%C Montreal
%F DBLP:conf/gecco/AlshammariLHZ09
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/1570256.1570358
%U http://dx.doi.org/doi:10.1145/1570256.1570358
%P 2539-2546

%0 Conference Proceedings
%T Unveiling Skype encrypted tunnels using GP
%A Alshammari, Riyad
%A Zincir-Heywood, A. Nur
%S IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%8 18 23 jul
%I IEEE Press
%C Barcelona, Spain
%F Alshammari:2010:cec
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2010.5586288
%U http://dx.doi.org/doi:10.1109/CEC.2010.5586288

%0 Conference Proceedings
%T An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype
%A Alshammari, Riyad
%A Zincir-Heywood, A. Nur
%S 2010 International Conference on Network and Service Management (CNSM)
%D 2010
%8 25 29 oct
%F Alshammari:2010:CNSM
%X 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.
%K 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
%R doi:10.1109/CNSM.2010.5691210
%U http://dx.doi.org/doi:10.1109/CNSM.2010.5691210
%P 310-313

%0 Conference Proceedings
%T Is Machine Learning losing the battle to produce transportable signatures against VoIP traffic?
%A Alshammari, Riyad
%A Zincir-Heywood, A. Nur
%Y Smith, Alice E.
%S Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%D 2011
%8 May 8 jun
%I IEEE Press
%C New Orleans, USA
%@ 0-7803-8515-2
%F Alshammari:2011:IMLltbtptsaVt
%X 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.
%K 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
%R doi:10.1109/CEC.2011.5949799
%U http://dx.doi.org/doi:10.1109/CEC.2011.5949799
%P 1542-1549

%0 Journal Article
%T Identification of VoIP encrypted traffic using a machine learning approach
%A Alshammari, Riyad
%A Zincir-Heywood, A. Nur
%J Journal of King Saud University - Computer and Information Sciences
%D 2015
%V 27
%N 1
%@ 1319-1578
%F Alshammari:2015:JKSUCIS
%X 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.
%K genetic algorithms, genetic programming, Machine learning, Encrypted traffic, Robustness, Network signatures
%9 journal article
%R doi:10.1016/j.jksuci.2014.03.013
%U http://www.sciencedirect.com/science/article/pii/S1319157814000561
%U http://dx.doi.org/doi:10.1016/j.jksuci.2014.03.013
%P 77-92

%0 Journal Article
%T Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm
%A Al-Shammari, Eiman Tamah
%A Keivani, Afram
%A Shamshirband, Shahaboddin
%A Mostafaeipour, Ali
%A Yee, Por Lip
%A Petkovic, Dalibor
%A Ch, Sudheer
%J Energy
%D 2016
%V 95
%@ 0360-5442
%F AlShammari:2016:Energy
%X 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.
%K genetic algorithms, genetic programming, District heating systems, Heat load, Estimation, Prediction, Support Vector Machines, Firefly algorithm
%9 journal article
%R doi:10.1016/j.energy.2015.11.079
%U http://www.sciencedirect.com/science/article/pii/S0360544215016424
%U http://dx.doi.org/doi:10.1016/j.energy.2015.11.079
%P 266-273

%0 Conference Proceedings
%T Off-line Parameter Tuning for Guided Local Search Using Genetic Programming
%A Alsheddy, Abdullah
%A Kampouridis, Michael
%Y Li, Xiaodong
%S Proceedings of the 2012 IEEE Congress on Evolutionary Computation
%D 2012
%8 October 15 jun
%C Brisbane, Australia
%@ 0-7803-8515-2
%F Alsheddy:2012:CEC
%X 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.
%K genetic algorithms, genetic programming, Heuristics, metaheuristics and hyper-heuristics
%R doi:10.1109/CEC.2012.6256155
%U http://dx.doi.org/doi:10.1109/CEC.2012.6256155
%P 112-116

%0 Conference Proceedings
%T The Influence of the Picking Times of the Components in Time and Space Assembly Line Balancing Problems: An Approach with Evolutionary Algorithms
%A Alsina, Emanuel F.
%A Capodieci, Nicola
%A Cabri, Giacomo
%A Regattieri, Alberto
%S 2015 IEEE Symposium Series on Computational Intelligence
%D 2015
%8 dec
%F Alsina:2015:ieeeSSCI
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/SSCI.2015.148
%U http://dx.doi.org/doi:10.1109/SSCI.2015.148
%P 1021-1028

%0 Conference Proceedings
%T Feature selection and classification in genetic programming: Application to haptic-based biometric data
%A Alsulaiman, Fawaz A.
%A Sakr, Nizar
%A Valdes, Julio J.
%A El Saddik, Abdulmotaleb
%A Georganas, Nicolas D.
%S IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009
%D 2009
%8 jul
%F Alsulaiman:2009:ieeeCISDA
%X 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.
%K 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
%R doi:10.1109/CISDA.2009.5356540
%U http://dx.doi.org/doi:10.1109/CISDA.2009.5356540
%P 1-7

%0 Conference Proceedings
%T Identity verification based on haptic handwritten signatures: Genetic programming with unbalanced data
%A Alsulaiman, Fawaz A.
%A Valdes, Julio J.
%A El Saddik, Abdulmotaleb
%S Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on
%D 2012
%F Alsulaiman:2012:CISDA
%X 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.
%K 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
%R doi:10.1109/CISDA.2012.6291531
%U http://dx.doi.org/doi:10.1109/CISDA.2012.6291531

%0 Journal Article
%T Identity verification based on handwritten signatures with haptic information using genetic programming
%A Alsulaiman, Fawaz A.
%A Sakr, Nizar
%A Valdes, Julio J.
%A El-Saddik, Abdulmotaleb
%J ACM Transactions on Multimedia Computing, Communications, and Applications
%D 2013
%8 may
%V 9
%N 2
%I ACM
%@ 1551-6857
%F journals/tomccap/AlsulaimanSVE13
%X 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).
%K genetic algorithms, genetic programming, Biometrics, Haptics, classification, user verification
%9 journal article
%R doi:10.1145/2457450.2457453
%U http://doi.acm.org/http://dx.doi.org/10.1145/2457450.2457453
%U http://dx.doi.org/doi:10.1145/2457450.2457453
%P 11:1-11:21

%0 Conference Proceedings
%T Identity verification based on haptic handwritten Signature: Novel fitness functions for GP framework
%A Alsulaiman, Fawaz A.
%A Valdes, Julio J.
%A El Saddik, Abdulmotaleb
%S IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE 2013)
%D 2013
%8 oct
%F Alsulaiman:2013:HAVE
%X 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.
%K 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
%R doi:10.1109/HAVE.2013.6679618
%U http://dx.doi.org/doi:10.1109/HAVE.2013.6679618
%P 98-102

%0 Journal Article
%T Similarity of Amyloid Protein Motif using an Hybrid Intelligent System
%A Altamiranda, J.
%A Aguilar, J.
%A Delamarche, C.
%J IEEE Latin America Transactions (Revista IEEE America Latina)
%D 2011
%8 sep
%V 9
%N 5
%@ 1548-0992
%F Altamiranda:2011:ieeeLAT
%O In Spanish
%X 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.
%K 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
%9 journal article
%R doi:10.1109/TLA.2011.6030978
%U http://dx.doi.org/doi:10.1109/TLA.2011.6030978
%P 700-710

%0 Conference Proceedings
%T Comparison and fusion model in protein motifs
%A Altamiranda, Junior
%A Aguilar, Jose
%A Delamarche, Chistian
%S XXXIX Latin American Computing Conference (CLEI 2013)
%D 2013
%8 July 11 oct
%I IEEE
%C Naiguata
%F Altamiranda:2013:CLEI
%X 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.
%K genetic algorithms, genetic programming, Bioinformatics, Neural Network, ANN, ACO, Ant Colony Optimization
%R doi:10.1109/CLEI.2013.6670618
%U http://dx.doi.org/doi:10.1109/CLEI.2013.6670618

%0 Book Section
%T The Evolution of Evolvability in Genetic Programming
%A Altenberg, Lee
%E Kinnear, Jr., Kenneth E.
%B Advances in Genetic Programming
%D 1994
%I MIT Press
%F kinnear:altenberg
%X 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 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 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
%K genetic algorithms, genetic programming
%U http://dynamics.org/~altenber/PAPERS/EEGP/
%P 47-74

%0 Conference Proceedings
%T Evolving better representations through selective genome growth
%A Altenberg, Lee
%S Proceedings of the 1st IEEE Conference on Evolutionary Computation
%D 1994
%8 27 29 jun
%V 1
%I IEEE
%C Orlando, Florida, USA
%F Altenberg:1994EBR
%X 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
%K genetic algorithms, genetic programming
%U http://dynamics.org/~altenber/PAPERS/EBR/
%P 182-187

%0 Conference Proceedings
%T Emergent phenomena in genetic programming
%A Altenberg, Lee
%Y Sebald, Anthony V.
%Y Fogel, Lawrence J.
%S Evolutionary Programming — Proceedings of the Third Annual Conference
%D 1994
%8 24 26 feb
%I World Scientific Publishing
%C San Diego, CA, USA
%@ 981-02-1810-9
%F Altenberg:1994EPIGP
%X 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
%K genetic algorithms, genetic programming
%U http://dynamics.org/~altenber/PAPERS/EPIGP/
%P 233-241

%0 Conference Proceedings
%T The Schema Theorem and Price’s Theorem
%A Altenberg, Lee
%Y Whitley, L. Darrell
%Y Vose, Michael D.
%S Foundations of Genetic Algorithms 3
%D 1994
%8 31 jul –2 aug
%I Morgan Kaufmann
%C Estes Park, Colorado, USA
%@ 1-55860-356-5
%F Altenberg:1995STPT
%O Published 1995
%X 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
%K genetic algorithms, genetic programming
%R doi:10.1016/B978-1-55860-356-1.50006-6
%U http://dynamics.org/~altenber/PAPERS/STPT/
%U http://dx.doi.org/doi:10.1016/B978-1-55860-356-1.50006-6
%P 23-49

%0 Book Section
%T Genome growth and the evolution of the genotype-phenotype map
%A Altenberg, Lee
%E Banzhaf, Wolfgang
%E Eeckman, Frank H.
%B Evolution as a Computational Process
%D 1995
%I Springer-Verlag
%C Berlin, Germany
%F Altenberg:1995GGEGPM
%X 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
%K genetic algorithms, genetic programming
%U http://dynamics.org/~altenber/PAPERS/GGEGPM/
%P 205-259

%0 Unpublished Work
%T Selection, generalized transmission, and the evolution of modifier genes. II. Modifier polymorphisms
%A Altenberg, Lee
%A Feldman, Marcus W.
%D 1995
%F Altenberg:and:Feldman:1995SGTEMG2
%O In preparation
%9 unpublished
%U ftp://ftp.mhpcc.edu/pub/incoming/altenberg/LeeSGTEMG2MP.ps.Z

%0 Book Section
%T Modularity in Evolution: Some Low-Level Questions
%A Altenberg, Lee
%E Rasskin-Gutman, Diego
%E Callebaut, Werner
%B Modularity: Understanding the Development and Evolution of Complex Natural Systems
%D 2005
%8 jun
%I MIT Press
%C Cambridge, MA, USA
%@ 0-262-03326-7
%F Altenberg:2004:MESLLQ
%X 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.
%K genetic algorithms, genetic programming
%U http://dynamics.org/Altenberg/FILES/LeeMESLLQ.pdf
%P 99-128

%0 Book Section
%T Open Problems in the Spectral Analysis of Evolutionary Dynamics
%A Altenberg, Lee
%E Menon, Anil
%B Frontiers of Evolutionary Computation
%S Genetic Algorithms And Evolutionary Computation Series
%D 2004
%V 11
%I Kluwer Academic Publishers
%C Boston, MA, USA
%@ 1-4020-7524-3
%F Altenberg:2004:OPSAED
%X 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?
%K genetic algorithms, genetic programming
%R doi:10.1007/1-4020-7782-3_4
%U http://dynamics.org/Altenberg/FILES/LeeOPSAED.pdf
%U http://dx.doi.org/doi:10.1007/1-4020-7782-3_4
%P 73-102

%0 Journal Article
%T Evolvability Suppression to Stabilize Far-Sighted Adaptations
%A Altenberg, Lee
%J Artificial Life
%D 2005
%8 Fall
%V 11
%N 3
%@ 1064-5462
%F altenberg:2004:ESSFSA
%X 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.
%K genetic algorithms
%9 journal article
%R doi:10.1162/106454605774270633
%U http://dx.doi.org/doi:10.1162/106454605774270633
%P 427-443

%0 Journal Article
%T Mathematics awaits: commentary on ”Genetic Programming and Emergence” by Wolfgang Banzhaf
%A Altenberg, Lee
%J Genetic Programming and Evolvable Machines
%D 2014
%8 mar
%V 15
%N 1
%@ 1389-2576
%F Altenberg:2014:GPEM
%X 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.
%K genetic algorithms, genetic programming, Evolvability, Robustness, Subtree exchange, Mathematics, Matrix theory, Lagrange distribution
%9 journal article
%R doi:10.1007/s10710-013-9198-5
%U http://dx.doi.org/doi:10.1007/s10710-013-9198-5
%P 87-89

%0 Journal Article
%T Evolvability and robustness in artificial evolving systems: three perturbations
%A Altenberg, Lee
%J Genetic Programming and Evolvable Machines
%D 2014
%8 sep
%V 15
%N 3
%@ 1389-2576
%F Altenberg:2014:GPEMb
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s10710-014-9223-3
%U http://dx.doi.org/doi:10.1007/s10710-014-9223-3
%P 275-280

%0 Book Section
%T Evolutionary Computation
%A Altenberg, Lee
%E Kliman, Richard M.
%B The Encyclopedia of Evolutionary Biology
%D 2016
%V 2
%I Academic Press
%C Oxford, UK
%F Altenberg:2016:EC
%X 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.
%K 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
%R doi:10.1016/B978-0-12-800049-6.00307-3
%U https://www.sciencedirect.com/science/article/pii/B9780128000496003073
%U http://dx.doi.org/doi:10.1016/B978-0-12-800049-6.00307-3
%P 40-47

%0 Journal Article
%T 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
%A Altenberg, Lee
%J Genetic Programming and Evolvable Machines
%D 2017
%8 sep
%V 18
%N 3
%@ 1389-2576
%F Altenberg:2017:GPEM
%O Special Peer Commentary on Mapping of Genotype to Phenotype in Evolutionary Algorithms
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s10710-017-9290-3
%U http://dx.doi.org/doi:10.1007/s10710-017-9290-3
%P 363-367

%0 Journal Article
%T Automatic Generation and Evaluation of Recombination Games. Doctoral Dissertation by Cameron Browne, Review
%A Althoefer, Ingo
%J ICGA Journal
%D 2010
%V 33
%N 4
%F Althoefer:2010:ICGA
%K genetic algorithms, genetic programming
%9 journal article
%U https://chessprogramming.wikispaces.com/ICGA+Journal

%0 Journal Article
%T Evolutionary data-modelling of an innovative low reflective vertical quay
%A Altomare, C.
%A Gironella, X.
%A Laucelli, D.
%J Journal of Hydroinformatics
%D 2013
%8 January
%V 15
%N 3
%F Altomare:2013:JoH
%X 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.
%K genetic algorithms, genetic programming, data-mining, evolutionary polynomial regression, low reflective vertical quay, wave reflection
%9 journal article
%R doi:10.2166/hydro.2012.219
%U https://iwaponline.com/jh/article-pdf/15/3/763/387059/763.pdf
%U http://dx.doi.org/doi:10.2166/hydro.2012.219
%P 763-779

%0 Conference Proceedings
%T Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm
%A Aluko, Babatunde
%A Smonou, Dafni
%A Kampouridis, Michael
%A Tsang, Edward
%S IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr 2104)
%D 2014
%8 27 28 mar
%F Aluko:2014:CIFEr
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1109/CIFEr.2014.6924092
%U http://dx.doi.org/doi:10.1109/CIFEr.2014.6924092
%P 333-340

%0 Journal Article
%T Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach
%A Alvarado-Iniesta, Alejandro
%A Guillen-Anaya, Luis Gonzalo
%A Rodriguez-Picon, Luis Alberto
%A Neco-Caberta, Raul
%J Journal of Intelligent Manufacturing
%D 2020
%8 jan
%V 31
%F alvarado-iniesta:JoIM
%X 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.
%K genetic algorithms, genetic programming, Structural optimization, Multi-objective optimization, Finite element analysis, Decision making
%9 journal article
%R doi:10.1007/s10845-018-1432-9
%U http://link.springer.com/article/10.1007/s10845-018-1432-9
%U http://dx.doi.org/doi:10.1007/s10845-018-1432-9
%P 19-32

%0 Journal Article
%T Forecasting front displacements with a satellite based ocean forecasting (SOFT) system
%A Alvarez, A.
%A Orfila, Alejandro
%A Basterretxea, G.
%A Tintore, J.
%A Vizoso, G.
%A Fornes, A.
%J Journal of Marine Systems
%D 2007
%8 mar
%V 65
%N 1-4
%F Alvarez:2007:JMS
%O Marine Environmental Monitoring and Prediction - Selected papers from the 36th International Liege Colloquium on Ocean Dynamics
%X 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.
%K genetic algorithms, genetic programming, Satellite data, Ocean prediction, Front evolution
%9 journal article
%R doi:10.1016/j.jmarsys.2005.11.017
%U http://dx.doi.org/doi:10.1016/j.jmarsys.2005.11.017
%P 299-313

%0 Book Section
%T Standard Versus Micro-Genetic Algorithms for Seismic Trace Inversion
%A Alvarez, Gabriel
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%D 2003
%8 April
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F alvarez:2003:SVMASTI
%K genetic algorithms
%P 1-10

%0 Conference Proceedings
%T Human-inspired Scaling in Learning Classifier Systems: Case Study on the n-bit Multiplexer Problem Set
%A Alvarez, Isidro M.
%A Browne, Will N.
%A Zhang, Mengjie
%Y Friedrich, Tobias
%Y Neumann, Frank
%Y Sutton, Andrew M.
%Y Middendorf, Martin
%Y Li, Xiaodong
%Y Hart, Emma
%Y Zhang, Mengjie
%Y Akimoto, Youhei
%Y Bosman, Peter A. N.
%Y Soule, Terry
%Y Miikkulainen, Risto
%Y Loiacono, Daniele
%Y Togelius, Julian
%Y Lopez-Ibanez, Manuel
%Y Hoos, Holger
%Y Handl, Julia
%Y Gomez, Faustino
%Y Fonseca, Carlos M.
%Y Trautmann, Heike
%Y Moraglio, Alberto
%Y Punch, William F.
%Y Krawiec, Krzysztof
%Y Vasicek, Zdenek
%Y Jansen, Thomas
%Y Smith, Jim
%Y Ludwig, Simone
%Y Merelo, J. J.
%Y Naujoks, Boris
%Y Alba, Enrique
%Y Ochoa, Gabriela
%Y Poulding, Simon
%Y Sudholt, Dirk
%Y Koetzing, Timo
%S GECCO ’16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation
%D 2016
%8 20 24 jul
%I ACM
%C Denver, USA
%F Alvarez:2016:GECCO
%X 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.
%K genetic algorithms, genetic programming
%R doi:10.1145/2908812.2908813
%U http://dx.doi.org/doi:10.1145/2908812.2908813
%P 429-436

%0 Conference Proceedings
%T Application of Genetic Programming to the Choice of a Structure of Global Approximations
%A Alvarez, Luis F.
%A Toropov, Vassili V.
%Y Koza, John R.
%S Late Breaking Papers at the Genetic Programming 1998 Conference
%D 1998
%8 22 25 jul
%I Stanford University Bookstore
%C University of Wisconsin, Madison, Wisconsin, USA
%F alvarez:1998:
%K genetic algorithms, genetic programming
%P 1

%0 Conference Proceedings
%T Approximation model building using genetic programming methodology: applications
%A Alvarez, Luis F.
%A Toropov, Vassili V.
%A Hughes, David C.
%A Ashour, Ashraf F.
%Y Baranger, Thouraya
%Y van Keulen, Fred
%S Second ISSMO/AIAA Internet Conference on Approximations and Fast Reanalysis in Engineering Optimization
%D 2000
%8 25 may  2 jun
%G en
%F oai:CiteSeerPSU:512359
%X 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.
%K genetic algorithms, genetic programming
%U http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/Fred4.pdf

%0 Thesis
%T Design Optimization based on Genetic Programming
%A Alvarez, L. F.
%D 2000
%C UK
%C Department of Civil and Environmental Engineering, University of Bradford
%F Alvarez:thesis
%X 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.
%K genetic algorithms, genetic programming, Design Optimization, Response Surface Methodology
%9 Ph.D. thesis
%U http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/abstract.pdf

%0 Journal Article
%T Forecasting exchange rates using genetic algorithms
%A Alvarez-Diaz, Marcos
%A Alvarez, Alberto
%J Applied Economics Letters
%D 2003
%8 apr
%V 10
%N 6
%F Alvarez-Diaz:2003:ael
%X 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.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1080/13504850210158250
%U http://dx.doi.org/doi:10.1080/13504850210158250
%P 319-322

%0 Journal Article
%T Genetic multi-model composite forecast for non-linear prediction of exchange rates
%A Alvarez-Diaz, Marcos
%A Alvarez, Alberto
%J Empirical Economics
%D 2005
%8 oct
%V 30
%N 3
%@ 0377-7332
%F Alvarez-Diaz:2005:EE
%X 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.
%K genetic algorithms, genetic programming, Composite-forecast or data-fusion, neural networks, exchange-rate forecasting
%9 journal article
%R doi:10.1007/s00181-005-0249-5
%U http://dx.doi.org/doi:10.1007/s00181-005-0249-5
%P 643-663

%0 Journal Article
%T Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery
%A Alvarez-Diaz, Marcos
%A Dominquez-Torreiro, Marcos
%J Journal of Bioeconomics
%D 2006
%8 apr
%V 8
%N 1
%@ 1387-6996
%F Alvarez-Diaz:2006:jbe
%X 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.
%K genetic algorithms, genetic programming, bioeconomic modeling, linear and non-linear forecasting
%9 journal article
%R doi:10.1007/s10818-005-0494-x
%U http://dx.doi.org/doi:10.1007/s10818-005-0494-x
%P 55-65

%0 Thesis
%T Exchange rates forecasting using nonparametric methods
%A Alvarez-Diaz, Marcos
%D 2006
%C New York, NY, USA
%C Columbia University
%F Marcos_Alvarez-Diaz:thesis
%X 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
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://search.proquest.com/docview/305345652

%0 Journal Article
%T The quality of institutions: A genetic programming approach
%A Alvarez-Diaz, Marcos
%A Caballero Miguez, Gonzalo
%J Economic Modelling
%D 2008
%V 25
%N 1
%@ 0264-9993
%F AlvarezDiaz2008161
%X 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.
%K genetic algorithms, genetic programming, Quality of institutions, Institutional determinants, Non-parametric perspective
%9 journal article
%R doi:10.1016/j.econmod.2007.05.001
%U http://www.sciencedirect.com/science/article/B6VB1-4P0VD80-1/2/c0bb8da3af64aa1ea6b0a4f90e4790b0
%U http://dx.doi.org/doi:10.1016/j.econmod.2007.05.001
%P 161-169

%0 Report
%T The institutional determinants of CO2 emissions: A computational modelling approach using Artificial Neural Networks and Genetic Programming
%A Alvarez-Diaz, Marcos
%A Caballero Miguez, Gonzalo
%A Solino, Mario
%D 2008
%8 jul
%N 401
%I Fundacion de las Cajas de Ahorros
%C Madrid
%F Alvarez-Diaz:funcas401
%K genetic algorithms, genetic programming, ANN
%9 FUNCAS Working Paper
%U https://dialnet.unirioja.es/ejemplar/212749

%0 Journal Article
%T Forecasting tourist arrivals to Balearic Islands using genetic programming
%A Alvarez-Diaz, Marcos
%A Mateu-Sbert, Josep
%A Rossello-Nadal, Jaume
%J International Journal of Computational Economics and Econometrics
%D 2009
%8 nov  06
%V 1
%N 1
%I Inderscience Publishers
%@ 1757-1189
%F Alvarez-Diaz:2009:IJCEE
%X 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.
%K genetic algorithms, genetic programming, tourism forecasting, Diebold-Mariano test, tourist arrivals, Balearic Islands, UK, United Kingdom, Germany, Spain
%9 journal article
%R doi:10.1504/IJCEE.2009.029153
%U http://www.inderscience.com/link.php?id=29153
%U http://dx.doi.org/doi:10.1504/IJCEE.2009.029153
%P 64-75

%0 Journal Article
%T On dichotomous choice contingent valuation data analysis: Semiparametric methods and Genetic Programming
%A Alvarez Diaz, Marcos
%A Gomez, Manuel Gonzalez
%A Gonzalez, Angeles Saavedra
%A De Una Alvarez, Jacobo
%J Journal of Forest Economics
%D 2010
%8 apr
%V 16
%N 2
%@ 1104-6899
%F AlvarezDiaz2009
%X 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.
%K genetic algorithms, genetic programming, Dichotomous choice contingent valuation, Genetic program, Parametric techniques, Proportional hazard model
%9 journal article
%R doi:10.1016/j.jfe.2009.02.002
%U http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1
%U http://dx.doi.org/doi:10.1016/j.jfe.2009.02.002
%P 145-156

%0 Journal Article
%T Forecasting exchange rates using local regression
%A Alvarez-Diaz, Marcos
%A Alvarez, Alberto
%J Applied Economics Letters
%D 2010
%8 mar
%V 17
%N 5
%F Alvarez-Diaz:2010:AEL
%X In this article we use a generalisation of the standard nearest neighbours, called local regression (LR), to study the predictability of the yen/US dollar and pound sterling/US dollar exchange rates. We also compare our results with those previously obtained with global methods such as neural networks, genetic programming, data fusion and evolutionary neural networks. We want to verify if we can generalise to the exchange rate forecasting problem the belief that local methods beat global ones.
%K genetic algorithms, genetic programming, local search
%9 journal article
%R doi:10.1080/13504850801987217
%U http://dx.doi.org/doi:10.1080/13504850801987217
%P 509-514

%0 Journal Article
%T Speculative strategies in the foreign exchange market based on genetic programming predictions
%A Alvarez Diaz, Marcos
%J Applied Financial Economics
%D 2010
%8 mar
%V 20
%N 6
%F Alvarez-Diaz:2010:AFE
%X In this article, we investigate the out-of-sample forecasting ability of a Genetic Program (GP) to approach the dynamic evolution of the yen/US dollar and British pound/US dollar exchange rates, and verify whether the method can beat the random walk model. Later on, we use the predicted values to generate a trading rule and we check the possibility of obtaining extraordinary profits in the foreign exchange market. Our results reveal a slight forecasting ability for one-period-ahead, which is lost when more periods ahead are considered. On the other hand, our trading strategy obtains above-normal profits. However, when transaction costs are incorporated, the profits practically disappear or become negative.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1080/09603100903459782
%U http://dx.doi.org/doi:10.1080/09603100903459782
%P 465-476

%0 Journal Article
%T The institutional determinants of CO2 emissions: a computational modeling approach using Artificial Neural Networks and Genetic Programming
%A Alvarez-Diaz, Marcos
%A Caballero-Miguez, Gonzalo
%A Solino, Mario
%J Environmetrics
%D 2011
%8 feb
%V 22
%N 1
%F Alvarez-Diaz:2011:EM
%X Understanding the complex process of climate change implies the knowledge of all possible determinants of CO2 emissions. This paper studies the influence of several institutional determinants on CO2 emissions, clarifying which variables are relevant to explain this influence. For this aim, Genetic Programming and Artificial Neural Networks are used to find an optimal functional relationship between the CO2 emissions and a set of historical, economic, geographical, religious, and social variables, which are considered as a good approximation to the institutional quality of a country. Besides this, the paper compares the results using these computational methods with that employing a more traditional parametric perspective: ordinary least squares regression (OLS). Following the empirical results of the cross-country application, this paper generates new evidence on the binomial institutions and CO2 emissions. Specifically, all methods conclude a significant influence of ethnolinguistic fractionalization (ETHF) on CO2 emissions.
%K genetic algorithms, genetic programming, artificial neural networks, ANN, computational methods, CO2 emissions, institutional determinants
%9 journal article
%R doi:10.1002/env.1025
%U https://doi.org/10.1002/env.1025
%U http://dx.doi.org/doi:10.1002/env.1025
%P 42-49

%0 Journal Article
%T Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming
%A Alvarez-Diaz, Marcos
%A Gonzalez-Gomez, Manuel
%A Otero-Giraldez, Maria Soledad
%J Forecasting
%D 2019
%V 1
%N 1
%@ 2571-9394
%F Alvarez-Diaz:2019:Forecasting
%O Special Issue Applications of Forecasting by Hybrid Artificial Intelligent Technologies
%X This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.
%K genetic algorithms, genetic programming, ANN, international tourism demand forecasting, artificial neural networks, SARIMA, spain
%9 journal article
%R doi:10.3390/forecast1010007
%U https://www.mdpi.com/2571-9394/1/1/7/
%U http://dx.doi.org/doi:10.3390/forecast1010007
%P 90-106

%0 Journal Article
%T Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods
%A Alvarez-Diaz, Marcos
%J Empirical Economics
%D 2020
%8 sep
%V 59
%F Alvarez-Diaz:2020:EE
%X Can we accurately predict the Brent oil price? If so, which forecasting method can provide the most accurate forecasts? To unravel these questions, we aim at predicting the weekly Brent oil price growth rate by using several forecasting methods that are based on different approaches. Basically, we assess and compare the out-of-sample performances of linear parametric models (the ARIMA, the ARFIMA and the autoregressive model), a nonlinear parametric model (the GARCH-in-Mean model) and different nonparametric data-driven methods (a nonlinear autoregressive artificial neural network, genetic programming and the nearest-neighbor method). The results obtained show that (1) all methods are capable of predicting accurately both the value and the directional change in the Brent oil price, (2) there are no significant forecasting differences among the methods and (3) the volatility of the series could be an important factor to enhance our predictive ability.
%K genetic algorithms, genetic programming, ANN, KNN, oil price, Forecasting, ARIMA, M-GARCH, Neural networks, Nearest-neighbour method
%9 journal article
%R doi:10.1007/s00181-019-01665-w
%U http://dx.doi.org/doi:10.1007/s00181-019-01665-w
%P 1285-1305

%0 Generic
%T Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach
%A Alves, Jeovane Honorio
%A de Oliveira, Lucas Ferrari
%D 2020
%I arXiv
%F journals/corr/abs-2005-07669
%K genetic algorithms, genetic programming, gene expression programming, GPU
%U https://arxiv.org/abs/2005.07669

%0 Generic
%T Implementing Genetic Algorithms on Arduino Micro-Controllers
%A Alves, Nuno
%D 2010
%8 feb  09
%F oai:arXiv.org:1002.2012
%X Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimisation and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimisations for low-end embedded architectures hasn’t embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.
%K genetic algorithms, computer science, neural and evolutionary computing
%U http://arxiv.org/abs/1002.2012

%0 Journal Article
%T Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming
%A Alviso, Dario
%A Artana, Guillermo
%A Duriez, Thomas
%J Fuel
%D 2020
%V 264
%@ 0016-2361
%F ALVISO:2020:Fuel
%X This paper presents regression analysis of biodiesel physico-chemical properties as a function of fatty acid composition using an experimental database. The study is done by using 48 edible and non-edible oils-based biodiesel available data. Regression equations are presented as a function of fatty acid composition (saturated and unsaturated methyl esters). The physico-chemical properties studied are kinematic viscosity, flash point, cloud point, pour point (PP), cold filter plugging point, cetane (CN) and iodine numbers. The regression technique chosen to carry out this work is genetic programming (GP). Unlike multiple linear regression (MLR) strategies available in literature, GP provides generic, non-parametric regression among variables. For all properties analyzed, the performance of the regression is systematically better for GP than MLR. Indeed, the RSME related to the experimental database is lower for GP models, from approx3percent for CN to approx55percent for PP, in comparison to the best MLR model for each property. Particularly, most GP regression models reproduce correctly the dependence of properties on the saturated and unsaturated methyl esters
%K genetic algorithms, genetic programming, Biodiesel, Fatty acid, Properties, Regression analysis
%9 journal article
%R doi:10.1016/j.fuel.2019.116844
%U http://www.sciencedirect.com/science/article/pii/S0016236119321982
%U http://dx.doi.org/doi:10.1016/j.fuel.2019.116844
%P 116844

%0 Conference Proceedings
%T Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System
%A Alyasiri, Hasanen
%A Clark, John A.
%A Kudenko, Daniel
%Y Sabourin, Christophe
%Y Guervos, Juan Julian Merelo
%Y Linares-Barranco, Alejandro
%Y Madani, Kurosh
%Y Warwick, Kevin
%S Proceedings of the 10th International Joint Conference on Computational Intelligence, IJCCI 2018, Seville, Spain, September 18-20, 2018
%D 2018
%I SciTePress
%F DBLP:conf/ijcci/AlyasiriCK18
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%R doi:10.5220/0006925901760183
%U https://doi.org/10.5220/0006925901760183
%U http://dx.doi.org/doi:10.5220/0006925901760183
%P 176-183

%0 Thesis
%T Developing Efficient and Effective Intrusion Detection System using Evolutionary Computation
%A Alyasiri, Hasanen
%D 2018
%8 nov
%C UK
%C Computer Science, University of York
%F Hasanen_Thesis_2018
%X The internet and computer networks have become an essential tool in distributed computing organisations especially because they enable the collaboration between components of heterogeneous systems. The efficiency and flexibility of online services have attracted many applications, but as they have grown in popularity so have the numbers of attacks on them. Thus, security teams must deal with numerous threats where the threat landscape is continuously evolving. The traditional security solutions are by no means enough to create a secure environment, intrusion detection systems (IDSs), which observe system works and detect intrusions, are usually used to complement other defense techniques. However, threats are becoming more sophisticated, with attackers using new attack methods or modifying existing ones. Furthermore, building an effective and efficient IDS is a challenging research problem due to the environment resource restrictions and its constant evolution. To mitigate these problems, we propose to use machine learning techniques to assist with the IDS building effort. In this thesis, Evolutionary Computation (EC) algorithms are empirically investigated for synthesising intrusion detection programs. EC can construct programs for raising intrusion alerts automatically. One novel proposed approach, i.e. Cartesian Genetic Programming, has proved particularly effective. We also used an ensemble-learning paradigm, in which EC algorithms were used as a meta-learning method to produce detectors. The latter is more fully worked out than the former and has proved a significant success. An efficient IDS should always take into account the resource restrictions of the deployed systems. Memory usage and processing speed are critical requirements. We apply a multi-objective approach to find trade-offs among intrusion detection capability and resource consumption of programs and optimise these objectives simultaneously. High complexity and the large size of detectors are identified as general issues with the current approaches. The multi-objective approach is used to evolve Pareto fronts for detectors that aim to maintain the simplicity of the generated patterns. We also investigate the potential application of these algorithms to detect unknown attacks.
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%9 Ph.D. thesis
%U http://etheses.whiterose.ac.uk/id/eprint/23699

%0 Journal Article
%T Ryan J. Urbanowicz, and Will N. Browne: Introduction to learning classifier systems Springer, 2017, 123 pp, ISBN 978-3-662-55007-6
%A Amandi, Analia
%J Genetic Programming and Evolvable Machines
%D 2018
%8 dec
%V 19
%N 4
%@ 1389-2576
%F Amandi:2018:GPEM
%O Book review
%K genetic algorithms, LCS
%9 journal article
%R doi:10.1007/s10710-018-9322-7
%U http://dx.doi.org/doi:10.1007/s10710-018-9322-7
%P 569-570

%0 Journal Article
%T Modeling viscosity of CO2 at high temperature and pressure conditions
%A Amar, Menad Nait
%A Ghriga, Mohammed Abdelfetah
%A Ouaer, Hocine
%A Seghier, Mohamed El Amine Ben
%A Pham, Binh Thai
%A Andersen, Pal Ostebo
%J Journal of Natural Gas Science and Engineering
%D 2020
%8 may
%V 77
%I HAL CCSD; Elsevier
%@ 1875-5100
%G en
%F Amar:2020:jNGSE
%X The present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.
%K genetic algorithms, genetic programming, gene expression programming, ANN, carbon dioxide, correlations, data-driven, GEP, MLP, viscosity, chemical sciences/polymers, material chemistry, physical chemistry
%9 journal article
%R doi:10.1016/j.jngse.2020.103271
%U https://hal.archives-ouvertes.fr/hal-02534736
%U http://dx.doi.org/doi:10.1016/j.jngse.2020.103271
%P 103271

%0 Journal Article
%T Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
%A Amar, Yehia
%A Schweidtmann, Artur M.
%A Deutsch, Paul
%A Cao, Liwei
%A Lapkin, Alexei
%J Chemical Science
%D 2019
%8 jul
%V 10
%N 27
%I Royal Society of Chemistry
%F Amar:2019:ChemSci
%O Edge Article
%X Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral alpha-beta unsaturated gamma-lactam. With two simultaneous objectives: high conversion and high diastereomeric excess, the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.
%K genetic algorithms, genetic programming, TPOT
%9 journal article
%R doi:10.1039/C9SC01844A
%U https://pubs.rsc.org/en/content/articlepdf/2019/sc/c9sc01844a
%U http://dx.doi.org/doi:10.1039/C9SC01844A
%P 6697-6706

%0 Thesis
%T Accelerating process development of complex chemical reactions
%A Amar, Yehia
%D 2019
%C UK
%C Department of Chemical Engineering and Biotechnology, University of Cambridge
%F Amar:thesis
%X Process development of new complex reactions in the pharmaceutical and fine chemicals industries is challenging, and expensive. The field is beginning to see a bridging between fundamental first-principles investigations, and use of data-driven statistical methods, such as machine learning. Nonetheless, process development and optimisation in these industries is mostly driven by trial-and-error, and experience. Approaches that move beyond these are limited to the well-developed optimisation of continuous variables, and often do not yield physical insights. This thesis describes several new methods developed to address research questions related to this challenge. First, we investigated whether using physical knowledge could aid statistics-guided self-optimisation of a C-H activation reaction, in which the optimisation variables were continuous. We then considered algorithmic treatment of the more challenging discrete variables, focusing on solvents. We parametrised a library of 459 solvents with physically meaningful molecular descriptors. Our case study was a homogeneous Rh-catalysed asymmetric hydrogenation to produce a chiral gamma-lactam, with conversion and diastereoselectivity as objectives. We adapted a state-of-the-art multi-objective machine learning algorithm, based on Gaussian processes, to use the descriptors as inputs, and to create a surrogate model for each objective. The aim of the algorithm was to determine a set of Pareto solutions with a minimum experimental budget, whilst simultaneously addressing model uncertainty. We found that descriptors are a valuable tool for Design of Experiments, and can produce predictive and interpretable surrogate models. Subsequently, a physical investigation of this reaction led to the discovery of an efficient catalyst-ligand system, which we studied by operando NMR, and identified a parameterised kinetic model. Turning the focus then to ligands for asymmetric hydrogenation, we calculated versatile empirical descriptors based on the similarity of atomic environments, for 102 chiral ligands, to predict diastereoselectivity. Whilst the model fit was good, it failed to accurately predict the performance of an unseen ligand family, due to analogue bias. Physical knowledge has then guided the selection of symmetrised physico-chemical descriptors. This produced more accurate predictive models for diastereoselectivity, including for an unseen ligand family. The contribution of this thesis is a development of novel and effective workflows and methodologies for process development. These open the door for process chemists to save time and resources, freeing them up from routine work, to focus instead on creatively designing new chemistry for future real-world applications.
%K molecular descriptors, design of experiments, asymmetric hydrogenation, machine learning, process development
%9 Ph.D. thesis
%R doi:10.17863/CAM.35535
%U https://www.repository.cam.ac.uk/handle/1810/288220
%U http://dx.doi.org/doi:10.17863/CAM.35535

%0 Conference Proceedings
%T An Evolutionary Approach to Complex System Regulation Using Grammatical Evolution
%A Amarteifio, Saoirse
%A O’Neill, Michael
%Y Pollack, Jordan
%Y Bedau, Mark
%Y Husbands, Phil
%Y Ikegami, Takashi
%Y Watson, Richard A.
%S Artificial Life XI Ninth International Conference on the Simulation and Synthesis of Living Systems
%D 2004
%8 December 15 sep
%I The MIT Press
%C Boston, Massachusetts
%@ 0-262-66183-7
%F amarteifio:2004:AL
%X Motivated by difficulties in engineering adaptive distributed systems, we consider a method to evolve cooperation in swarms to model dynamical systems. We consider an information processing swarm model that we find to be useful in studying control methods for adaptive distributed systems and attempt to evolve systems that form consistent patterns through the interaction of constituent agents or particles. This model considers artificial ants as walking sensors in an information-rich environment. Grammatical Evolution is combined with this swarming model as we evolve an ant’s response to information. The fitness of the swarm depends on information processing by individual ants, which should lead to appropriate macroscopic spatial and/or temporal patterns. We discuss three primary issues, which are tractability, representation and fitness evaluation of dynamical systems and show how Grammatical Evolution supports a promising approach to addressing these concerns
%K genetic algorithms, genetic programming, grammatical evolution
%U http://ncra.ucd.ie/papers/alife2004.pdf
%P 551-556

%0 Conference Proceedings
%T Coevolving Antibodies with a Rich Representation of Grammatical Evolution
%A Amarteifio, Saoirse
%A O’Neill, Michael
%Y Corne, David
%Y Michalewicz, Zbigniew
%Y Dorigo, Marco
%Y Eiben, Gusz
%Y Fogel, David
%Y Fonseca, Carlos
%Y Greenwood, Garrison
%Y Chen, Tan Kay
%Y Raidl, Guenther
%Y Zalzala, Ali
%Y Lucas, Simon
%Y Paechter, Ben
%Y Willies, Jennifier
%Y Guervos, Juan J. Merelo
%Y Eberbach, Eugene
%Y McKay, Bob
%Y Channon, Alastair
%Y Tiwari, Ashutosh
%Y Volkert, L. Gwenn
%Y Ashlock, Dan
%Y Schoenauer, Marc
%S Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%D 2005
%8 February 5 sep
%V 1
%I IEEE Press
%C Edinburgh, UK
%@ 0-7803-9363-5
%F amarteifio:2005:CEC
%X A number of natural anticipatory systems employ dual processes of feature definition and feature exploitation. Presented here, a coevolutionary dual process model based on the immune system, considers the effect of coevolving complementary templates to bias feature selection and recombination. This work considers the issue of module exploitation in evolutionary algorithms. Our approach is characterised by the use of rich representations in grammatical evolution.
%K genetic algorithms, genetic programming, grammatical evolution
%R doi:10.1109/CEC.2005.1554779
%U http://dx.doi.org/doi:10.1109/CEC.2005.1554779
%P 904-911

%0 Thesis
%T Interpreting a Genotype-Phenotype Map with Rich Representations in XMLGE
%A Amarteifio, Saoirse
%D 2005
%C University of Limerick, Ireland
%C University of Limerick
%G en
%F amarteifio:2005:IAGPMWRRIX
%X A novel XML implementation of Grammatical Evolution is developed. This has a number of interesting features such as the use of XSLT for genetic operators and the use of reflection to build an object tree from an XML expression tree. This framework is designed to be used for remote or local evaluation of evolved program structures and provides a number of abstraction layers for program evaluation and evolution. A dynamical swarm system is evolved as a special-case function induction problem to illustrate the application of XMLGE. Particle behaviours are evolved to optimise colony performance. A dual process evolutionary algorithm based on the immune system using rich representations is developed. A dual process feature detection and feature integration model is described and the performance shown on benchmark GP problems. An adaptive feature detection method uses coevolving XPath antibodies to take selective interest in primary structures. Grammars are used to generate reciprocal binding structures (antibodies) given any primary domain grammar. A codon compression algorithm is developed which shows performance improvements on symbolic regression and multiplexer problems. The algorithm is based on questions about the information content of a genome. This also exploits information from the rich representation of XMLGE.
%K genetic algorithms, genetic programming, grammatical evolution, xml
%9 Master of Science in Computer Science
%9 Masters thesis
%U http://ncra.ucd.ie/downloads/pub/SaoirseMScThesis.pdf

%0 Journal Article
%T Electricity consumption forecasting models for administration buildings of the UK higher education sector
%A Amber, K. P.
%A Aslam, M. W.
%A Hussain, S. K.
%J Energy and Buildings
%D 2015
%V 90
%@ 0378-7788
%F Amber:2015:EB
%X Electricity consumption in the administration buildings of a typical higher education campus in the UK accounts for 26percent of the campus annual electricity consumption. A reliable forecast of electricity consumption helps energy managers in numerous ways such as in preparing future energy budgets and setting up energy consumption targets. In this paper, we developed two models, a multiple regression (MR) model and a genetic programming (GP) model to forecast daily electricity consumption of an administration building located at the Southwark campus of London South Bank University in London. Both models integrate five important independent variables, i.e. ambient temperature, solar radiation, relative humidity, wind speed and weekday index. Daily values of these variables were collected from year 2007 to year 2013. The data sets from year 2007 to 2012 are used for training the models while 2013 data set is used for testing the models. The predicted test results for both the models are analysed and compared with actual electricity consumption. At the end, some conclusions are drawn about the performance of both models regarding their forecasting capabilities. The results demonstrate that the GP model performs better with a Total Absolute Error (TAE) of 6percent compared to TAE of 7percent for MR model.
%K genetic algorithms, genetic programming, Electricity forecasting, Administration buildings, Multiple regression
%9 journal article
%R doi:10.1016/j.enbuild.2015.01.008
%U http://www.sciencedirect.com/science/article/pii/S0378778815000110
%U http://dx.doi.org/doi:10.1016/j.enbuild.2015.01.008
%P 127-136

%0 Journal Article
%T Intelligent techniques for forecasting electricity consumption of buildings
%A Amber, K. P.
%A Ahmad, R.
%A Aslam, M. W.
%A Kousar, A.
%A Usman, M.
%A Khan, M. S.
%J Energy
%D 2018
%V 157
%@ 0360-5442
%F AMBER:2018:Energy
%X The increasing trend in building sector’s energy demand calls for reliable and robust energy consumption forecasting models. This study aims to compare prediction capabilities of five different intelligent system techniques by forecasting electricity consumption of an administration building located in London, United Kingdom. These five techniques are; Multiple Regression (MR), Genetic Programming (GP), Artificial Neural Network (ANN), Deep Neural Network (DNN) and Support Vector Machine (SVM). The prediction models are developed based on five years of observed data of five different parameters such as solar radiation, temperature, wind speed, humidity and weekday index. Weekday index is an important parameter introduced to differentiate between working and non-working days. First four years data is used for training the models and to obtain prediction data for fifth year. Finally, the predicted electricity consumption of all models is compared with actual consumption of fifth year. Results demonstrate that ANN performs better than all other four techniques with a Mean Absolute Percentage Error (MAPE) of 6percent whereas MR, GP, SVM and DNN have MAPE of 8.5percent, 8.7percent, 9percent and 11percent, respectively. The applicability of this study could span to other building categories and will help energy management teams to forecast energy consumption of various buildings
%K genetic algorithms, genetic programming, Electricity forecasting, ANN, DNN, GP, MR, SVM
%9 journal article
%R doi:10.1016/j.energy.2018.05.155
%U http://www.sciencedirect.com/science/article/pii/S036054421830999X
%U http://dx.doi.org/doi:10.1016/j.energy.2018.05.155
%P 886-893

%0 Journal Article
%T Investigation of shear strength correlations and reliability assessments of sandwich structures by kriging method
%A Ameryan, Ala
%A Ghalehnovi, Mansour
%A Rashki, Mohsen
%J Composite Structures
%D 2020
%V 253
%@ 0263-8223
%F AMERYAN:2020:CS
%X Steel-concrete-steel (SCS) sandwich composite structure with corrugated-strip connectors (CSC) is the promising structure which is applied in offshore and building structures. The behavior prediction of shear connections is of major importance in SCS structures. The present study evaluated the existing shear strength correlations of SCS sandwich structures exploiting experimental data and Finite Element Analysis (FEA). The considered system is a double steel skin sandwich structure with CSC (DSCS). Due to the limitation of the literature regarding CSC development, some new correlations were proposed and studied relying on several FEA results through the Genetic Programming method. The accuracy of the estimated shear strength predicted by the existing and proposed equations was evaluated using the FEA data and push-out test results. The FE models were verified through experimental data. Moreover, the correlations were investigated based on reliability assessment due to the high importance of the reliability analysis of such structures. Given that high accuracy in estimating the shear strength fails to necessarily lead to acceptable results in structural reliability analysis, the reliability of the existing and proposed equations was evaluated using the Kriging model by considering experimental data. This meta-model could predict accurate values with a limited number of initial training samples
%K genetic algorithms, genetic programming, Structural reliability, Kriging, Sandwich structures, Finite element, Experimental data, Failure probability
%9 journal article
%R doi:10.1016/j.compstruct.2020.112782
%U http://www.sciencedirect.com/science/article/pii/S0263822320327082
%U http://dx.doi.org/doi:10.1016/j.compstruct.2020.112782
%P 112782

%0 Journal Article
%T Rule-centred genetic programming (RCGP): an imperialist competitive approach
%A Amini, Seyed Mohammad Hossein Hosseini
%A Abdollahi, Mohammad
%A Haeri, Maryam Amir
%J Appl. Intell.
%D 2020
%V 50
%N 8
%F DBLP:journals/apin/AminiAH20
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s10489-019-01601-6
%U https://doi.org/10.1007/s10489-019-01601-6
%U http://dx.doi.org/doi:10.1007/s10489-019-01601-6
%P 2589-2609

%0 Journal Article
%T A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method
%A Aminian, Pejman
%A Javid, Mohamad Reza
%A Asghari, Abazar
%A Gandomi, Amir Hossein
%A Arab Esmaeili, Milad
%J Neural Computing and Applications
%D 2011
%V 20
%N 8
%I Springer
%@ 0941-0643
%F journals/nca/AminianJAGE11
%X This study presents a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. The base shear of steel frames was formulated in terms of the number of bays, number of storey, soil type, and situation of braced or unbraced. A classical GP model was developed to benchmark the GP/SA model. The comprehensive database used for the development of the correlations was obtained from finite element analysis. A parametric analysis was carried out to evaluate the sensitivity of the base shear to the variation of the influencing parameters. The GP/SA and classical GP correlations provide a better prediction performance than the widely used UBC code and a neural network-based model found in the literature. The developed correlations may be used as quick checks on solutions developed by deterministic analyses.
%K genetic algorithms, genetic programming, base shear, steel frame structures, simulated annealing, nonlinear modelling
%9 journal article
%R doi:10.1007/s00521-011-0689-0
%U http://dx.doi.org/doi:10.1007/s00521-011-0689-0
%P 1321-1332

%0 Journal Article
%T New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach
%A Aminian, Pejman
%A Niroomand, Hadi
%A Gandomi, Amir Hossein
%A Alavi, Amir Hossein
%A Arab Esmaeili, Milad
%J Neural Computing and Applications
%D 2013
%8 jul
%V 23
%N 1
%I Springer
%@ 0941-0643
%G English
%F Aminian:2013:NCA
%X This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalisation capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.
%K genetic algorithms, genetic programming, Linear genetic programming, Castellated beam, Load carrying capacity, Simulated annealing, Formulation
%9 journal article
%R doi:10.1007/s00521-012-1138-4
%U http://link.springer.com/article/10.1007%2Fs00521-012-1138-4
%U http://dx.doi.org/doi:10.1007/s00521-012-1138-4
%P 119-131

%0 Book Section
%T Statistical Genetic Programming: The Role of Diversity
%A Amir Haeri, Maryam
%A Ebadzadeh, Mohammad Mehdi
%A Folino, Gianluigi
%E Snasel, Vaclav
%E Kroemer, Pavel
%E Koeppen, Mario
%E Schaefer, Gerald
%B Soft Computing in Industrial Applications
%S Advances in Intelligent Systems and Computing
%D 2014
%8 21 nov
%V 223
%I Springer
%G English
%F AmirHaeri:wsc17
%O Proceedings of the 17th Online World Conference on Soft Computing in Industrial Applications
%X In this chapter, a new GP-based algorithm is proposed. The algorithm, named SGP (Statistical GP), exploits statistical information, i.e. mean, variance and correlation-based operators, in order to improve the GP performance. SGP incorporates new genetic operators, i.e. Correlation Based Mutation, Correlation Based Crossover, and Variance Based Editing, to drive the search process towards fitter and shorter solutions. Furthermore, this work investigates the correlation between diversity and fitness in SGP, both in terms of phenotypic and genotypic diversity. First experiments conducted on four symbolic regression problems illustrate the goodness of the approach and permits to verify the different behaviour of SGP in comparison with standard GP from the point of view of the diversity and its correlation with the fitness.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-00930-8_4
%U http://dx.doi.org/10.1007/978-3-319-00930-8_4
%U http://dx.doi.org/doi:10.1007/978-3-319-00930-8_4
%P 37-48

%0 Journal Article
%T Improving GP generalization: a variance-based layered learning approach
%A Amir Haeri, Maryam
%A Ebadzadeh, Mohammad Mehdi
%A Folino, Gianluigi
%J Genetic Programming and Evolvable Machines
%D 2015
%8 mar
%V 16
%N 1
%@ 1389-2576
%F AmirHaeri:2014:GPEM
%X This paper introduces a new method that improves the generalisation ability of genetic programming (GP) for symbolic regression problems, named variance-based layered learning GP. In this approach, several datasets, called primitive training sets, are derived from the original training data. They are generated from less complex to more complex, for a suitable complexity measure. The last primitive dataset is still less complex than the original training set. The approach decomposes the evolution process into several hierarchical layers. The first layer of the evolution starts using the least complex (smoothest) primitive training set. In the next layers, more complex primitive sets are given to the GP engine. Finally, the original training data is given to the algorithm. We use the variance of the output values of a function as a measure of the functional complexity. This measure is used in order to generate smoother training data, and controlling the functional complexity of the solutions to reduce the overfitting. The experiments, conducted on four real-world and three artificial symbolic regression problems, demonstrate that the approach enhances the generalization ability of the GP, and reduces the complexity of the obtained solutions.
%K genetic algorithms, genetic programming, VBLL-GP, Generalisation, Layered learning, Over fitting, Variance
%9 journal article
%R doi:10.1007/s10710-014-9220-6
%U http://dx.doi.org/doi:10.1007/s10710-014-9220-6
%P 27-55

%0 Journal Article
%T Statistical genetic programming for symbolic regression
%A Haeri, Maryam Amir
%A Ebadzadeh, Mohammad Mehdi
%A Folino, Gianluigi
%J Applied Soft Computing
%D 2017
%8 nov
%V 60
%F journals/asc/HaeriEF17
%X In this paper, a new genetic programming (GP) algorithm for symbolic regression problems is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical information (such as variance, mean and correlation coefficient) to improve GP. To this end, we define well-structured trees as a tree with the following property: nodes which are closer to the root have a higher correlation with the target. It is shown experimentally that on average, the trees with structures closer to well-structured trees are smaller than other trees. SGP biases the search process to find solutions whose structures are closer to a well-structured tree. For this purpose, it extends the terminal set by some small well-structured subtrees, and starts the search process in a search space that is limited to semi-well-structured trees (i.e., trees with at least one well-structured subtree). Moreover, SGP incorporates new genetic operators, i.e., correlation-based mutation and correlation-based crossover, which use the correlation between outputs of each subtree and the targets, to improve the functionality. Furthermore, we suggest a variance-based editing operator which reduces the size of the trees. SGP uses the new operators to explore the search space in a way that it obtains more accurate and smaller solutions in less time. SGP is tested on several symbolic regression benchmarks. The results show that it increases the evolution rate, the accuracy of the solutions, and the generalization ability, and decreases the rate of code growth.
%K genetic algorithms, genetic programming, Symbolic regression, Well-structured subtree, Semi-well-structured tree, Well-structuredness measure, Correlation coefficient
%9 journal article
%R doi:10.1016/j.asoc.2017.06.050
%U http://dx.doi.org/doi:10.1016/j.asoc.2017.06.050
%P 447-469

%0 Journal Article
%T Ground motion prediction equations (GMPEs) for elastic response spectra in the Iranian plateau using Gene Expression Programming (GEP)
%A Amiri, Gholamreza Ghodrati
%A Amiri, Mohamad Shamekhi
%A Tabrizian, Zahra
%J Journal of Intelligent and Fuzzy Systems
%D 2014
%V 26
%N 6
%F journals/jifs/AmiriAT14
%X This paper proposes ground-motion prediction equations (GMPEs) for the horizontal component of earthquake in Iranian plateau. These equations present the velocity and acceleration response spectra at 5percent damping ratio as continuous period functions, within range of 0.1 to 4 seconds. So far many equations have been presented and the recent suggested proportions are functions of several parameters. In this research, due to easy usage and lack of information in Iran, only the magnitude of earthquake, the distance between earthquake source and the location and the ground type are used as important factors. Iranian plateau is divided into two zones: Alborz-Central Iran and Zagros, each of which is divided into rock and soil region according to the ground type. Regarding the fact that the occurred and reported earthquakes in Iran are shallow, surface wave magnitude (Ms) is used in this study. Moreover, hypocentral distance is considered as distance between the earthquake source and the location. To obtain the velocity and acceleration response spectra, a Gene Expression Programming (GEP) algorithm is used which uses no constant regression model and the model is acquired smartly as a continuous period function. The consequences show a consistency with high proportionality coefficient among the observed and anticipated results
%K genetic algorithms, genetic programming, gene expression programming
%9 journal article
%R doi:10.3233/IFS-130950
%U http://dx.doi.org/10.3233/IFS-130950
%U http://dx.doi.org/doi:10.3233/IFS-130950
%P 2825-2839

%0 Journal Article
%T Modeling intermolecular potential of He-F2 dimer from symmetry-adapted perturbation theory using multi-gene genetic programming
%A Amiri, M.
%A Eftekhari, M.
%A Dehestani, M.
%A Tajaddini, A.
%J Scientia Iranica
%D 2013
%V 20
%N 3
%@ 1026-3098
%F Amiri:2013:SI
%X Abstract Any molecular dynamical calculation requires a precise knowledge of interaction potential as an input. In an appropriate form, such that the potential, with respect to the coordinates, can be evaluated easily and accurately at arbitrary geometries (in our study parameters for geometry are R and theta), a good potential energy expression can offer the exact intermolecular behaviour of systems. There are many methods to create mathematical expressions for the potential energy. In this study for the first time, we used the Multi-gene Genetic Programming (MGGP) method to generate a potential energy model for the He-F2 system. The MGGP method is one of the most powerful methods used for non-linear regression problems. A dataset of size 714 created by the SAPT 2008 program is used to generate models of MGGP. The results obtained show the power of MGGP for producing an efficient nonlinear regression model, in terms of accuracy and complexity.
%K genetic algorithms, genetic programming, Potential energy, SAPT, MGGP, Lennard-Jones potential
%9 journal article
%R doi:10.1016/j.scient.2012.12.040
%U http://www.sciencedirect.com/science/article/pii/S1026309813000758
%U http://dx.doi.org/doi:10.1016/j.scient.2012.12.040
%P 543-548

%0 Journal Article
%T Shape Quantization and Recognition with Randomized Trees
%A Amit, Yali
%A Geman, Donald
%J Neural Computation
%D 1997
%8 oct
%V 9
%N 7
%F nc:Amit+Geman:1997
%X We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labelled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred LATeX symbols. State-of-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on LATeX symbols is to analyse invariance, generalisation error and related issues, and a comparison with artificial neural networks methods is presented in this context.
%9 journal article
%P 1545-1588

%0 Journal Article
%T Multi-agent architecture for Multiaobjective optimization of Flexible Neural Tree
%A Ammar, Marwa
%A Bouaziz, Souhir
%A Alimi, Adel M.
%A Abraham, Ajith
%J Neurocomputing
%D 2016
%V 214
%@ 0925-2312
%F Ammar:2016:Neurocomputing
%X In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)’ training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi-Dimensional Particle Swarm Optimization ( PMD _ PSO ) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree ( PMA _ FNT ). To assess the effectiveness of PMA _ FNT , four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature.
%K genetic algorithms, genetic programming, Flexible Neural Tree, Multi-agent architecture, Multi-objective optimization, Evolutionary Computation algorithms, Negotiation, Classification
%9 journal article
%R doi:10.1016/j.neucom.2016.06.019
%U http://www.sciencedirect.com/science/article/pii/S0925231216306579
%U http://dx.doi.org/doi:10.1016/j.neucom.2016.06.019
%P 307-316

%0 Journal Article
%T Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework
%A Amoretti, Michele
%J Genetic Programming and Evolvable Machines
%D 2013
%8 jun
%V 14
%N 2
%@ 1389-2576
%F Amoretti:2013:GPEM
%X A peer-to-peer (P2P) network is a complex system whose elements (peer nodes, or simply peers) cooperate to implement scalable distributed services. From a general point of view, the activities of a P2P system are consequences of external inputs coming from the environment, and of the internal feedback among nodes. The reaction of a peer to direct or indirect inputs from the environment is dictated by its functional structure, which is usually defined in terms of static rules (protocols) shared among peers. The introduction of artificial evolution mechanisms may improve the efficiency of P2P networks, with respect to resource consumption, while preserving high performance in response to the environmental needs. In this paper, we propose the distributed remodelling framework (DRF), a general approach for the design of efficient environment-driven peer-to-peer networks. As a case study, we consider an ultra-large-scale storage and computing system whose nodes perform lookups for resources provided by other nodes, to cope with task execution requests that cannot be fulfilled locally. Thanks to the DRF, workload modifications trigger reconfigurations at the level of single peers, from which global system adaptation emerges without centralised control.
%K genetic algorithms, Peer-to-peer network, Artificial evolution, Complex adaptive system
%9 journal article
%R doi:10.1007/s10710-013-9182-0
%U http://dx.doi.org/doi:10.1007/s10710-013-9182-0
%P 127-153

%0 Conference Proceedings
%T DNA Simulation of Boolean Circuits
%A Amos, Martyn
%A Dunne, Paul E.
%A Gibbons, Alan
%Y Koza, John R.
%Y Banzhaf, Wolfgang
%Y Chellapilla, Kumar
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max H.
%Y Goldberg, David E.
%Y Iba, Hitoshi
%Y Riolo, Rick
%S Genetic Programming 1998: Proceedings of the Third Annual Conference
%D 1998
%8 22 25 jul
%I Morgan Kaufmann
%C University of Wisconsin, Madison, Wisconsin, USA
%@ 1-55860-548-7
%F amos:1998:DNAsbc
%K DNA Computing
%P 679-683

%0 Conference Proceedings
%T Automatic generation of Lyapunov function using Genetic programming approach
%A Amte, A. Y.
%A Kate, P. S.
%S 2015 International Conference on Energy Systems and Applications
%D 2015
%8 oct
%F Amte:2015:ICESA
%X The paper introduces a novel approach for the automated generation of a Lyapunov function for the analysis of a given dynamic system using genetic programming (GP). Genetic programming is a branch of Genetic algorithm. It introduces the concept of GP for the automation of Lyapunov function in MATLAB used for various optimisation techniques. A Lyapunov function method used for transient stability assessment is discussed and hence discussion followed by the establishment of domain of attraction of stable equilibrium point. The results obtained by MATLAB coding for the generation of Lyapunov function of single machine infinite bus system is related by considering a ball rolling on the inner surface of a bowl which depicted in edition of Power System Analysis and Control.
%K genetic algorithms, genetic programming
%R doi:10.1109/ICESA.2015.7503454
%U http://dx.doi.org/doi:10.1109/ICESA.2015.7503454
%P 771-775

%0 Conference Proceedings
%T PyGGI: Python General framework for Genetic Improvement
%A An, Gabin
%A Kim, Jinhan
%A Lee, Seongmin
%A Yoo, Shin
%S Proceedings of Korea Software Congress
%S KSC 2017
%D 2017
%8 20 22 dec
%C Busan, South Korea
%F An2017aa
%X We present Python General Framework for Genetic Improvement (PYGGI, pronounced pigi), a lightweight general framework for Genetic Improvement (GI). It is designed to be a simple and easy to configure GI tool for multiple programming languages such as Java, C, or Python. Through two case studies, we show that PYGGI can modify source code of a given program either to improve non-functional properties or to automatically repair functional faults.
%K genetic algorithms, genetic programming, Genetic Improvement
%U https://coinse.kaist.ac.kr/publications/pdfs/An2017aa.pdf
%P 536-538

%0 Conference Proceedings
%T Comparing Line and AST Granularity Level for Program Repair using PyGGI
%A An, Gabin
%A Kim, Jinhan
%A Yoo, Shin
%Y Petke, Justyna
%Y Stolee, Kathryn
%Y Langdon, William B.
%Y Weimer, Westley
%S GI-2018, ICSE workshops proceedings
%D 2018
%8 February
%I ACM
%C Gothenburg, Sweden
%F An:2018:GI
%X PyGGI is a lightweight Python framework that can be used to implement generic Genetic Improvement algorithms at the API level. The original version of PyGGI only provided lexical modifications, i.e., modifications of the source code at the physical line granularity level. This paper introduces new extensions to PyGGI that enables syntactic modifications for Python code, i.e., modifications that operates at the AST granularity level. Taking advantage of the new extensions, we also present a case study that compares the lexical and syntactic search granularity level for automated program repair, using ten seeded faults in a real world open source Python project. The results show that search landscapes at the AST granularity level are more effective (i.e. eventually more likely to produce plausible patches) due to the smaller sizes of ingredient spaces (i.e., the space from which we search for the material to build a patch), but may require longer time for search because the larger number of syntactically intact candidates leads to more fitness evaluations.
%K genetic algorithms, genetic programming, genetic improvement, APR, SBSE
%R doi:10.1145/3194810.3194814
%U http://geneticimprovementofsoftware.com/wp-content/uploads/2018/04/An_2018_GI.pdf
%U http://dx.doi.org/doi:10.1145/3194810.3194814
%P 19-26

%0 Journal Article
%T Genetic Improvement Workshop at ICSE 2018
%A An, Gabin
%J SIGEVOlution
%D 2018
%8 dec
%V 11
%N 4
%F An:2018:sigevolution
%X In Gothenburg, on 2nd June 2018, the fourth edition of Genetic Improvement (GI) Workshop was co-located with this year’s ICSE (International Conference on Software Engineering), the biggest and probably the most prestigious software engineering conference...
%K genetic algorithms, genetic programming, genetic improvement
%9 journal article
%R doi:10.1145/3302542.3302544
%U http://www.sigevolution.org/issues/SIGEVOlution1104.pdf
%U http://dx.doi.org/doi:10.1145/3302542.3302544
%P 11-13

%0 Conference Proceedings
%T PyGGI 2.0: Language Independent Genetic Improvement Framework
%A An, Gabin
%A Blot, Aymeric
%A Petke, Justyna
%A Yoo, Shin
%Y Apel, Sven
%Y Russo, Alessandra
%S Proceedings of the 27th Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering ESEC/FSE 2019)
%D 2019
%8 aug 26–30
%I ACM
%C Tallinn, Estonia
%F an:2019:fse
%X PyGGI is a research tool for Genetic Improvement (GI), that is designed to be versatile and easy to use. We present version 2.0 of PyGGI, the main feature of which is an XML-based intermediate program representation. It allows users to easily define GI operators and algorithms that can be reused with multiple target languages. Using the new version of PyGGI, we present two case studies. First, we conduct an Automated Program Repair (APR) experiment with the QuixBugs benchmark, one that contains defective programs in both Python and Java. Second, we replicate an existing work on runtime improvement through program specialisation for the MiniSAT satisfiability solver. PyGGI 2.0 was able to generate a patch for a bug not previously fixed by any APR tool. It was also able to achieve 14percent runtime improvement in the case of MiniSAT. The presented results show the applicability and the expressiveness of the new version of PyGGI. A video of the tool demo is at: https://youtu.be/PxRUdlRDS40
%K genetic algorithms, genetic programming, Genetic Improvement, APR, SBSE, XML, srcML, Python
%R doi:10.1145/3338906.3341184
%U http://www.cs.ucl.ac.uk/staff/a.blot/files/an_2019_fse.pdf
%U http://dx.doi.org/doi:10.1145/3338906.3341184
%P 1100-1104

%0 Conference Proceedings
%T Adaptive user similarity measures for recommender systems: A genetic programming approach
%A Anand, Deepa
%A Bharadwaj, K. K.
%S 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010)
%D 2010
%8 September 11 jul
%V 8
%F Anand:2010:ICCSIT
%X Recommender systems signify the shift from the paradigm of searching for items to discovering items and have been employed by an increasing number of e-commerce sites for matching users to their preferences. Collaborative Filtering is a popular recommendation technique which exploits the past user-item interactions to determine user similarity. The preferences of such similar users are leveraged to offer suggestions to the active user. Even though several techniques for similarity assessment have been suggested in literature, no technique has been proven to be optimal under all contexts/data conditions. Hence, we propose a two-stage process to assess user similarity, the first is to learn the optimal transformation function to convert the raw ratings data to preference data by employing genetic programming, and the second is to use the preference values, so derived, to compute user similarity. The application of such learnt user bias gives rise to adaptive similarity measures, i.e. similarity estimates that are dataset dependent and hence expected to work best under any data environment. We demonstrate the superiority of our proposed technique by contrasting it to traditional similarity estimation techniques on four different datasets representing varied data environments.
%K genetic algorithms, genetic programming, adaptive user similarity measure, collaborative filtering, data environment, item discovery, item searching, optimal transformation function, preference value, raw ratings data, recommender system, similarity assessment, similarity estimation, user-item interaction, groupware, information filtering, recommender systems
%R doi:10.1109/ICCSIT.2010.5563737
%U http://dx.doi.org/doi:10.1109/ICCSIT.2010.5563737
%P 121-125

%0 Thesis
%T Enhancing Accuracy of Recommender Systems through various approaches to Local and Global Similarity Measures
%A Anand, Deepa
%D 2011
%8 jul
%C New Delhi, India
%C Computer and System Sciences, Jawaharlal Nehru University
%F Anand:thesis
%X Web 2.0 represents a paradigm shift in the way that internet is consumed. Users’ role has evolved from that of passive consumers of content to active prosumers, implying a plethora of information sources and an ever increasing ocean of content. Collaborative Recommender systems have thus emerged as Web 2.0 personalisation tools which aid users in grappling with the overload of information by allowing the discovery of content in contrast to plain search popularised by prior web technologies. To this end Collaborative filtering (CF) exploit the preferences of users who have liked similar items in the past to help a user to identify interesting products and services. The success of CF algorithms, however, is hugely dependent on the technique designed to determine the set of users whose opinion is sought. Traditionally user closeness is assessed by matching their preferences on a set of common experiences that both share. The challenge with this kind of computation is the overabundance of available content to be experienced, at the user’s disposal, thus rendering the user-preference space very sparse. The similarity so computed is thus unstable for user pairs sharing a small set of experiences and is in fact incomputable for most user pairs due to a lack of expressed common preferences. To remedy the sparsity problems we propose methods to enrich the set of user connections obtained using measures such as Pearson Correlation Coefficient (PCC) and Cosine Similarity (COS). We achieve this by leveraging on explicit trust elicitation and trust transitivity. When interacting with anonymous users online, in the absence of physical cues apparent in our daily life, trust provides a reliable measure of quality and guides the user decision process on whether or not to interact with an entity. These trust statements in addition to identifying malicious users also enhance user connectivity by establishing links between pairs of users whose closeness cannot be determined through preference data. In addition transitivity of trust can also be leveraged to further expand the set of neighbours to collaborate with. We first explore a bifurcated view of trust: functional and referral trust i.e. trust in an entity to recommend items and the trust in an entity to recommend recommenders and propose models to quantify referral trust. Such a referral-functional trust framework leads to more meaningful derivation of trust through transitivity resulting in better quality recommendations. Though trust has been extensively used in literature to support the CF process, distrust information has been explored very little in this context. We thus propose a tri-component computation of trust and distrust using preference, functional trust and referral trust in order to densify the network of user interconnections. To maintain a balance between increased coverage and the quality of recommendations, however, we quantify risk measures for each trust and distrust relationship so derived and prune the network to retain high quality relationships thus ensuring good connections formed between users through transitivity of trust and distrust. In the absence of supplemental information such as trust/distrust to provide extra knowledge about user links the local similarity connections can be harnessed to deem a pair of users similar if they are share preferences with the same set of users thus estimating the global similarity between user pairs. We investigate the effectiveness of various graph based global or indirect similarity computation schemes in enhancing the user or item neighborhood thus bettering the quality and number of recommendations obtained.
%K genetic algorithms, genetic programming, recommender systems
%9 Ph.D. thesis
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Anand_thesis.pdf

%0 Journal Article
%T Feature Extraction for Collaborative Filtering: A Genetic Programming Approach
%A Anand, Deepa
%J International Journal of Computer Science Issues
%D 2012
%8 sep
%V 9
%N 5
%I IJCSI Press
%@ 16940784
%G eng
%F Anand:2012:IJCSI
%X Collaborative filtering systems offer customised recommendations to users by exploiting the interrelationships between users and items. Users are assessed for their similarity in tastes and items preferred by similar users are offered as recommendations. However scalability and scarcity of data are the two major bottlenecks to effective recommendations. With web based RS typically having users in order of millions, timely recommendations pose a major challenge. Sparsity of ratings data also affects the quality of suggestions. To alleviate these problems we propose a genetic programming approach to feature extraction by employing GP to convert from user-item space to user-feature preference space where the feature space is much smaller than the item space. The advantage of this approach lies in the reduction of sparse high dimensional preference information into a compact and dense low dimensional preference data. The features are constructed using GP and the individuals are evolved to generate the most discriminative set of features. We compare our approach to content based feature extraction approach and demonstrate the effectiveness of the GP approach in generating the optimal feature set.
%K genetic algorithms, genetic programming, Recommender Systems, Collaborative Filtering, Feature Extraction
%9 journal article
%U http://www.ijcsi.org/contents.php?volume=9&&issue=5
%P 348-354

%0 Journal Article
%T Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina
%A Anders, Torsten
%A Inden, Benjamin
%J PeerJ Comput. Sci.
%D 2019
%V 5
%F DBLP:journals/peerj-cs/AndersI19
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.7717/peerj-cs.244
%U https://doi.org/10.7717/peerj-cs.244
%U http://dx.doi.org/doi:10.7717/peerj-cs.244
%P e244

%0 Journal Article
%T Machine learning of symbolic compositional rules with genetic programming: Dissonance treatment in Palestrina
%A Anders, Torsten
%A Inden, Benjamin
%J PeerJ Prepr.
%D 2019
%V 7
%F DBLP:journals/peerjpre/AndersI19
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.7287/peerj.preprints.27731v1
%U https://doi.org/10.7287/peerj.preprints.27731v1
%U http://dx.doi.org/doi:10.7287/peerj.preprints.27731v1
%P e27731

%0 Report
%T Courage in Profiling
%A Anderson, Kenneth R.
%D 1994
%8 28 jul
%I BBN
%F anderson:1994:profile
%K genetic algorithms, genetic programming, CASCOR1
%U http://openmap.bbn.com/~kanderso/performance/postscript/courage-in-profiles.ps

%0 Conference Proceedings
%T Off-Line Evolution of Behaviour for Autonomous Agents in Real-Time Computer Games
%A Anderson, Eike Falk
%Y Merelo-Guervos, Juan J.
%Y Adamidis, Panagiotis
%Y Beyer, Hans-Georg
%Y Fernandez-Villacanas, Jose-Luis
%Y Schwefel, Hans-Paul
%S Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%D 2002
%8 July 11 sep
%N 2439
%I Springer-Verlag
%C Granada, Spain
%@ 3-540-44139-5
%F anderson:ppsn2002:pp689
%X This paper describes and analyses a series of experiments intended to evolve a player for a variation of the classic arcade game Asteroids TM using steady state genetic programming. The player’s behaviour is defined using a LISP like scripting language. While the game interprets scripts in real-time, such scripts are evolved off-line by a second program which simulates the realtime application. This method is used, as on-line evolution of the players would be too time consuming. A successful player needs to satisfy multiple conflicting objectives. This problem is addressed by the use of an automatically defined function (ADF) for each of these objectives in combination with task specific fitness functions. The overall fitness of evolved scripts is evaluated by a conventional fitness function. In addition to that, each of the ADFs is evaluated with a separate fitness function, tailored specifically to the objective that needs to be satisfied by that ADF.
%K genetic algorithms, genetic programming, Games, Machine Learning, Fitness Evaluation
%R doi:10.1007/3-540-45712-7_66
%U http://dx.doi.org/doi:10.1007/3-540-45712-7_66
%P 689-699

%0 Conference Proceedings
%T Reactive and Memory-Based Genetic Programming for Robot Control
%A Andersson, Bjorn
%A Svensson, Per
%A Nordin, Peter
%A Nordahl, Mats
%Y Poli, Riccardo
%Y Nordin, Peter
%Y Langdon, William B.
%Y Fogarty, Terence C.
%S Genetic Programming, Proceedings of EuroGP’99
%S LNCS
%D 1999
%8 26 27 may
%V 1598
%I Springer-Verlag
%C Goteborg, Sweden
%@ 3-540-65899-8
%F andersson:1999:rmbGPrc
%X In this paper we introduce a new approach to genetic programming with memory in reinforcement learning situations, which selects memories in order to increase the probability of modelling the most relevant parts of memory space. We evolve maps directly from state to action, rather than maps that predict reward based on state and action, which reduces the complexity of the evolved mappings. The work is motivated by applications to the control of autonomous robots. Preliminary results in software simulations indicate an enhanced learning speed and quality.
%K genetic algorithms, genetic programming: Poster
%R doi:10.1007/3-540-48885-5_13
%U http://dx.doi.org/doi:10.1007/3-540-48885-5_13
%P 161-172

%0 Conference Proceedings
%T On-line Evolution of Control for a Four-Legged Robot Using Genetic Programming
%A Andersson, Bjorn
%A Svensson, Per
%A Nordin, Peter
%A Nordahl, Mats
%Y Cagnoni, Stefano
%Y Poli, Riccardo
%Y Smith, George D.
%Y Corne, David
%Y Oates, Martin
%Y Hart, Emma
%Y Lanzi, Pier Luca
%Y Willem, Egbert Jan
%Y Li, Yun
%Y Paechter, Ben
%Y Fogarty, Terence C.
%S Real-World Applications of Evolutionary Computing
%S LNCS
%D 2000
%8 17 apr
%V 1803
%I Springer-Verlag
%C Edinburgh
%@ 3-540-67353-9
%F andersson:2000:4lrGP
%X We evolve a robotic controller for a four-legged real robot enabling it to walk dynamically. Evolution is performed on-line by a linear machine code GP system. The robot has eight degrees of freedom and is built from standard R/C servos. Different walking strategies are shown by the robot during evolution and the evolving system is robust against mechanical failures.
%K genetic algorithms, genetic programming, linear GP
%R doi:10.1007/3-540-45561-2_31
%U http://dx.doi.org/doi:10.1007/3-540-45561-2_31
%P 319-326

%0 Conference Proceedings
%T Evolving Coupled Map Lattices for Computation
%A Andersson, Claes
%A Nordahl, Mats G.
%Y Banzhaf, Wolfgang
%Y Poli, Riccardo
%Y Schoenauer, Marc
%Y Fogarty, Terence C.
%S Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%D 1998
%8 14 15 apr
%V 1391
%I Springer-Verlag
%C Paris
%@ 3-540-64360-5
%F Andersson:1998:ecmlc
%X Genetic Programming is used to evolve coupled map lattices for density classification. The most successful evolved rules depending only on nearest neighbors (r=1) show better performance than existing r=3 cellular automaton rules on this task.
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0055935
%U http://dx.doi.org/doi:10.1007/BFb0055935
%P 151-162

%0 Generic
%T The Rolling Stones - Genetic Programming in AIP
%A Andersson, Thord
%A Forssen, Per-Erik
%D 2000
%8 mar  06
%G en
%F oai:CiteSeerPSU:491253
%O student project
%X This report describes the design of a soccer playing agent developed in the scope of the AI Programming course. This agent uses a variant of the subsumption architecture [2]. The primitive behaviours that dene the intelligence of the agent are evolved using genetic programming [4]. We chose the genetic-programming approach instead of designs such as decision trees etc, since we wanted the intelligence in the agents to be truly articial, and not designed
%K genetic algorithms, genetic programming
%U http://www.ida.liu.se/~silco/AIP/Rolling-Stones.ps

%0 Conference Proceedings
%T Interactive GP with Tree Representation of Classical Music Pieces
%A Ando, Daichi
%A Dahlsted, Palle
%A Nordahl, Mats
%A Iba, Hitoshi
%Y Giacobini, Mario
%Y Brabazon, Anthony
%Y Cagnoni, Stefano
%Y Di Caro, Gianni A.
%Y Drechsler, Rolf
%Y Farooq, Muddassar
%Y Fink, Andreas
%Y Lutton, Evelyne
%Y Machado, Penousal
%Y Minner, Stefan
%Y O’Neill, Michael
%Y Romero, Juan
%Y Rothlauf, Franz
%Y Squillero, Giovanni
%Y Takagi, Hideyuki
%Y Uyar, A. Sima
%Y Yang, Shengxiang
%S Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%D 2007
%8 November 13 apr
%V 4448
%I Springer Verlag
%C Valencia, Spain
%F ando:evows07
%X Research on the application of Interactive Evolutionary Computation(IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, we purpose a new IEC approach to music composition based on classical music theory. In this paper, we describe an established system according to the above idea, and detail of making success of composition a piece.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-71805-5_63
%U http://dx.doi.org/doi:10.1007/978-3-540-71805-5_63
%P 577-584

%0 Conference Proceedings
%T Interactive Composition Aid System by Means of Tree Representation of Musical Phrase
%A Ando, Daichi
%A Iba, Hitoshi
%Y Srinivasan, Dipti
%Y Wang, Lipo
%S 2007 IEEE Congress on Evolutionary Computation
%D 2007
%8 25 28 sep
%I IEEE Press
%C Singapore
%@ 1-4244-1340-0
%F Ando:2007:cec
%X Research on the application of Interactive Evolutionary Computation (IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, the authors purpose a new IEC approach to music composition based on classical music theory. In this paper, the authors describe an established system according to the above idea, and detail of making success of composition a piece.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2007.4425027
%U 1814.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2007.4425027
%P 4258-4265

%0 Conference Proceedings
%T Real-time Breeding Composition System by means of Genetic Programming and Breeding Procedure
%A Ando, Daichi
%S Proceedings International Computer Music Conference Proceedings SMC 2014
%D 2014
%8 14 20 sep
%I Michigan Publishing
%C Athens, Greece
%F conf/icmc/Ando14
%X The use of laptop computers to produce real-time music and multimedia performances has increased significantly in recent years. In this paper, I propose a new method of generating club-style loop music in real time by means of interactive evolutionary computation (IEC). The method includes two features. The first is the concept of breeding without any consciousness of generation. The second is a multiple-ontogeny mechanism that generates several phenotypes from one genotype, incorporating ideas of co-evolution and multi-objective optimisation. The proposed method overcomes certain limitations of IEC, namely the burden of interactive evaluation and the narrow search domain resulting from handling few individuals. A performance system that generates club-style loop music from the photo album in mobile devices is implemented by means of the proposed method. This system is then tested, and the success of performances with the implemented system indicates that the proposed methods work effectively.
%K genetic algorithms, genetic programming
%U http://hdl.handle.net/2027/spo.bbp2372.2014.062

%0 Conference Proceedings
%T Image classification and processing using modified parallel-ACTIT
%A Ando, Jun
%A Nagao, Tomoharu
%S IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
%D 2009
%8 oct
%F Ando:2009:ieeeSMC
%X Image processing and recognition technologies are required to solve various problems. We have already proposed the system which automatically constructs image processing with Genetic Programming (GP), Automatic Construction of Tree-structural Image Transformation (ACTIT). However, it is necessary that training image sets are properly classified in advance if they have various characteristics. In this paper, we propose Modified Parallel-ACTIT which automatically classifies training image sets into several subpopulations. And it optimizes tree-structural image transformation for each training image sets in each subpopulations. We show experimentally that Modified Parallel-ACTIT is more effective in comparison with ordinary ACTIT.
%K genetic algorithms, genetic programming, automatic construction of tree-structural image transformation, image classification, image recognition, modified parallel-ACTIT, training image sets, image classification, tree data structures
%R doi:10.1109/ICSMC.2009.5346894
%U http://dx.doi.org/doi:10.1109/ICSMC.2009.5346894
%P 1787-1791

%0 Conference Proceedings
%T Modeling Genetic Network by Hybrid GP
%A Ando, Shin
%A Iba, Hitoshi
%A Sakamoto, Erina
%Y Fogel, David B.
%Y El-Sharkawi, Mohamed A.
%Y Yao, Xin
%Y Greenwood, Garry
%Y Iba, Hitoshi
%Y Marrow, Paul
%Y Shackleton, Mark
%S Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%D 2002
%8 December 17 may
%I IEEE Press
%@ 0-7803-7278-6
%F ando:2002:mgnbhg
%X We present an Evolutionary Modelling method for modeling genetic regulatory networks. The method features hybrid algorithm of Genetic Programming with statistical analysis to derive systems of differential equations. Genetic Programming and Least Mean Square method were combined to identify a concise form of regulation between the variables from a given set of time series. Also, results of multiple runs were statistically analysed to indicate the term with robust and significant influence. Our approach was evaluated in artificial data and real world data.
%K genetic algorithms, genetic programming, artificial data, differential equations, evolutionary modelling method, genetic regulatory network modeling, hybrid algorithm, hybrid genetic programming, least mean square method, multiple runs, real world data, regulation, statistical analysis, time series, differential equations, least mean squares methods, statistical analysis
%R doi:10.1109/CEC.2002.1006249
%U http://citeseer.ist.psu.edu/520794.html
%U http://dx.doi.org/doi:10.1109/CEC.2002.1006249
%P 291-296

%0 Journal Article
%T Evolutionary modeling and inference of gene network
%A Ando, Shin
%A Sakamoto, Erina
%A Iba, Hitoshi
%J Information Sciences
%D 2002
%8 sep
%V 145
%N 3-4
%@ 0020-0255
%F ando:emi
%X we describe an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.
%K genetic algorithms, genetic programming, Gene network, Evolutionary modeling, Time series prediction
%9 journal article
%R doi:10.1016/S0020-0255(02)00235-9
%U http://www.sciencedirect.com/science/article/B6V0C-46WWB37-3/2/963172f8c0faa12d700376b07bfc96a5
%U http://dx.doi.org/doi:10.1016/S0020-0255(02)00235-9
%P 237-259

%0 Journal Article
%T Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning
%A Ando, Shin
%A Iba, Hitoshi
%J Genetic Programming and Evolvable Machines
%D 2004
%8 jun
%V 5
%N 2
%@ 1389-2576
%F ando:2004:GPEM
%X The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in Bioinformatics. The problem we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues. Two subtasks in such problem, feature subset selection and inductive learning has critical effect on each other. In the wrapper approach, combinatorial search of feature subset is done with performance of inductive learning as search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation is introduced, which uses Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals synergistic effect of genes in discrimination of the profiles.
%K genetic algorithms, genetic programming, evolutionary computation, artificial immune system, wrapper approach, gene expression classification, cancer diagnosis
%9 journal article
%R doi:10.1023/B:GENP.0000023685.83861.69
%U http://dx.doi.org/doi:10.1023/B:GENP.0000023685.83861.69
%P 145-156

%0 Conference Proceedings
%T Fusion of Local and Global Descriptors for Content-Based Image and Video Retrieval
%A Andrade, Felipe S. P.
%A Almeida, Jurandy
%A Pedrini, Helio
%A da S. Torres, Ricardo
%S 17th Iberoamerican Congress on Pattern Recognition
%D 2012
%C Buenos Aires, Argentina
%F Andrade2012CIARP
%X Recently, fusion of descriptors has become a trend for improving the performance in image and video retrieval tasks. Descriptors can be global or local, depending on how they analyse visual content. Most of existing works have focused on the fusion of a single type of descriptor. Different from all of them, this paper aims to analyze the impact of combining global and local descriptors. Here, we perform a comparative study of different types of descriptors and all of their possible combinations. Extensive experiments of a rigorous experimental design show that global and local descriptors complement each other, such that, when combined, they outperform other combinations or single descriptors.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-33275-3_104
%U http://dx.doi.org/10.1007/978-3-642-33275-3_104
%U http://dx.doi.org/doi:10.1007/978-3-642-33275-3_104
%P 845-853

%0 Generic
%T Artificial Evolution of Intelligence: Lessons from natural evolution: An illustrative approach using Genetic Programming
%A Andre, David
%D 1994
%F andre:UGthesis
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.1367&rep=rep1&type=pdf

%0 Book Section
%T Automatically Defined Features: The Simultaneous Evolution of 2-Dimensional Feature Detectors and an Algorithm for Using Them
%A Andre, David
%E Kinnear, Jr., Kenneth E.
%B Advances in Genetic Programming
%D 1994
%I MIT Press
%F kinnear:andre
%X Although automatically defined functions (ADFcts) with genetic programming (GP) appear to have great utility in a wide variety of domains, their application to the automatic discovery of 2-dimensional features has been only moderately successful [Koza 1993]. Boolean functions of pixel inputs, although very general, may not be the best representation for 2-dimensional features. This chapter describes a method for the simultaneous evolution of 2-dimensional hit-miss matrices and an algorithm to use these matrices in pattern recognition. Hit-miss matrices are templates that can be moved over part of an input pattern to check for a match. These matrices are evolved using a 2-dimensional genetic algorithm, while the algorithms controlling the templates are evolved using GP. The approach is applied to the problem of digit recognition, and is found to be successful at discovering individuals which can recognize very low resolution digits. Possibilities for expansion into a full-size character recognition system are discussed.
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888
%P 477-494

%0 Conference Proceedings
%T Evolution of Mapmaking Ability: Strategies for the evolution of learning, planning, and memory using genetic programming
%A Andre, David
%S Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%D 1994
%8 27 29 jun
%V 1
%I IEEE Press
%C Orlando, Florida, USA
%F andre:maps
%X An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to genetic programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using genetic programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations. An illustrative problem of ‘gold’ collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans
%K genetic algorithms, genetic programming, evolved representations, gold collection, information encoding, intelligent agent, learning, mapmaking evolution
%K memory, multi-phasic fitness environment, planning, brain models, cartography, cognitive systems, learning (artificial intelligence), planning (artificial intelligence)
%R doi:10.1109/ICEC.1994.350007
%U http://dx.doi.org/doi:10.1109/ICEC.1994.350007
%P 250-255

%0 Conference Proceedings
%T Learning and Upgrading Rules for an OCR System Using Genetic Programming
%A Andre, David
%S Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%D 1994
%8 27 29 jun
%I IEEE Press
%C Orlando, Florida, USA
%F ieee94:andre
%K genetic algorithms, genetic programming
%R doi:10.1109/ICEC.1994.349906
%U http://citeseer.ist.psu.edu/31976.html
%U http://dx.doi.org/doi:10.1109/ICEC.1994.349906

%0 Conference Proceedings
%T The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming
%A Andre, David
%Y Eshelman, Larry J.
%S Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%D 1995
%8 15 19 jul
%I Morgan Kaufmann
%C Pittsburgh, PA, USA
%@ 1-55860-370-0
%F Andre:1995:ammsp
%K genetic algorithms, genetic programming, memory
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Andre_1995_ammsp.pdf
%P 248-255

%0 Conference Proceedings
%T Parallel Genetic Programming on a Network of Transputers
%A Andre, David
%A Koza, John R.
%Y Rosca, Justinian P.
%S Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%D 1995
%8 September
%C Tahoe City, California, USA
%F andre:1995:parallel
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf
%P 111-120

%0 Conference Proceedings
%T The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action
%A Andre, David
%Y Mellish, Chris S.
%S IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence
%D 1995
%8 20 25 aug
%V 1
%I Morgan Kaufmann
%C Montreal, Quebec, Canada
%@ 1-55860-363-8
%F andre:1995:apalmm
%X An essential component of an intelligent agent is the ability to notice, encode, store, and use information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, a method for the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans using the stored mental models. In this method, agents are multi-part computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of ’gold’ collection is used to demonstrate the method in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the method can evolve programs that store simple representations of their environments and use these representations to produce simple plans.
%K genetic algorithms, genetic programming, memory
%U http://ijcai.org/Past%20Proceedings/IJCAI-95-VOL%201/pdf/097.pdf
%P 741-747

%0 Conference Proceedings
%T Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming
%A Andre, David
%A Bennett III, Forrest H.
%A Koza, John R.
%S Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems
%D 1996
%8 16–18 may
%V 1
%I MIT Press
%C Nara, Japan
%F andre:1996:GKL
%X A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has an accuracy of 82.326%. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it uses a fine-grained internal representation of density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large distances in time and space.
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf

%0 Book Section
%T Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture
%A Andre, David
%A Koza, John R.
%E Angeline, Peter J.
%E Kinnear, Jr., K. E.
%B Advances in Genetic Programming 2
%D 1996
%I MIT Press
%C Cambridge, MA, USA
%@ 0-262-01158-1
%F andre:1996:aigp2
%X This chapter describes the parallel implementation of genetic programming in the C programming language using a PC type computer (running Windows) acting as a host and a network of processing nodes using the transputer architecture. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. This approach is illustrated by a comparison of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required the least computational effort with an 5% migration rate. Moreover, this computational effort was less than that required for solving the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of genetic programming, the use of distributed sub-populations with only limited migration delivered more than linear speed-up in solving the problem.
%K genetic algorithms, genetic programming
%R doi:10.7551/mitpress/1109.003.0022
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277532
%U http://dx.doi.org/doi:10.7551/mitpress/1109.003.0022
%P 317-337

%0 Conference Proceedings
%T Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem
%A Andre, David
%A Bennett III, Forrest H.
%A Koza, John R.
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F andre:1996:camc
%X It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various human- written algorithms have appeared in the past two decades for the vexatious majority classification task for one-dimensional two-state cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy of 82.326%. This level of accuracy exceeds that of the original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other known rules produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate repertoire of domains and particles to represent and communicate information across the cellular space.
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf
%P 3-11

%0 Conference Proceedings
%T A Study in Program Response and the Negative Effects of Introns in Genetic Programming
%A Andre, David
%A Teller, Astro
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F andre:1996:introns
%X The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods have been explored. One alternative is to treat a specific location in indexed memory as the response value when the program terminates. The purpose of this paper is to explore the applicability of this technique to tree-structured programs and to explore the intron effects that these studies bring to light. This paper’s experimental results support the finding that this memory-based program response technique is an improvement for some, but not all, problems. In addition, this paper’s experimental results support the finding that, contrary to past research and speculation, the addition or even facilitation of introns can seriously degrade the search performance of genetic programming.
%K genetic algorithms, genetic programming
%U http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AndreTeller.ps
%P 12-20

%0 Conference Proceedings
%T A parallel implementation of genetic programming that achieves super-linear performance
%A Andre, David
%A Koza, John R.
%Y Arabnia, Hamid R.
%S Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications
%D 1996
%8 September 11 aug
%V III
%I CSREA
%C Sunnyvale
%F andre:1996:parGP
%X This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance on the same problem.
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/pdpta1996.pdf
%P 1163-1174

%0 Book Section
%T Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming
%A Andre, David
%E Baeck, Thomas
%E Fogel, David B.
%E Michalewicz, Zbigniew
%B Handbook of Evolutionary Computation
%D 1997
%I Oxford University Press
%@ 0-7503-0392-1
%F andre:1997:HEC
%X Rule-based systems used for optical character recognition (OCR) are notoriously difficult to write, maintain, and upgrade. This case study describes a method for using genetic programming (GP) to automatically generate and upgrade rules for an OCR system. Sets of rules for recognizing a single character are encoded as LISP programs and are evolved using GP. The rule sets are programs that evolve to examine a set of preprocessed features using complex constructs including iteration, pointers, and memory. The system was successful at learning rules for large character sets consisting of multiple fonts and sizes, with good generalization to test sets. In addition, the method was found to be successful at updating human-coded rules written in C for new fonts. This research demonstrates the successful application of GP to a difficult, noisy, real-world problem, and introduces GP as a method for learning sets of rules.
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf

%0 Generic
%T Multi-level parallelism in automatically synthesizing soccer-playing programs for Robocup using genetic programming
%A Andre, David
%D 1998
%F andre:cs267
%X Many of the various proposals for tomorrow’s supercomputers have included clusters of multiprocessors as an essential component. However, when designing the systems of the future, it is important to insure that the nature of the parallelism provided matches up with some relevant and important set of algorithms. This project presents empirical program synthesis as an algorithm that can successfully exploit the multiple levels of interconnect present in an multi-SMP cluster system. When applying program synthesis techniques to difficult problems, it is often the case that two distinct levels of parallelism will emerge. First, many example programs must be tested – and can often be tested in parallel. This matches up with the ’slow’ interconnect on a clump-based system. Second, the execution of a particular program can often be parallelized, especially if the program is complicated or requires interactions with a complex simulation. This level of parallelism, in contrast to the first, often requires fine-grained communication. Thus, this matches up with the ’fast’ level of the clump-based system. In particular, this project presents a multi-level parallel system for the automatic program synthesis of soccer-playing agents for the Robocup simulator competition using genetic programming. The system uses both the fast shared-memory communication of the SMP system as well as a much slower mechanism for the inter-SMP communication. The system is benchmarked on a variety of configurations, and speedup curves are presented. Additionally, a simple LogP analysis comparing the performance of the designed system with a single-processor based NOW system is presented. Finally, the Robocup project is reviewed and the future work outlined.
%K genetic algorithms, genetic programming, memory
%U http://citeseer.ist.psu.edu/245675.html

%0 Journal Article
%T A parallel implementation of genetic programming that achieves super-linear performance
%A Andre, David
%A Koza, John R.
%J Information Sciences
%D 1998
%V 106
%N 3-4
%@ 0020-0255
%F AK97
%X This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a <i>super-linear</i> speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1016/S0020-0255(97)10011-1
%U http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03
%U http://dx.doi.org/doi:10.1016/S0020-0255(97)10011-1
%P 201-218

%0 Conference Proceedings
%T On the Theory of Designing Circuits using Genetic Programming and a Minimum of Domain Knowledge
%A Andre, David
%A Bennett III, Forrest H.
%A Koza, John
%A Keane, Martin A.
%S Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%8 May 9 may
%I IEEE Press
%C Anchorage, Alaska, USA
%@ 0-7803-4869-9
%F andre:1998:tdcGPmdk
%X The problem of analog circuit design is a difficult problem that is generally viewed as requiring human intelligence to solve. Considerable progress has been made in automating the design of certain categories of purely digital circuits; however, the design of analog electrical circuits and mixed analog-digital circuits has not proved to be as amenable to automation. When critical analog circuits are required for a project, skilled and highly trained experts are necessary. Previous work on applying genetic programming to the design of analog circuits has proved to be successful at evolving a wide variety of circuits, including filters, amplifiers, and computational circuits; however, previous approaches have required the specification of an appropriate embryonic circuit. This paper explores a method to eliminate even this small amount of problem specific knowledge, and, in addition, proves that the representation used is capable of producing all circuits.
%K genetic algorithms, genetic programming, amplifiers, analog circuit design, circuit evolution, computational circuits, embryonic circuit elimination, filters, knowledge representation, minimal domain knowledge, problem-specific knowledge, analogue circuits, circuit CAD, circuit optimisation, intelligent design assistants, knowledge representation, programming
%R doi:10.1109/ICEC.1998.699489
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00699489
%U c023.pdf
%U http://dx.doi.org/doi:10.1109/ICEC.1998.699489
%P 130-135

%0 Conference Proceedings
%T Evolving Team Darwin United
%A Andre, D.
%A Teller, A.
%Y Asada, M.
%Y Kitano, H.
%S RoboCup-98: Robot Soccer World Cup II
%S LNCS
%D 1999
%8 jul 1998
%V 1604
%I Springer Verlag
%C Paris, France
%@ 3-540-66320-7
%F Andre:1999:ETD
%X The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a lack of reliable strategic guidelines, the pervasive attitude has been that the problem can most successfully be attacked by human expertise, possibly assisted by some level of machine learning. This led, in RoboCup’97, to a field of simulator teams all of whose level and style of play were heavily influenced by the human designers of those teams. It is the thesis of our work that machine learning, if given the opportunity to design (learn) “everything” about how the simulator team operates, can develop a competitive simulator team that solves the problem using highly successful, if largely non- human, styles of play. To this end, Darwin United is a team of eleven players that have been evolved as a team of coordinated agents in the RoboCup simulator. Each agent is given a subset of the lowest level perceptual inputs and must learn to execute series of the most basic actions (turn, kick, dash) in order to participate as a member of the team. This paper presents our motivation, our approach, and the specific construction of our team that created itself from scratch.
%K genetic algorithms, genetic programming
%R doi:10.1007/3-540-48422-1_28
%U http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Teller_Astro.ps
%U http://dx.doi.org/doi:10.1007/3-540-48422-1_28
%P 346-351

%0 Journal Article
%T Genetic Programming for detecting rhythmic stress in spoken English
%A Andreae, Peter
%A Xie, Huayang
%A Zhang, Mengjie
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%D 2008
%V 12
%N 1
%I IOS Press
%@ 1327-2314
%F Andreae:2008:IJKBIES
%X Rhythmic stress detection is an important but difficult problem in speech recognition. This paper describes an approach to the automatic detection of rhythmic stress in New Zealand spoken English using a linear genetic programming system with speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. In addition to the four standard arithmetic operators, this approach also uses other functions such as trigonometric and conditional functions in the function set to cope with the complexity of the task. The error rate on the training set is used as the fitness function. The approach is examined and compared to a decision tree approach and a support vector machine approach on a speech data set with 703 vowels segmented from 60 female adult utterances. The genetic programming approach achieved a maximum average accuracy of 92.6percent. The results suggest that the genetic programming approach developed in this paper outperforms the decision tree approach and the support vector machine approach for stress detection on this data set in terms of the detection accuracy, the ability of handling redundant features, and the automatic feature selection capability.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.3233/KES-2008-12103
%U http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00139
%U http://dx.doi.org/doi:10.3233/KES-2008-12103
%P 15-28

%0 Book Section
%T Genetic Programming for the Acquisition of Double Auction Market Strategies
%A Andrews, Martin
%A Prager, Richard
%E Kinnear, Jr., Kenneth E.
%B Advances in Genetic Programming
%D 1994
%I MIT Press
%F kinnear:andrews
%X The Double Auction (DA) is the mechanism behind the minute-by-minute trading on many futures and commodity exchanges. Since 1990, DA tournaments have been held by the Santa Fe Institute. The competitors in the tournaments are strategies embodied in computer programmes written by a variety of economists, computer scientists and mathematicians. This paper describes how Genetic Programming (GP) methods have been used to create strategies superior, in local DA playoffs, to many of the hand-coded strategies. To isolate the contribution that the evolutionary process makes to the search for good strategies, we compare GP and Simulated Annealing (SA) optimisation of programmes. To reduce the cost of learning, we also investigate an approach that uses statistical measures to maintain a uniform population pressure.
%K genetic algorithms, genetic programming, SA
%U http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888
%P 355-368

%0 Journal Article
%T Evelyne Lutton, Nathalie Perrot, Alberto Tonda: Evolutionary algorithms for food science and technology
%A Androutsopoulos, Kelly
%J Genetic Programming and Evolvable Machines
%D 2019
%8 mar
%V 20
%N 1
%@ 1389-2576
%F Androutsopoulos:2019:GPEM
%O Book Review
%X ...Evolutionary Algorithms for Food Science and Technology would be invaluable to anyone considering using EAs in food science...
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s10710-018-9335-2
%U http://dx.doi.org/doi:10.1007/s10710-018-9335-2
%P 147-149

%0 Book Section
%T Intelligent Data Analysis in Electric Power Engineering Applications
%A Androvitsaneas, V. P.
%A Boulas, K.
%A Dounias, G. D.
%E Tsihrintzis, George A.
%E Sotiropoulos, Dionisios N.
%E Jain, Lakhmi C.
%B Machine Learning Paradigms: Advances in Data Analytics
%S ISRL
%D 2019
%V 149
%I Springer
%F androvitsaneas_intelligent_2019
%X This chapter presents various intelligent approaches for modelling, generalization and knowledge extraction from data, which are applied in different electric power engineering domains of the real world. Specifically, the chapter presents: (1) the application of ANNs, inductive ML, genetic programming and wavelet NNs, in the problem of ground resistance estimation, an important problem for the design of grounding systems in constructions, (2) the application of ANNs, genetic programming and nature inspired techniques such as gravitational search algorithm in the problem of estimating the value of critical flashover voltage of insulators, a well-known difficult topic of electric power systems, (3) the application of specific intelligent techniques (ANNs, fuzzy logic, etc.) in load forecasting problems and in optimization tasks in transmission lines. The presentation refers to previously conducted research related to the application domains and briefly analyses each domain of application, the data corresponding to the problem under consideration, while are also included a brief presentation of each intelligent technique and presentation and discussion of the results obtained. Intelligent approaches are proved to be handy tools for the specific applications as they succeed to generalize the operation and behaviour of specific parts of electric power systems, they manage to induce new, useful knowledge (mathematical relations, rules and rule based systems, etc.) and thus they effectively assist the proper design and operation of complex real world electric power systems.
%K genetic algorithms, genetic programming, gene expression programming, electric power systems, Gravitational Search Algorithm, ground resistance estimation, insulators, wavelet neural nets
%R doi:10.1007/978-3-319-94030-4_11
%U https://doi.org/10.1007/978-3-319-94030-4_11
%U http://dx.doi.org/doi:10.1007/978-3-319-94030-4_11
%P 269-313

%0 Conference Proceedings
%T Dimension Reduction Using Evolutionary Support Vector Machines
%A Ang, J. H.
%A Teoh, E. J.
%A Tan, C. H.
%A Goh, K. C.
%A Tan, K. C.
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Ang:2008:cec
%X This paper presents a novel approach of hybridising two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2008.4631290
%U EC0777.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2008.4631290
%P 3634-3641

%0 Thesis
%T Evolutionary Algorithms and Emergent Intelligence
%A Angeline, Peter John
%D 1993
%C USA
%C Ohio State University
%F angeline:dissertation
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter0.ps.Z

%0 Book Section
%T Genetic Programming and Emergent Intelligence
%A Angeline, Peter John
%E Kinnear, Jr., Kenneth E.
%B Advances in Genetic Programming
%D 1994
%I MIT Press
%F kinnear:angeline
%X Genetic programming is but one of several problem solving methods based on a computational analogy to natural evolution. Such algorithms, collectively titled evolutionary computations, embody dynamics that permit task specific knowledge to emerge while solving the problem. In contrast to the traditional knowledge representations of artificial intelligence, this method of problem solving is termed emergent intelligence. This chapter describes some of the basics of emergent intelligence, its implementation in evolutionary computations, and its contributions to genetic programming. Demonstrations and guidelines on how to exploit emergent intelligence to extend the problem solving capabilities of genetic programming and other evolutionary computations are also presented.
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap4.pdf
%P 75-98

%0 Conference Proceedings
%T Competitive Environments Evolve Better Solutions for Complex Tasks
%A Angeline, Peter J.
%A Pollack, Jordan B.
%Y Forrest, Stephanie
%S Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%D 1993
%8 17 21 jul
%I Morgan Kaufmann
%C University of Illinois at Urbana-Champaign
%@ 1-55860-299-2
%F icga93:angeline
%X In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we demonstrate that competitive fitness functions, i.e. fitness functions that are dependent on the constituents of the population, can provide a more robust training environment than independent fitness functions. We describe three differing methods for competitive fitness, and discuss their respective advantages.
%K genetic algorithms, genetic programming
%U http://www.demo.cs.brandeis.edu/papers/icga5.pdf
%P 264-270

%0 Conference Proceedings
%T Genetic programming: A current snapshot
%A Angeline, P. J.
%Y Fogel, D. B.
%Y Atmar, W.
%S Proceedings of the Third Annual Conference on Evolutionary Programming
%D 1994
%I Evolutionary Programming Society
%F Angeline:1994:GPCS
%X Genetic programming is a promising variant of genetic algorithms that evolves dynamic, hierarchical structures often described as programs. Genetic programming’s flexibility to tailor the representation language to the problem being solved, and its specially designed crossover operator provide a robust tool for evolving problem solutions. This paper provides an introduction to genetic programming, a short review of dynamic representations used in evolutionary systems and their relation to genetic programming, and a description of some of genetic programming’s inherent properties. The paper concludes with a review of on going research and some potential future directions for the field.
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzep94-gp.pdf/angeline94genetic.pdf

%0 Conference Proceedings
%T The evolutionary induction of subroutines
%A Angeline, Peter J.
%A Pollack, Jordan B.
%S Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society
%D 1992
%I Lawrence Erlbaum
%C Bloomington, Indiana, USA
%F Angeline:1992:EIS
%X we describe a genetic algorithm capable of evolving large programs by exploiting two new genetic operators which construct and deconstruct parameterized subroutines. These subroutines protect useful partial solutions and help to solve the scaling problem for a class of genetic problem solving methods. We demonstrate that our algorithm acquires useful subroutines by evolving a modular program from scratch to play and win at Tic-Tac-Toe against a flawed expert. This work also serves to amplify our previous note (Pollack, 1991) that a phase transition is the principle behind induction in dynamical cognitive models.
%K genetic algorithms, genetic programming
%U http://www.demo.cs.brandeis.edu/papers/glib92.pdf
%P 236-241

%0 Report
%T Coevolving High-Level Representations
%A Angeline, P. J.
%A Pollack, J. B.
%D 1993
%N Technical report 92-PA-COEVOLVE
%I Laboratory for Artificial Intelligence. The Ohio State University
%F Angeline:1993:CHLR
%X Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype’s maximum size and complexity. In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly highlevel representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved languages are provided.
%K genetic algorithms, genetic programming
%9 July
%U http://www.demo.cs.brandeis.edu/papers/alife3.pdf

%0 Conference Proceedings
%T Evolutionary Module Acquisition
%A Angeline, Peter J.
%A Pollack, Jordan
%Y Fogel, D.
%Y Atmar, W.
%S Proceedings of the Second Annual Conference on Evolutionary Programming
%D 1993
%8 25 26 feb
%C La Jolla, CA, USA
%F angeline:1993:ema
%X Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms, called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time.
%K genetic algorithms, genetic programming, FSM, GLiB
%U http://www.demo.cs.brandeis.edu/papers/ep93.pdf
%P 154-163

%0 Conference Proceedings
%T Coevolving high-level representations
%A Angeline, P. J.
%A Pollack, J. B.
%Y Langton, Christopher G.
%S Artificial Life III
%S SFI Studies in the Sciences of Complexity
%D 1994
%8 15 19 jun 1992
%V XVII
%I Addison-Wesley
%C Santa Fe, New Mexico
%@ 0-201-62492-3
%F Angeline:1991:CHLR
%X Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype’s maximum size and complexity. In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly high-level representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved languages are provided.
%K genetic algorithms, genetic programming
%U http://www.demo.cs.brandeis.edu/papers/alife3.pdf
%P 55-71

%0 Journal Article
%T Genetic programming: On the programming of computers by means of natural selection,John R. Koza, A Bradford Book, MIT Press, Cambridge MA, 1992, ISBN 0-262-11170-5, xiv + 819pp., US$55.00
%A Angeline, Peter J.
%J Biosystems
%D 1994
%V 33
%N 1
%F angeline:1994:BS
%O Book review
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1016/0303-2647(94)90062-0
%U http://dx.doi.org/doi:10.1016/0303-2647(94)90062-0
%P 69-73

%0 Journal Article
%T Evolution Revolution: An Introduction to the Special Track on Genetic and Evolutionary Programming
%A Angeline, Peter J.
%J IEEE Expert
%D 1995
%8 jun
%V 10
%N 3
%F angeline:1995:er
%O Guest editor’s introduction
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1109/MIS.1995.10027
%U http://dx.doi.org/doi:10.1109/MIS.1995.10027
%P 6-10

%0 Conference Proceedings
%T Morphogenic Evolutionary Computations: Introduction, Issues and Examples
%A Angeline, Peter J.
%Y McDonnell, John Robert
%Y Reynolds, Robert G.
%Y Fogel, David B.
%S Evolutionary Programming IV: The Fourth Annual Conference on Evolutionary Programming
%D 1995
%I MIT Press
%@ 0-262-13317-2
%F angeline:1995:mcc
%X Morphogenic (or morphogenetic) evolutionary computations are evolutionary computations that distinguish between the representation that is evolved and the representation that is evaluated by the fitness function. A user defined development function provides the necessary mapping between these often very different structures. Such a separation affords important advantages for these evolutionary computations, not the least of which is modification of a relatively small structure that is expanded into a much larger one for evaluation by the fitness function. This paper provides a formal definition of morphogenic evolutionary computations along with a review and discussion of the relevant literature.
%K genetic algorithms, genetic programming
%R doi:10.7551/mitpress/2887.003.0037
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6300850
%U http://dx.doi.org/doi:10.7551/mitpress/2887.003.0037
%P 387-401

%0 Book Section
%T Adaptive and Self-Adaptive Evolutionary Computations
%A Angeline, Peter J.
%E Palaniswami, Marimuthu
%E Attikiouzel, Yianni
%B Computational Intelligence: A Dynamic Systems Perspective
%D 1995
%I IEEE Press
%F angeline:1995:asa
%X This paper reviews the various studies that have introduced adaptive and selfadaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in use. Previous studies are reviewed and placed into a categorisation that helps to illustrate their similarities and differences
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/cache/papers/cs/1007/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzicec95.pdf/angeline95adaptive.pdf
%P 152-163

%0 Book
%T Advances in Genetic Programming 2
%E Angeline, Peter J.
%E Kinnear, Jr., K. E.
%D 1996
%I MIT Press
%C Cambridge, MA, USA
%@ 0-262-01158-1
%F book:1996:aigp2
%X Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field.
%K genetic algorithms, genetic programming
%R doi:10.7551/mitpress/1109.001.0001
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp2.html
%U http://dx.doi.org/doi:10.7551/mitpress/1109.001.0001

%0 Book Section
%T Genetic Programming’s Continued Evolution
%A Angeline, Peter J.
%E Angeline, Peter J.
%E Kinnear, Jr., K. E.
%B Advances in Genetic Programming 2
%D 1996
%I MIT Press
%C Cambridge, MA, USA
%@ 0-262-01158-1
%F intro:1996:aigp2
%X Genetic programming is a variant of genetic algorithms that evolves computer programs represented as tree structures. Genetic programming and genetic algorithms are but one technique in the larger collection of evolutionary computations that also include evolution strategies and evolutionary programming. This chapter begins with a description of the various evolutionary computations that highlights their respective differences across several important dimensions. Following this, an introduction to genetic programming and its relation to other evolutionary computations is provided. Research topics of current interest in the genetic programming field are then reviewed, demonstrating the present breadth and maturity of the field. The chapter ends with a description of the organisation of the remainder of this book and a brief synopsis of each chapter that appears.
%K genetic algorithms, genetic programming
%R doi:10.7551/mitpress/1109.003.0004
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277539
%U http://dx.doi.org/doi:10.7551/mitpress/1109.003.0004
%P 1-20

%0 Book Section
%T Two Self-Adaptive Crossover Operators for Genetic Programming
%A Angeline, Peter J.
%E Angeline, Peter J.
%E Kinnear, Jr., K. E.
%B Advances in Genetic Programming 2
%D 1996
%I MIT Press
%C Cambridge, MA, USA
%@ 0-262-01158-1
%F angeline:1996:aigp2
%X Two self-adaptive crossover operations are studied in a nonstandard genetic program. It is shown that for three distinct problems the results obtained when using either of the self-adaptive crossover operations are equivalent or better than the results when using standard GP crossover. A postmortem analysis of the evolved values for the self-adaptive parameters suggests that certain heuristics commonly used in genetic programming may not be optimal.
%K genetic algorithms, genetic programming
%R doi:10.7551/mitpress/1109.003.0009
%U http://www.natural-selection.com/Library/1996/aigp2.ps.Z
%U http://dx.doi.org/doi:10.7551/mitpress/1109.003.0009
%P 89-110

%0 Conference Proceedings
%T An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover
%A Angeline, Peter J.
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F angeline:1996:leaf
%X In genetic programming, crossover swaps randomly selected subtrees between parents. Typically, the probability of selecting a leaf as the subtree to be swapped is reduced, supposedly to allow larger structures on average. This paper reports on a study to determine the effect of modifying the leaf selection frequency for subtree crossover on the performance of a non-standard genetic program. Both a variety of constant values and dynamic update methods are investigated . It is shown that the performance of the genetic program is impacted by the manipulation of the leaf selection frequency and often can be improved using a random process rather than a constant value.
%K genetic algorithms, genetic programming
%U http://www.natural-selection.com/Library/1996/gp96.zip
%P 21-29

%0 Conference Proceedings
%T Evolving Fractal Movies
%A Angeline, Peter J.
%Y Koza, John R.
%Y Goldberg, David E.
%Y Fogel, David B.
%Y Riolo, Rick L.
%S Genetic Programming 1996: Proceedings of the First Annual Conference
%D 1996
%8 28–31 jul
%I MIT Press
%C Stanford University, CA, USA
%F angeline:1996:efm
%K Evolutionary Programming
%U http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap84.pdf
%P 503-511

%0 Conference Proceedings
%T Subtree Crossover: Building Block Engine or Macromutation?
%A Angeline, Peter J.
%Y Koza, John R.
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max
%Y Iba, Hitoshi
%Y Riolo, Rick L.
%S Genetic Programming 1997: Proceedings of the Second Annual Conference
%D 1997
%8 13 16 jul
%I Morgan Kaufmann
%C Stanford University, CA, USA
%F angeline:1997:tcbbe
%X In genetic programming, crossover swaps randomly selected subtrees between parents. Recent work in genetic algorithms (Jones 1995) demonstrates that when one of the parents selected for crossover is replaced with a randomly generated parent, the algorithm performs as well or better than crossover for some problems. Terry Jones (ICGA 1995) termed this form of macromutation headless chicken crossover. The following paper investigates two forms of headless-chicken crossover for manipulating parse trees and shows that both types of macromutation perform as well or better than standard subtree crossover. It is argued that these experiments support the hypothesis that the building block hypothesis is not descriptive of the operation of subtree crossover and that sub-tree crossover is better modelled as a macromutation restricted by population content.
%K genetic algorithms, genetic programming
%U http://ncra.ucd.ie/COMP41190/SubtreeXoverBuildingBlockorMacromutation_angeline_gp97.ps
%P 9-17

%0 Conference Proceedings
%T An Alternative to Indexed Memory for Evolving Programs with Explicit State Representations
%A Angeline, Peter J.
%Y Koza, John R.
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max
%Y Iba, Hitoshi
%Y Riolo, Rick L.
%S Genetic Programming 1997: Proceedings of the Second Annual Conference
%D 1997
%8 13 16 jul
%I Morgan Kaufmann
%C Stanford University, CA, USA
%F Angeline:1997:aIMepesr
%K evolutionary programming and evolution strategies
%P 423-430

%0 Conference Proceedings
%T Tracking Extrema in Dynamic Environments
%A Angeline, Peter J.
%Y Angeline, P. J.
%Y Reynolds, R. G.
%Y McDonnell, J. R.
%Y Eberhart, R.
%S Proceedings of the 6th International Conference on Evolutionary Programming
%S Lecture Notes in Computer Science
%D 1997
%8 apr 13 16
%V 1213
%I Springer Verlag
%C Indianapolis, Indiana, USA
%@ 3-540-62788-X
%F angeline:1997:txde
%X Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0014823
%U http://www.natural-selection.com/Library/1997/ep97b.pdf
%U http://dx.doi.org/doi:10.1007/BFb0014823
%P 335-345

%0 Conference Proceedings
%T An evolutionary program for the identification of dynamical systems
%A Angeline, Peter J.
%A Fogel, David B.
%Y Rogers, S.
%S Application and Science of Artificial Neural Networks III
%D 1997
%V 3077
%F angeline:1997:spie
%X Various forms of neural networks have been applied to the identification of non-linear dynamical systems. In most of these methods, the network architecture is set prior to training. In this paper, a method that evolves a symbolic solution for plant models is described. This method uses an evolutionary program to manipulate collections of parse trees expressed in a task specific language. Experiments performed on two unknown plants show this method is competitive with those that train neural networks for similar problems
%K genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, system identification, dynamical systems, optimization
%R doi:10.1117/12.271503
%U http://www.natural-selection.com/Library/1997/spie97.pdf
%U http://dx.doi.org/doi:10.1117/12.271503
%P 409-417

%0 Book Section
%T Parse Trees
%A Angeline, Peter J.
%E Baeck, Thomas
%E Fogel, David B.
%E Michalewicz, Zbigniew
%B Handbook of Evolutionary Computation
%D 1997
%I Oxford University Press
%@ 0-7503-0392-1
%F Angeline:1997:HEC
%X This section reviews parse tree representations, a popular representation for evolving executable structures. The field of genetic programming is based entirely on the flexibility of this representation. This section describes some of the history of parse trees in evolutionary computation, the form of the representation and some special properties.
%K genetic algorithms, genetic programming
%R doi:10.1201/9781420050387.ptc
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf
%U http://dx.doi.org/doi:10.1201/9781420050387.ptc

%0 Book Section
%T Mutation: Parse Trees
%A Angeline, Peter J.
%E Baeck, Thomas
%E Fogel, David B.
%E Michalewicz, Zbigniew
%B Handbook of Evolutionary Computation
%D 1997
%I Oxford University Press
%@ 0-7503-0392-1
%F Angeline:1997:HECa
%X Genetics-based evolutionary computations typically discount the role of mutation operation in the induction of evolved structures. This is especially true in genetic programming where mutation operations for parse trees are often not used. Some practitioners of genetic programming believe that mutation has an important role in evolving fit parse trees. This section describes several mutation operations for parse trees used by some genetic programming enthusiasts.
%K genetic algorithms, genetic programming
%R doi:10.1201/9781420050387.ptc
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf
%U http://dx.doi.org/doi:10.1201/9781420050387.ptc

%0 Book Section
%T Crossover: parse trees
%A Angeline, Peter J.
%E Baeck, Thomas
%E Fogel, David B.
%E Michalewicz, Zbigniew
%B Handbook of Evolutionary Computation
%D 1997
%I Oxford University Press
%@ 0-7503-0392-1
%F Angeline:1997:HECb
%X Described here is the standard crossover operation for parse tree representations most often used in genetic programming. Extensions to this operator for subtrees with multiple return types and genetic programs using automatically defined functions are also described.
%K genetic algorithms, genetic programming
%R doi:10.1201/9781420050387.ptc
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf
%U http://dx.doi.org/doi:10.1201/9781420050387.ptc

%0 Conference Proceedings
%T Subtree Crossover Causes Bloat
%A Angeline, Peter J.
%Y Koza, John R.
%Y Banzhaf, Wolfgang
%Y Chellapilla, Kumar
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max H.
%Y Goldberg, David E.
%Y Iba, Hitoshi
%Y Riolo, Rick
%S Genetic Programming 1998: Proceedings of the Third Annual Conference
%D 1998
%8 22 25 jul
%I Morgan Kaufmann
%C University of Wisconsin, Madison, Wisconsin, USA
%@ 1-55860-548-7
%F angeline:1998:sccb
%K genetic algorithms, genetic programming, evolutionary programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/angeline_1998_sccb.pdf
%P 745-752

%0 Journal Article
%T A Historical Perspective on the Evolution of Executable Structures
%A Angeline, Peter J.
%J Fundamenta Informaticae
%D 1998
%8 aug
%V 35
%N 1–4
%@ 0169-2968
%F angeline:1998:hpees
%X Genetic programming (Koza 1992) is a method of inducing behaviors represented as executable programs. The generality of the approach has spawned a proliferation of work in the evolution of executable structures that is unmatched in the history of the subject. This paper describes the standard approach to genetic programming, as defined in Koza (1992), and then presents the significant studies that preceded its inception as well as the diversification of techniques evolving executable structures that is currently underway in the field.
%K genetic algorithms, genetic programming
%9 journal article
%U http://www.natural-selection.com/Library/1998/gphist.pdf
%P 179-195

%0 Journal Article
%T Multiple Interacting Programs: A Representation for Evolving Complex Behaviors
%A Angeline, Peter J.
%J Cybernetics and Systems
%D 1998
%8 nov
%V 29
%N 8
%@ 0196-9722
%F angeline:1998:mips3
%X This paper defines a representation for expressing complex behaviors, called multiple interacting programs (MIPs), and describes an evolutionary method for evolving solutions to difficult problems expressed as MIPs structures. The MIPs representation is a generalization of neural network architectures that can model any type of dynamic system. The evolutionary training method described is based on an evolutionary program originally used to evolve the architecture and weights of recurrent neural networks. Example experiments demonstrate the training method s ability to evolve appropriate MIPs solutions for difficult problems. An analysis of the evolved solutions shows their dynamics to be interesting and non-trivial.
%K genetic algorithms, genetic programming, mips
%9 journal article
%R doi:10.1080/019697298125407
%U http://www.natural-selection.com/Library/1998/mips3.pdf
%U http://dx.doi.org/doi:10.1080/019697298125407
%P 779-803

%0 Conference Proceedings
%T Evolving Predictors for Chaotic Time Series
%A Angeline, Peter J.
%Y Rogers, S.
%Y Fogel, D.
%Y Bezdek, J.
%Y Bosacchi, B.
%S Proceedings of SPIE: Application and Science of Computational Intelligence
%D 1998
%V 3390
%F angeline:1998:spie
%X Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current methods performance is shown to compare favorably with using larger neural networks.
%K genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, neural networks, chaotic time series prediction
%R doi:10.1117/12.304803
%U http://www.natural-selection.com/Library/1998/spie98.pdf
%U http://dx.doi.org/doi:10.1117/12.304803
%P 170-80

%0 Book Section
%T A Historical Perspective on the Evolution of Executable Structures
%A Angeline, Peter J.
%E Eiben, A. E.
%E Michalewicz, A.
%B Evolutionary Computation
%D 1999
%I Ohmsha
%C Tokyo
%@ 4-274-90269-2
%F angeline:1999:hpees
%K genetic algorithms, genetic programming
%U http://www.ohmsha.co.jp/data/books/e_contents/4-274-90269-2.htm

%0 Book Section
%T Parse trees
%A Angeline, Peter J.
%E Baeck, Thomas
%E Fogel, David B.
%E Michalewicz, Zbigniew
%B Evolutionary Computation 1 Basic Algorithms and Operators
%D 2000
%I Institute of Physics Publishing
%C Bristol
%@ 0-7503-0664-5
%F angeline:2000:EC1
%K genetic algorithms, genetic programming
%U http://www.crcpress.com/product/isbn/9780750306645
%P 155-159

%0 Conference Proceedings
%T Evolving fuzzy inferential sensors for process industry
%A Angelov, Plamen
%A Kordon, Arthur
%A Zhou, Xiaowei
%S 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008
%D 2008
%8 April 7 mar
%C Witten-Boommerholz, Germany
%F Angelov:2008:GEFS
%X This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts aging, industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by the Dow Chemical Company, USA.
%K genetic algorithms, genetic programming, Dow Chemical Company, Takagi-Sugeno-fuzzy system, fuzzy inferential sensor, multi-objective genetic-programming-based optimization, on-line input selection techniques, on-line learning algorithm, process industry, self-tuning inferential soft sensor, chemical industry, fuzzy set theory, fuzzy systems, sensors
%R doi:10.1109/GEFS.2008.4484565
%U http://dx.doi.org/doi:10.1109/GEFS.2008.4484565
%P 41-46

%0 Journal Article
%T Prediction of laser cutting heat affected zone by extreme learning machine
%A Anicic, Obrad
%A Jovic, Srdan
%A Skrijelj, Hivzo
%A Nedic, Bogdan
%J Optics and Lasers in Engineering
%D 2017
%V 88
%@ 0143-8166
%F Anicic:2017:OLE
%X Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be used effectively in applications of HAZ forecasting.
%K genetic algorithms, genetic programming, Extreme Learning Machine, Forecasting, HAZ, Laser cutting
%9 journal article
%R doi:10.1016/j.optlaseng.2016.07.005
%U http://www.sciencedirect.com/science/article/pii/S0143816616301385
%U http://dx.doi.org/doi:10.1016/j.optlaseng.2016.07.005
%P 1-4

%0 Conference Proceedings
%T Ariadne: Evolving test data using Grammatical Evolution
%A Anjum, Muhammad Sheraz
%A Ryan, Conor
%Y Sekanina, Lukas
%Y Hu, Ting
%Y Lourenco, Nuno
%S EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming
%S LNCS
%D 2019
%8 24 26 apr
%V 11451
%I Springer Verlag
%C Leipzig, Germany
%F Anjum:2019:EuroGP
%X Software testing is a key component in software quality assurance; it typically involves generating test data that exercises all instructions and tested conditions in a program and, due to its complexity, can consume as much as 50percent of overall software development budget. Some evolutionary computing techniques have been successfully applied to automate the process of test data generation but no existing techniques exploit variable interdependencies in the process of test data generation, even though several studies from the software testing literature suggest that the variables examined in the branching conditions of real life programs are often interdependent on each other, for example, if (x == y), etc. We propose the Ariadne system which uses Grammatical Evolution (GE) and a simple Attribute Grammar to exploit the variable interdependencies in the process of test data generation. Our results show that Ariadne dramatically improves both effectiveness and efficiency when compared with existing techniques based upon well-established criteria, attaining coverage (the standard software testing success metric for these sorts of problems) of 100percent on all benchmarks with far fewer program evaluations (often between a third and a tenth of other systems).
%K genetic algorithms, genetic programming, Grammatical Evolution, SBSE
%R doi:10.1007/978-3-030-16670-0_1
%U https://www.springer.com/us/book/9783030166694
%U http://dx.doi.org/doi:10.1007/978-3-030-16670-0_1
%P 3-18

%0 Conference Proceedings
%T Gene Permutation: A new Probabilistic Genetic Operator for Improving Multi Expression Programming
%A Anjum, Aftab
%A Islam, Mazharul
%A Wang, Lin
%S 2019 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2019
%8 dec
%F Anjum:2019:SSCI
%X Multi-expression Programming (MEP) encodes multiple genes through linear representation and is a widely useful technique for tangible applications like classification, symbolic regression and digital circuit designing. MEP uses only two genetic operators (mutation, crossover) to explore the search space and exploit genetic materials. However, after going through multiple generations and due to its naturally inspired fitness-based selection procedure, MEP significantly reduces genetic diversity in the population and ultimately produces homogeneous individuals; hence, leading to poor convergence and an ultimate fall into the local minimum. Gene-permutation, the newly proposed Probabilistic Genetic Operator, breakouts the homogeneity by rearranging and inducing new genetic materials in the individuals which in turn maintains the healthy genetic diversity in the population. Moreover, it also assists other genetic operators to produce more effective chromosomes and fully explore the search space. The experiments point out that Gene-permutation improves training efficiency as well as reduces test errors on several well-known symbolic regression problems.
%K genetic algorithms, genetic programming
%R doi:10.1109/SSCI44817.2019.9003048
%U http://dx.doi.org/doi:10.1109/SSCI44817.2019.9003048
%P 3139-3146

%0 Conference Proceedings
%T Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing
%A Anjum, Muhammad Sheraz
%A Ryan, Conor
%Y Hu, Ting
%Y Lourenco, Nuno
%Y Medvet, Eric
%S EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming
%S LNCS
%D 2020
%8 15 17 apr
%V 12101
%I Springer Verlag
%C Seville, Spain
%F Anjum:2020:EuroGP
%X Software-based optimization techniques have been increasingly used to automate code coverage analysis since the nineties. Although several studies suggest that interdependencies can exist between condition constructs in branching conditions of real life programs e.g. (i<=100) or (i==j), etc., to date, only the Ariadne system, a Grammatical Evolution (GE)-based Search Based Software Testing (SBST) technique, exploits interdependencies between variables to efficiently automate code coverage analysis. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to very efficiently evolve highly complex test cases, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from the interdependencies involving constants e.g. (i<=100), which are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. ...
%K genetic algorithms, genetic programming, Grammatical Evolution, SBSE, SBST, Automatic test case generation, Code coverage, Evolutionary Testing
%R doi:10.1007/978-3-030-44094-7_2
%U http://dx.doi.org/doi:10.1007/978-3-030-44094-7_2
%P 18-34

%0 Conference Proceedings
%T Scalability Analysis of Grammatical Evolution Based Test Data Generation
%A Anjum, Muhammad Sheraz
%A Ryan, Conor
%Y Coello Coello, Carlos Artemio
%Y Aguirre, Arturo Hernandez
%Y Uribe, Josu Ceberio
%Y Fabre, Mario Garza
%Y Toscano Pulido, Gregorio
%Y Rodriguez-Vazquez, Katya
%Y Wanner, Elizabeth
%Y Veerapen, Nadarajen
%Y Montes, Efren Mezura
%Y Allmendinger, Richard
%Y Marin, Hugo Terashima
%Y Wagner, Markus
%Y Bartz-Beielstein, Thomas
%Y Filipic, Bogdan
%Y Trautmann, Heike
%Y Tang, Ke
%Y Koza, John
%Y Goodman, Erik
%Y Langdon, William B.
%Y Nicolau, Miguel
%Y Zarges, Christine
%Y Volz, Vanessa
%Y Tusar, Tea
%Y Naujoks, Boris
%Y Bosman, Peter A. N.
%Y Whitley, Darrell
%Y Solnon, Christine
%Y Helbig, Marde
%Y Doncieux, Stephane
%Y Wilson, Dennis G.
%Y Fernandez de Vega, Francisco
%Y Paquete, Luis
%Y Chicano, Francisco
%Y Xue, Bing
%Y Bacardit, Jaume
%Y Mostaghim, Sanaz
%Y Fieldsend, Jonathan
%Y Schuetze, Oliver
%Y Arnold, Dirk
%Y Ochoa, Gabriela
%Y Segura, Carlos
%Y Cotta, Carlos
%Y Emmerich, Michael
%Y Zhang, Mengjie
%Y Purshouse, Robin
%Y Ray, Tapabrata
%Y Petke, Justyna
%Y Ishikawa, Fuyuki
%Y Lengler, Johannes
%Y Neumann, Frank
%S Proceedings of the 2020 Genetic and Evolutionary Computation Conference
%S GECCO ’20
%D 2020
%8 jul 8 12
%I Association for Computing Machinery
%C internet
%F Anjum:2020:GECCO
%X Heuristic-based search techniques have been increasingly used to automate different aspects of software testing. Several studies suggest that variable interdependencies may exist in branching conditions of real-life programs, and these dependencies result in the need for highly precise data values (such as of the form i=j=k) for code coverage analysis. This requirement makes it very difficult for Genetic Algorithm (GA)-based approach to successfully search for the required test data from vast search spaces of real-life programs. Ariadne is the only Grammatical Evolution (GE)-based test data generation system, proposed to date, that uses grammars to exploit variable interdependencies to improve code coverage. Ariadne has been compared favourably to other well-known test data generation techniques in the literature; however, its scalability has not yet been tested for increasingly complex programs. This paper presents the results of a rigorous analysis performed to examine Ariadne’s scalability. We also designed and employed a large set of highly scalable 18 benchmark programs for our experiments. Our results suggest that Ariadne is highly scalable as it exhibited 100percent coverage across all the programs of increasing complexity with significantly smaller search costs than GA-based approaches, which failed even with huge search budgets.
%K genetic algorithms, genetic programming, grammatical evolution, code coverage analysis, scalability, software testing, search based software testing, automatic test data generation, evolutionary testing, variable interdependencies
%R doi:10.1145/3377930.3390167
%U https://doi.org/10.1145/3377930.3390167
%U http://dx.doi.org/doi:10.1145/3377930.3390167
%P 1213-1221

%0 Generic
%T A Novel Continuous Representation of Genetic Programmings using Recurrent Neural Networks for Symbolic Regression
%A Anjum, Aftab
%A Sun, Fengyang
%A Wang, Lin
%A Orchard, Jeff
%D 2019
%I arXiv
%F DBLP:journals/corr/abs-1904-03368
%K genetic algorithms, genetic programming
%U http://arxiv.org/abs/1904.03368

%0 Journal Article
%T Artificial Life Approach for Continuous Optimisation of Non Stationary Dynamical Systems
%A Annunziato, Mauro
%A Bruni, Carlo
%A Lucchetti, Matteo
%A Pizzuti, Stefano
%J Integrated Computer-Aided Engineering
%D 2003
%V 10
%N 2
%@ 1069-2509
%F AnnunziatoL2003:ICAE
%X In this paper, we develop an intelligent system to approach dynamical optimisation problems emerging in control of complex systems. In particular our proposal is to exploit the adaptivity of an artificial life (alife) environment in order to achieve ’not control rules but autonomous structures able to dynamically adapt and to generate optimised-control rules’. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to environmental changes). The suggested methodology has been tested on an energy regulation problem deriving from a classical testbed in dynamical systems experimentations: the Chua’s circuit. We supposed not to know the system dynamics and to be able to act only on a subset of control parameters, letting the others vary in time in a random discrete way. We let the optimisation process searching for the new best value of performance, whenever a drop due to changes in fitness landscape occurred. We present the most important results showing the effectiveness of the proposed approach in adapting to environmental non-stationary changes by recovering the optimal value of process performance.
%K genetic algorithms, genetic programming, artificial life
%9 journal article
%R doi:10.3233/ICA-2003-10202
%U http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00140
%U http://dx.doi.org/doi:10.3233/ICA-2003-10202
%P 111-125

%0 Conference Proceedings
%T A Novel Genetic Programming Algorithm with Knowledge Transfer for Uncertain Capacitated Arc Routing Problem
%A Ansari Ardeh, Mazhar
%A Mei, Yi
%A Zhang, Mengjie
%Y Nayak, Abhaya C.
%Y Sharma, Alok
%S PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part I
%S Lecture Notes in Computer Science
%D 2019
%V 11670
%I Springer
%F DBLP:conf/pricai/ArdehMZ19
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-030-29908-8_16
%U https://doi.org/10.1007/978-3-030-29908-8_16
%U http://dx.doi.org/doi:10.1007/978-3-030-29908-8_16
%P 196-200

%0 Conference Proceedings
%T Transfer Learning in Genetic Programming Hyper-heuristic for Solving Uncertain Capacitated Arc Routing Problem
%A Ansari Ardeh, Mazhar
%A Mei, Yi
%A Zhang, Mengjie
%Y Coello, Carlos A. Coello
%S 2019 IEEE Congress on Evolutionary Computation, CEC 2019
%D 2019
%8 October 13 jun
%I IEEE Press
%C Wellington, New Zealand
%F AnsariArdeh:2019:CEC
%X Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimization problem that has many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Generalisation is an open issue in the field of UCARP and in this direction, an open challenge is the case of changes in number of vehicles which currently leads to new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. Since none of the existing GP transfer methods have been used as a hyper-heuristic in solving UCARP, we conduct a comprehensive study to investigate the behaviour of the existing GP transfer methods for evolving routing policy in UCARP,
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2019.8789920
%U http://dx.doi.org/doi:10.1109/CEC.2019.8789920
%P 49-56

%0 Conference Proceedings
%T Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem
%A Ansari Ardeh, Mazhar
%A Mei, Yi
%A Zhang, Mengjie
%Y Allmendinger, Richard
%Y Cotta, Carlos
%Y Doerr, Carola
%Y Oliveto, Pietro S.
%Y Weise, Thomas
%Y Zamuda, Ales
%Y Auger, Anne
%Y Brockhoff, Dimo
%Y Hansen, Nikolaus
%Y Tusar, Tea
%Y Varelas, Konstantinos
%Y Camacho-Fernandez, David
%Y Vasile, Massimiliano
%Y Riccardi, Annalisa
%Y Derbel, Bilel
%Y Li, Ke
%Y Li, Xiaodong
%Y Zapotecas, Saul
%Y Zhang, Qingfu
%Y Akman, Ozgur
%Y Alyahya, Khulood
%Y Branke, Juergen
%Y Fieldsend, Jonathan
%Y Chugh, Tinkle
%Y Hakanen, Jussi
%Y Ceberio Uribe, Josu
%Y Santucci, Valentino
%Y Baioletti, Marco
%Y McCall, John
%Y Hart, Emma
%Y Tauritz, Daniel R.
%Y Woodward, John R.
%Y Nakayama, Koichi
%Y Oshima, Chika
%Y Wagner, Stefan
%Y Affenzeller, Michael
%Y Osaba, Eneko
%Y Del Ser, Javier
%Y Kerschke, Pascal
%Y Naujoks, Boris
%Y Volz, Vanessa
%Y Esparcia-Alcazar, Anna I.
%Y Alshammari, Riyad
%Y Hemberg, Erik
%Y Makanju, Tokunbo
%Y Alexander, Brad
%Y Haraldsson, Saemundur O.
%Y Wagner, Markus
%Y Fernandez Alzueta, Silvino
%Y Valledor Pellicer, Pablo
%Y Stuetzle, Thomas
%Y Walker, David
%Y Johns, Matt
%Y Ross, Nick
%Y Keedwell, Ed
%Y Nakata, Masaya
%Y Stein, Anthony
%Y Tatsumi, Takato
%Y Veerapen, Nadarajen
%Y Liefooghe, Arnaud
%Y Verel, Sebastien
%Y Ochoa, Gabriela
%Y Smith, Stephen
%Y Cagnoni, Stefano
%Y Patton, Robert M.
%Y La Cava, William
%Y Olson, Randal
%Y Orzechowski, Patryk
%Y Urbanowicz, Ryan
%Y Oyama, Akira
%Y Shimoyama, Koji
%Y Singh, Hemant Kumar
%Y Chiba, Kazuhisa
%Y Palar, Pramudita Satria
%Y Rahat, Alma
%Y Everson, Richard
%Y Wang, Handing
%Y Jin, Yaochu
%Y Gallagher, Marcus
%Y Preuss, Mike
%Y Teytaud, Olivier
%Y Lezama, Fernando
%Y Soares, Joao
%Y Vale, Zita
%S GECCO ’19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
%D 2019
%8 13 17 jul
%I ACM
%C Prague, Czech Republic
%F AnsariArdeh:2019:GECCOcomp
%K genetic algorithms, genetic programming
%R doi:10.1145/3319619.3321988
%U http://dx.doi.org/doi:10.1145/3319619.3321988
%P 334-335

%0 Conference Proceedings
%T An efficient evolutionary algorithm for solving incrementally structured problems
%A Ansel, Jason
%A Pacula, Maciej
%A Amarasinghe, Saman
%A O’Reilly, Una-May
%Y Krasnogor, Natalio
%Y Lanzi, Pier Luca
%Y Engelbrecht, Andries
%Y Pelta, David
%Y Gershenson, Carlos
%Y Squillero, Giovanni
%Y Freitas, Alex
%Y Ritchie, Marylyn
%Y Preuss, Mike
%Y Gagne, Christian
%Y Ong, Yew Soon
%Y Raidl, Guenther
%Y Gallager, Marcus
%Y Lozano, Jose
%Y Coello-Coello, Carlos
%Y Silva, Dario Landa
%Y Hansen, Nikolaus
%Y Meyer-Nieberg, Silja
%Y Smith, Jim
%Y Eiben, Gus
%Y Bernado-Mansilla, Ester
%Y Browne, Will
%Y Spector, Lee
%Y Yu, Tina
%Y Clune, Jeff
%Y Hornby, Greg
%Y Wong, Man-Leung
%Y Collet, Pierre
%Y Gustafson, Steve
%Y Watson, Jean-Paul
%Y Sipper, Moshe
%Y Poulding, Simon
%Y Ochoa, Gabriela
%Y Schoenauer, Marc
%Y Witt, Carsten
%Y Auger, Anne
%S GECCO ’11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%D 2011
%8 December 16 jul
%I ACM
%C Dublin, Ireland
%F Ansel:2011:GECCO
%X Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn’t appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.
%K genetic algorithms, genetic programming, SBSE, Real world applications
%R doi:10.1145/2001576.2001805
%U http://dx.doi.org/doi:10.1145/2001576.2001805
%P 1699-1706

%0 Journal Article
%T Deep Data Dives Discover Natural Laws
%A Anthes, Gary
%J Communications of the ACM
%D 2009
%8 nov
%V 52
%N 11
%@ 0001-0782
%F Anthes:2009:ACM
%O News
%X Computer scientists have found a way to bootstrap science, using evolutionary computation to find fundamental meaning in massive amounts of raw data. Mining scientific data for patterns and relationships has been a common practice for decades, and the use of self-mutating genetic algorithms is nothing new, either. But now a pair of computer scientists at Cornell University have pushed these techniques into an entirely new realm, one that could fundamentally transform the methods of science at the frontiers of research.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1145/1592761.1592768
%U http://cacm.acm.org/magazines/2009/11/48443-deep-data-dives-discover-natural-laws/pdf
%U http://dx.doi.org/doi:10.1145/1592761.1592768
%P 13-14

%0 Thesis
%T Evolving board evaluation fuctions for a complex strategy game
%A Anthony, Lisa Patricia
%D 2002
%8 dec  30
%C Drexel University
%G en_US
%F hdl:1860/18
%X The development of board evaluation functions for complex strategy games has been approached in a variety of ways. The analysis of game interactions is recognized as a valid analogy to common real-world problems, which often present difficulty in designing algorithms to solve them. Genetic programming, as a branch of evolutionary computation, provides advantages over traditional algorithms in solving these complex real-world problems in speed, robustness and flexibility. This thesis attempts to address the problem of applying genetic programming techniques to the evolution of a strategy for evaluating potential moves in a one-step lookahead intelligent agent heuristic for a complex strategybased game. This is meant to continue the work in artificial intelligence which seeks to provide computer systems with the tools they need to learn how to operate within a domain, given only the basic building blocks. The issues surrounding this problem are formulated and techniques are presented within the realm of genetic programming which aim to contribute to the solution of this problem. The domain chosen is the strategy game known as Acquire, whose object is to amass wealth while investing stock in hotel chains and effecting mergers of these chains as they grow. The evolution of the board evaluation functions to be used by agent players of the game is accomplished via genetic programming. Implementation details are discussed, empirical results are presented, and the strategies of some of the best players are analyzed. Future improvements on these techniques within this domain are outlined, as well as implications for artificial intelligence and genetic programming.
%K genetic algorithms, genetic programming
%9 Masters thesis
%U http://dspace.library.drexel.edu/handle/1721.1/18

%0 Journal Article
%T Facebook’s evolutionary search for crashing software bugs
%A Anthony, Sebastian
%J ars technica UK
%D 2017
%8 22 aug 07:52
%F Anthony:2017/08/facebook
%X Ars gets the first look at Facebook’s fancy new dynamic analysis tool.
%K genetic algorithms, genetic programming, genetic improvement, APR, SBSE, Facebook, Sapienz
%9 journal article
%U https://arstechnica.co.uk/information-technology/2017/08/facebook-dynamic-analysis-software-sapienz/

%0 Thesis
%T Evolutionary Tree Genetic Programming
%A Antolik, Jan
%D 2004
%C Manhattan, Kansas, USA
%C Department of Computing and Information Sciences, College of Arts and Sciences, Kansan State University
%F antolik:mastersthesis
%X We introduce an extension of a genetic programming (GP) algorithm we call Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree like pattern. We want to simulate similar behaviour in artificial evolutionary systems such as GP. In this thesis we provide multiple reasons why we believe simulation of this phenomenon can be beneficial for GP systems. We present various empirical results from test runs. As the test bed for our experiments two standard benchmark problems for GP systems are used, particularly the Artificial Ant problem and the Multiplexer problem. The performance of the ETGP algorithm is compared to the performance of GP system. Unfortunately no significant speedup is found. Some unexpected behaviors of our system are also identified, and a hypothesis is formulated that addresses the question of why we observe this strange behaviour and the lack of speedup. Suggestions on how to extend the ETGP system to overcome the problems identified by this hypothesis are then presented in the end of our concluding chapter.
%K genetic algorithms, genetic programming
%9 Master of Science
%9 Masters thesis
%U http://www.ms.mff.cuni.cz/~antoj9am/thesis.pdf

%0 Conference Proceedings
%T Evolutionary tree genetic programming
%A Antolik, Jan
%A Hsu, William H.
%Y Beyer, Hans-Georg
%Y O’Reilly, Una-May
%Y Arnold, Dirk V.
%Y Banzhaf, Wolfgang
%Y Blum, Christian
%Y Bonabeau, Eric W.
%Y Cantu-Paz, Erick
%Y Dasgupta, Dipankar
%Y Deb, Kalyanmoy
%Y Foster, James A.
%Y de Jong, Edwin D.
%Y Lipson, Hod
%Y Llora, Xavier
%Y Mancoridis, Spiros
%Y Pelikan, Martin
%Y Raidl, Guenther R.
%Y Soule, Terence
%Y Tyrrell, Andy M.
%Y Watson, Jean-Paul
%Y Zitzler, Eckart
%S GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%D 2005
%8 25 29 jun
%V 2
%I ACM Press
%C Washington DC, USA
%@ 1-59593-010-8
%F 1068312
%X We introduce a clustering-based method of subpopulation management in genetic programming (GP) called Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree-like phylogenetic pattern. Our goal is to simulate similar behavior in artificial evolutionary systems such as GP. To test our model we use three common GP benchmarks: the Ant Algorithm, 11-Multiplexer, and Parity problems.The performance of the ETGP system is empirically compared to those of the GP system. Code size and variance are consistently reduced by a small but statistically significant percentage, resulting in a slight speedup in the Ant and 11-Multiplexer problems, while the same comparisons on the Parity problem are inconclusive.
%K genetic algorithms, genetic programming, Poster
%R doi:10.1145/1068009.1068312
%U http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1789.pdf
%U http://dx.doi.org/doi:10.1145/1068009.1068312
%P 1789-1790

%0 Conference Proceedings
%T Evolutionary Fuzzy Classifiers for Imbalanced Datasets: An Experimental Comparison
%A Antonelli, Michela
%A Ducange, Pietro
%A Marcelloni, Francesco
%A Segatori, Armando
%S Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS 2013)
%D 2013
%8 jun
%F Antonelli:2013:NAFIPS
%X In this paper, we compare three state-of-the-art evolutionary fuzzy classifiers (EFCs) for imbalanced datasets. The first EFC performs an evolutionary data base learning with an embedded rule base generation. The second EFC builds a hierarchical fuzzy rule-based classifier (FRBC): first, a genetic programming algorithm is used to learn the rule base and then a post-process, which includes a genetic rule selection and a membership function parameters tuning, is applied to the generated FRBC. The third EFC is an extension of a multi-objective evolutionary learning scheme we have recently proposed: the rule base and the membership function parameters of a set of FRBCs are concurrently learnt by optimising the sensitivity, the specificity and the complexity. By performing non-parametric statistical tests, we show that, without re-balancing the training set, the third EFC outperforms, in terms of area under the ROC curve, the other comparison approaches.
%K genetic algorithms, genetic programming, database management systems, fuzzy set theory, learning (artificial intelligence), pattern classification, statistical testing, EFC, FRBC, ROC curve, complexity optimisation, embedded rule base generation, evolutionary data base learning, evolutionary fuzzy classifiers, genetic programming algorithm, genetic rule selection, hierarchical fuzzy rule-based classifier, imbalanced datasets, membership function parameters tuning, multiobjective evolutionary learning scheme, nonparametric statistical tests, rule base learning, sensitivity optimisation, specificity optimisation, Accuracy, Biological cells, Complexity theory, Genetics, Input variables, Training, Tuning, Fuzzy Rule-based Classifiers, Genetic and Evolutionary Fuzzy Systems, Imbalanced Datasets
%R doi:10.1109/IFSA-NAFIPS.2013.6608367
%U http://dx.doi.org/doi:10.1109/IFSA-NAFIPS.2013.6608367
%P 13-18

%0 Conference Proceedings
%T A Gene Expression Programming Environment for Fatigue Modeling of Composite Materials
%A Antoniou, Maria A.
%A Georgopoulos, Efstratios F.
%A Theofilatos, Konstantinos A.
%A Vassilopoulos, Anastasios P.
%A Likothanassis, Spiridon D.
%Y Konstantopoulos, Stasinos
%Y Perantonis, Stavros J.
%Y Karkaletsis, Vangelis
%Y Spyropoulos, Constantine D.
%Y Vouros, George A.
%S 6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications (SETN 2010)
%S Lecture Notes in Computer Science
%D 2010
%8 may 4 7
%V 6040
%I Springer
%C Athens, Greece
%F conf/setn/AntoniouGTVL10
%X In the current paper is presented the application of a Gene Expression Programming Environment in modeling the fatigue behavior of composite materials. The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques. In order to evaluate the performance of the presented environment, we tested it in fatigue modeling of composite materials.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-642-12842-4
%U http://dx.doi.org/doi:10.1007/978-3-642-12842-4
%P 297-302

%0 Conference Proceedings
%T Forecasting Euro - United States Dollar Exchange Rate with Gene Expression Programming
%A Antoniou, Maria
%A Georgopoulos, Efstratios
%A Theofilatos, Konstantinos
%A Likothanassis, Spiridon
%Y Papadopoulos, Harris
%Y Andreou, Andreas
%Y Bramer, Max
%S 6th IFIP Advances in Information and Communication Technology AIAI 2010
%S IFIP Advances in Information and Communication Technology
%D 2010
%8 oct 6 7
%V 339
%I Springer
%C Larnaca, Cyprus
%F Antoniou:2010:AIAI
%X In the current paper we present the application of our Gene Expression Programming Environment in forecasting Euro-United States Dollar exchange rate. Specifically, using the GEP Environment we tried to forecast the value of the exchange rate using its previous values. The data for the EURO-USD exchange rate are online available from the European Central Bank (ECB). The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques.
%K genetic algorithms, genetic programming, Gene Expression Programming
%R doi:10.1007/978-3-642-16239-8_13
%U http://dx.doi.org/doi:10.1007/978-3-642-16239-8_13
%P 78-85

%0 Conference Proceedings
%T A Grammar-Based Genetic Algorithm
%A Antonisse, Hendrik James
%Y Rawlins, Gregory J. E.
%S Foundations of Genetic Algorithms
%D 1991
%8 15–18 jul 1990
%I Morgan Kaufmann
%C Indiana University, Bloomington, USA
%@ 1-55860-170-8
%F foga90*193
%X High-level syntactically-based representations pose problems for applying the GA because it is hard to construct crossover operators that always result in legal offspring. This paper proposes a reformulation of the genetic algorithm that makes it appropriate to any representation that can be cast in a formal grammar. This reformulation is consistent with recent reinterpretations of GA foundations in set-theoretic terms, and concentrates on the modifications required to make the space of legal structures closed under the crossover operator. The analysis places no restriction on the form of the grammars.
%K genetic algorithms, genetic programming, inductive bias, high-level representations, crossover
%R doi:10.1016/B978-0-08-050684-5.50015-X
%U http://dx.doi.org/doi:10.1016/B978-0-08-050684-5.50015-X
%P 193-204

%0 Conference Proceedings
%T Automatic construction of tree-structural image transformation using genetic programming
%A Aoki, Shinya
%A Nagao, Tomoharu
%S Proceedings of the 1999 International Conference on Image Processing (ICIP-99)
%D 1999
%8 oct  24–28
%V 1
%I IEEE
%C Kobe
%F ICIP99_Vol1*529
%X We previously proposed an automatic construction method of image transformations. In this method, we approximated an unknown image transformation by a series of several known image filters, and a genetic algorithm optimizes their combination to meet the processing purpose presented by sets of original and target images. In this paper, we propose an extended method named ’Automatic Construction of Tree-structural Image Transformations (ACTIT)’. In this new method, a tree whose interior nodes are image filters and leaf ones are input images approximates the transformation. The structures of the trees are optimized using genetic programming. ACTIT finds practical filter combinations that are too complicated to be designed by hand. It can be applied to various kinds of image processing tasks. We show examples of its applications to document and medical image processing
%K genetic algorithms, genetic programming, automatic construction, image filters, medical image processing, tree-structural image transformation, image coding, image processing
%R doi:10.1109/ICIP.1999.821685
%U http://dx.doi.org/doi:10.1109/ICIP.1999.821685
%P 529-533

%0 Conference Proceedings
%T Populations are Multisets-PLATO
%A Aparicio, Joaquim N.
%A Correia, Luis
%A Moura-Pires, Fernando
%Y Banzhaf, Wolfgang
%Y Daida, Jason
%Y Eiben, Agoston E.
%Y Garzon, Max H.
%Y Honavar, Vasant
%Y Jakiela, Mark
%Y Smith, Robert E.
%S Proceedings of the Genetic and Evolutionary Computation Conference
%D 1999
%8 13 17 jul
%V 2
%I Morgan Kaufmann
%C Orlando, Florida, USA
%@ 1-55860-611-4
%F aparicio:1999:PM
%K methodology, pedagogy and philosophy
%U http://gpbib.cs.ucl.ac.uk/gecco1999/MP-603.ps
%P 1845-1850

%0 Conference Proceedings
%T An Analysis of Exchanging Fitness Cases with Population Size in Symbolic Regression Genetic Programming with Respect to the Computational Model
%A Applegate, Douglas
%A Mayfield, Blayne
%Y de la Fraga, Luis Gerardo
%S 2013 IEEE Conference on Evolutionary Computation
%D 2013
%8 jun 20 23
%V 1
%C Cancun, Mexico
%F Applegate:2013:CEC
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2013.6557949
%U http://dx.doi.org/doi:10.1109/CEC.2013.6557949
%P 3111-3116

%0 Conference Proceedings
%T A gene expression programming approach for evolving multi-class image classifiers
%A Aquino, Nelson Marcelo Romero
%A Ribeiro, Manasses
%A Gutoski, Matheus
%A Vargas Benitez, Cesar Manuel
%A Lopes, Heitor Silverio
%S 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
%D 2017
%I IEEE
%F conf/lacci/AquinoRGBL17
%X This paper presents a methodology to perform multi-class image classification using Gene Expression Programming(GEP) in both balanced and unbalanced datasets. Descriptors are extracted from images and then their dimensionality are reduced by applying Principal Component Analysis. The aspects extracted from images are texture, colour and shape that are, later, concatenated in a feature vector. Finally, GEP is used to evolve trees capable of performing as classifiers using the features as terminals. The quality of the solution evolved is evaluated by the introduced Cross-Entropy-Loss-based fitness function and compared with standard fitness function (both accuracy and product of sensibility and specificity). A novel GEP function linker Softmax-based is introduced. GEP performance is compared with the obtained by classifiers with tree structure, as C4.5 and Random Forest algorithms. Results show that GEP is capable of evolving classifiers able to achieve satisfactory results for image multi-class classification.
%K genetic algorithms, genetic programming, gene expression programming
%R doi:10.1109/LA-CCI.2017.8285696
%U http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8275062
%U http://dx.doi.org/doi:10.1109/LA-CCI.2017.8285696

%0 Journal Article
%T QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network
%A Arakawa, Masamoto
%A Hasegawa, Kiyoshi
%A Funatsu, Kimito
%J Chemometrics and Intelligent Laboratory Systems
%D 2006
%8 15 sep
%V 83
%N 2
%F Arakawa:2006:CILS
%X Quantitative structure-activity relationship (QSAR) has been developed for a set of inhibitors of the human immunodeficiency virus 1 (HIV-1) reverse transcriptase, derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT). Structural descriptors used in this study are Hansch constants for each substituent and topological descriptors. We have applied the variable selection method based on multi-objective genetic programming (GP) to the HEPT data and constructed the nonlinear QSAR model using counter-propagation (CP) neural network with the selected variables. The obtained network is accurate and interpretable. Moreover in order to confirm a predictive ability of the model, a validation test was performed.
%K genetic algorithms, genetic programming, Multi-objective optimisation, Variable selection, HEPT, quantitative structure activity relationship
%9 journal article
%R doi:10.1016/j.chemolab.2006.01.009
%U http://dx.doi.org/doi:10.1016/j.chemolab.2006.01.009
%P 91-98

%0 Conference Proceedings
%T The effect of using evolutionary algorithms on ant clustering techniques
%A Aranha, Claus
%A Iba, Hitoshi
%Y Pham, The Long
%Y Le, Hai Khoi
%Y Nguyen, Xuan Hoai
%S Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%D 2006
%C Military Technical Academy, Hanoi, VietNam
%F Aranha:2006:ASPGP
%X Ant-based clustering is a biologically inspired data clustering technique. In this technique, multiple agents carry the information to be clustered, and make local comparisons. In this work we use genetic algorithms to improve the implementation and use of ant-clustering techniques.
%K genetic algorithms, genetic programming
%U http://gpbib.cs.ucl.ac.uk/aspgp06/Aranha_2006_ASPGP.pdf
%P 24-34

%0 Conference Proceedings
%T Effectiveness of scale-free properties in genetic programming
%A Araseki, Hitoshi
%S Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on
%D 2012
%8 20 24 nov
%C Kobe
%F Araseki:2012:SCIS
%X In this paper, we propose a new selection method, named scale-free selection, which is based on a scale-free network. Through study of the complex network, scale-free networks have been found in various fields. In recent years, it has been proposed that a scale-free property be applied to some optimisation problems. We investigate if the new selection method is an effective selection method to apply to genetic programming. Our experimental results on three benchmark problems show that performance of the scale-free selection model is similar to the usual selection methods in spite of different optimisations and may be able to resolve the bloating problem in genetic programming. Further, we show that the optimisation problem is relevant to complex network study.
%R doi:10.1109/SCIS-ISIS.2012.6505204
%U http://dx.doi.org/doi:10.1109/SCIS-ISIS.2012.6505204
%P 285-289

%0 Conference Proceedings
%T Genetic Programming with Scale-Free Dynamics
%A Araseki, Hitoshi
%Y Emmerich, Michael
%Y Deutz, Andre
%Y Schuetze, Oliver
%Y Baeck, Thomas
%Y Tantar, Emilia
%Y Del Moral, Pierre
%Y Legrand, Pierrick
%Y Bouvry, Pascal
%Y Coello, Carlos A.
%E Alexandru-Adrian
%S EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV
%S Advances in Intelligent Systems and Computing
%D 2013
%8 jul 10 13
%V 227
%I Springer
%C Leiden, Holland
%F Araseki:2013:EVOLVE
%X This paper describe a new selection method, named SFSwT (Scale-Free Selection method with Tournament mechanism) which is based on a scale-free network study. A scale-free selection model was chosen in order to generate a scale-free structure. The proposed model reduces computational complexity and improves computational performance compared with a previous version of the model. Experimental results with various benchmark problems show that performance of the SFSwT is higher than with other selection methods. In various fields, scale-free structures are closely related to evolutionary computation. Further, it was found through the experiments that the distribution of node connectivity could be used as an index of search efficiency.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-319-01128-8_18
%U http://dx.doi.org/doi:10.1007/978-3-319-01128-8_18
%P 277-291

%0 Conference Proceedings
%T A parallel genetic algorithm for rule discovery in large databases
%A Araujo, Dieferson L. A.
%A Lopes, Heitor S.
%A Freitas, Alex A.
%S Proceedings of IEEE Systems, Man and Cybernetics Conference
%D 1999
%V III
%F Die99
%O Tokyo, Japan, 12-15/october/1999
%K genetic algorithms, data mining, parallel
%U http://www.cpgei.cefetpr.br/publicacoes/1999/ieeesmc99.zip
%P 940-945

%0 Conference Proceedings
%T Rule discovery with a parallel genetic algorithm
%A Araujo, Dieferson L. A.
%A Lopes, Heitor S.
%A Freitas, Alex A.
%Y Freitas, Alex A.
%Y Hart, William
%Y Krasnogor, Natalio
%Y Smith, Jim
%S Data Mining with Evolutionary Algorithms
%D 2000
%8 August
%C Las Vegas, Nevada, USA
%F araujo:2000:R
%K genetic algorithms, data mining, parallel
%U http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/gecco2000b.zip
%P 89-94

%0 Conference Proceedings
%T Using Genetic Programming and High Level Synthesis to Design Optimized Datapath
%A Araujo, Sergio G.
%A Mesquita, A.
%A Pedroza, Aloysio C. P.
%Y Tyrrell, Andy M.
%Y Haddow, Pauline C.
%Y Torresen, Jim
%S Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003
%S LNCS
%D 2003
%8 17 20 mar
%V 2606
%I Springer-Verlag
%C Trondheim, Norway
%@ 3-540-00730-X
%F araujo:2003:ICES
%X a methodology to design optimised electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level synthesis tools to automatically improve design structural quality (area measure). A two-stage, multiobjective optimization algorithm is used to search for circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation datapath design targeted to FPGA exemplifies the proposed methodology.
%K genetic algorithms, genetic programming
%R doi:10.1007/3-540-36553-2_39
%U http://dx.doi.org/doi:10.1007/3-540-36553-2_39
%P 434-445

%0 Conference Proceedings
%T Síntese de Circuitos Digitais Otimizados via ProgramaÇão Genética
%A de Araujo, Sergio Granato
%A Mesquita, Antonio C.
%A Pedroza, Aloysio C. P.
%S XXX Seminário Integrado de Software e Hardware
%D 2003
%8 February 8 aug
%V III
%C Unicamp, Campinas, SP, Brasil
%G por; eng
%F semish2003meta007
%X This paper presents a methodology for the design of optimized electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level synthesis tools to improve the design quality (area optimization). A two-stage, multiobjective optimization algorithm was used to search for circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation function design targeted to FPGA illustrates the methodology.
%K genetic algorithms, genetic programming
%U http://www.lbd.dcc.ufmg.br/bdbcomp/servlet/Trabalho?id=2490
%P 273-285

%0 Conference Proceedings
%T Genetic Programming for Natural Language Parsing
%A Araujo, Lourdes
%Y Keijzer, Maarten
%Y O’Reilly, Una-May
%Y Lucas, Simon M.
%Y Costa, Ernesto
%Y Soule, Terence
%S Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%D 2004
%8 May 7 apr
%V 3003
%I Springer-Verlag
%C Coimbra, Portugal
%@ 3-540-21346-5
%F araujo:2004:eurogp
%X Our aim is to prove the effectiveness of the genetic programming approach in automatic parsing of sentences of real texts. Classical parsing methods are based on complete search techniques to find the different interpretations of a sentence. However, the size of the search space increases exponentially with the length of the sentence or text to be parsed and the size of the grammar, so that exhaustive search methods can fail to reach a solution in a reasonable time. This paper presents the implementation of a probabilistic bottom-up parser based on genetic programming which works with a population of partial parses, i.e. parses of sentence segments. The quality of the individuals is computed as a measure of its probability, which is obtained from the probability of the grammar rules and lexical tags involved in the parse. In the approach adopted herein, the size of the trees generated is limited by the length of the sentence. In this way, the size of the search space, determined by the size of the sentence to parse, the number of valid lexical tags for each words and specially by the size of the grammar, is also limited.
%K genetic algorithms, genetic programming: Poster
%R doi:10.1007/978-3-540-24650-3_21
%U http://dx.doi.org/doi:10.1007/978-3-540-24650-3_21
%P 230-239

%0 Conference Proceedings
%T Multiobjective Genetic Programming for Natural Language Parsing and Tagging
%A Araujo, L.
%Y Runarsson, Thomas Philip
%Y Beyer, Hans-Georg
%Y Burke, Edmund
%Y Merelo-Guervos, Juan J.
%Y Whitley, L. Darrell
%Y Yao, Xin
%S Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%D 2006
%8 September 13 sep
%V 4193
%I Springer-Verlag
%C Reykjavik, Iceland
%@ 3-540-38990-3
%F Araujo:PPSN:2006
%X Parsing and Tagging are very important tasks in Natural Language Processing. Parsing amounts to searching the correct combination of grammatical rules among those compatible with a given sentence. Tagging amounts to labelling each word in a sentence with its lexical category and, because many words belong to more than one lexical class, it turns out to be a disambiguation task. Because parsing and tagging are related tasks, its simultaneous resolution can improve the results of both of them. This work aims developing a multiobjective genetic program to perform simultaneously statistical parsing and tagging. It combines the statistical data about grammar rules and about tag sequences to guide the search of the best structure. Results show that any of the implemented multiobjective optimisation models improve on the results obtained in the resolution of each problem separately.
%K genetic algorithms, genetic programming
%R doi:10.1007/11844297_44
%U http://ppsn2006.raunvis.hi.is/proceedings/055.pdf
%U http://dx.doi.org/doi:10.1007/11844297_44
%P 433-442

%0 Conference Proceedings
%T Evolving natural language grammars without supervision
%A Araujo, Lourdes
%A Santamaria, Jesus
%S IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%8 18 23 jul
%I IEEE Press
%C Barcelona, Spain
%F Araujo:2010:cec
%X Unsupervised grammar induction is one of the most difficult works of language processing. Its goal is to extract a grammar representing the language structure using texts without annotations of this structure. We have devised an evolutionary algorithm which for each sentence evolves a population of trees that represent different parse trees of that sentence. Each of these trees represent a part of a grammar. The evaluation function takes into account the contexts in which each sequence of Part-Of-Speech tags (POSseq) appears in the training corpus, as well as the frequencies of those POSseqs and contexts. The grammar for the whole training corpus is constructed in an incremental manner. The algorithm has been evaluated using a well known Annotated English corpus, though the annotation have only been used for evaluation purposes. Results indicate that the proposed algorithm is able to improve the results of a classical optimisation algorithm, such as EM (Expectation Maximisation), for short grammar constituents (right side of the grammar rules), and its precision is better in general.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2010.5586291
%U http://dx.doi.org/doi:10.1109/CEC.2010.5586291

%0 Conference Proceedings
%T Grammatical Evolution for Identifying Wikipedia Taxonomies
%A Araujo, Lourdes
%A Martinez-Romo, Juan
%A Duque, Andres
%Y Silva, Sara
%Y Esparcia-Alcazar, Anna I.
%Y Lopez-Ibanez, Manuel
%Y Mostaghim, Sanaz
%Y Timmis, Jon
%Y Zarges, Christine
%Y Correia, Luis
%Y Soule, Terence
%Y Giacobini, Mario
%Y Urbanowicz, Ryan
%Y Akimoto, Youhei
%Y Glasmachers, Tobias
%Y Fernandez de Vega, Francisco
%Y Hoover, Amy
%Y Larranaga, Pedro
%Y Soto, Marta
%Y Cotta, Carlos
%Y Pereira, Francisco B.
%Y Handl, Julia
%Y Koutnik, Jan
%Y Gaspar-Cunha, Antonio
%Y Trautmann, Heike
%Y Mouret, Jean-Baptiste
%Y Risi, Sebastian
%Y Costa, Ernesto
%Y Schuetze, Oliver
%Y Krawiec, Krzysztof
%Y Moraglio, Alberto
%Y Miller, Julian F.
%Y Widera, Pawel
%Y Cagnoni, Stefano
%Y Merelo, J. J.
%Y Hart, Emma
%Y Trujillo, Leonardo
%Y Kessentini, Marouane
%Y Ochoa, Gabriela
%Y Chicano, Francisco
%Y Doerr, Carola
%S GECCO Companion ’15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
%D 2015
%8 November 15 jul
%I ACM
%C Madrid, Spain
%F Araujo:2015:GECCOcomp
%X This work applies Grammatical Evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a concept and is cross-linked by hyperlinks that connect related concepts. Hierarchical taxonomies and their generalization to ontologies are a highly useful resource for many applications by enabling semantic search and reasoning. We have developed a system which arranges a set of Wikipedia concepts into a taxonomy.
%K genetic algorithms, genetic programming, grammatical evolution: Poster
%R doi:10.1145/2739482.2764629
%U http://doi.acm.org/10.1145/2739482.2764629
%U http://dx.doi.org/doi:10.1145/2739482.2764629
%P 1345-1346

%0 Journal Article
%T Discovering taxonomies in Wikipedia by means of grammatical evolution
%A Araujo, Lourdes
%A Martinez-Romo, Juan
%A Fernandez, Andres Duque
%J Soft Computing
%D 2018
%V 22
%N 9
%F AraujoMF18
%X This work applies grammatical evolution to identify taxonomic hierarchies of concepts from Wikipedia. Each article in Wikipedia covers a topic and is cross-linked by hyperlinks that connect related topics. Hierarchical taxonomies and their generalization to ontologies are a highly useful resource for many applications since they enable semantic search and reasoning. Thus, the automatic identification of taxonomies composed of concepts associated with linked Wikipedia pages has attracted much attention. We have developed a system which arranges a set of Wikipedia concepts into a taxonomy. This technique is based on the relationships among a set of features extracted from the contents of the Wikipedia pages. We have used a grammatical evolution algorithm to discover the best way of combining the considered features in an explicit function. Candidate functions are evaluated by applying a genetic algorithm to approximate the optimal taxonomy that the function can provide for a number of training cases. The fitness is computed as an average of the precision obtained by comparing, for the set of training cases, the taxonomy provided by the evaluated function with the reference one. Experimental results show that the proposal is able to provide valuable functions to find high-quality taxonomies.
%K genetic algorithms, genetic programming, grammatical evolution
%9 journal article
%R doi:10.1007/s00500-017-2544-4
%U https://doi.org/10.1007/s00500-017-2544-4
%U http://dx.doi.org/doi:10.1007/s00500-017-2544-4
%P 2907-2919

%0 Journal Article
%T Genetic programming for natural language processing
%A Araujo, Lourdes
%J Genetic Programming and Evolvable Machines
%D 2020
%8 jun
%V 21
%N 1-2
%@ 1389-2576
%F Araujo:GPEM20
%O Twentieth Anniversary Issue
%X This work takes us through the literature on applications of genetic programming to problems of natural language processing. The purpose of natural language processing is to allow us to communicate with computers in natural language. Among the problems addressed in the area is, for example, the extraction of information, which draws relevant data from unstructured texts written in natural language. There are also domains of application of particular relevance because of the difficulty in dealing with the corresponding documents, such as opinion mining in social networks, or because of the need for high precision in the information extracted, such as the biomedical domain. There have been proposals to apply genetic programming techniques in several of these areas. This tour allows us to observe the potential (not yet fully exploited) of such applications. We also review some cases in which genetic programming can provide information that is absent from other approaches, revealing its ability to provide easy to interpret results, in form of programs or functions. Finally, we identify some important challenges in the area.
%K genetic algorithms, genetic programming, Grammatical evolution, NLP, Natural language processing, Applications, Challenges
%9 journal article
%R doi:10.1007/s10710-019-09361-5
%U http://dx.doi.org/doi:10.1007/s10710-019-09361-5
%P 11-32

%0 Journal Article
%T Learning predictors for flash memory endurance: a comparative study of alternative classification methods
%A Arbuckle, Tom
%A Hogan, Damien
%A Ryan, Conor
%J International Journal of Computational Intelligence Studies
%D 2014
%8 jan  14
%V 3
%N 1
%I Inderscience Publishers
%@ 1755-4985
%G eng
%F Arbuckle:2014:IJCISTUDIES
%X Flash memory’s ability to be programmed multiple times is called its endurance. Beyond being able to give more accurate chip specifications, more precise knowledge of endurance would permit manufacturers to use flash chips more effectively. Rather than physical testing to determine chip endurance, which is impractical because it takes days and destroys an area of the chip under test, this research seeks to predict whether chips will meet chosen endurance criteria. Timing data relating to erasure and programming operations is gathered as the basis for modelling. The purpose of this paper is to determine which methods can be used on this data to accurately and efficiently predict endurance. Traditional statistical classification methods, support vector machines and genetic programming are compared. Cross-validating on common datasets, the classification methods are evaluated for applicability, accuracy and efficiency and their respective advantages and disadvantages are quantified.
%K genetic algorithms, genetic programming, flash memory endurance, performance prediction, linear programming, support vector machines, SVMs, learning predictors, classification methods, timing data, erasure, programming, modelling
%9 journal article
%R doi:10.1504/IJCISTUDIES.2014.058644
%U http://www.inderscience.com/link.php?id=58644
%U http://dx.doi.org/doi:10.1504/IJCISTUDIES.2014.058644
%P 18-39

%0 Conference Proceedings
%T Semi-supervised genetic programming for classification
%A de Lima Arcanjo, Filipe
%A Pappa, Gisele Lobo
%A Bicalho, Paulo Viana
%A da Silva, Altigran Soares
%Y Krasnogor, Natalio
%Y Lanzi, Pier Luca
%Y Engelbrecht, Andries
%Y Pelta, David
%Y Gershenson, Carlos
%Y Squillero, Giovanni
%Y Freitas, Alex
%Y Ritchie, Marylyn
%Y Preuss, Mike
%Y Gagne, Christian
%Y Ong, Yew Soon
%Y Raidl, Guenther
%Y Gallager, Marcus
%Y Lozano, Jose
%Y Coello-Coello, Carlos
%Y Silva, Dario Landa
%Y Hansen, Nikolaus
%Y Meyer-Nieberg, Silja
%Y Smith, Jim
%Y Eiben, Gus
%Y Bernado-Mansilla, Ester
%Y Browne, Will
%Y Spector, Lee
%Y Yu, Tina
%Y Clune, Jeff
%Y Hornby, Greg
%Y Wong, Man-Leung
%Y Collet, Pierre
%Y Gustafson, Steve
%Y Watson, Jean-Paul
%Y Sipper, Moshe
%Y Poulding, Simon
%Y Ochoa, Gabriela
%Y Schoenauer, Marc
%Y Witt, Carsten
%Y Auger, Anne
%A Wagner Meira, Jr.
%S GECCO ’11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%D 2011
%8 December 16 jul
%I ACM
%C Dublin, Ireland
%F Arcanjo:2011:GECCO
%X Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available. Semi-supervised learning, which builds models from a small set of labeled examples and a potential large set of unlabeled examples, is a paradigm that may effectively use those unlabeled data. Here we propose KGP, a semi-supervised transductive genetic programming algorithm for classification. Apart from being one of the first semi-supervised algorithms, it is transductive (instead of inductive), i.e., it requires only a training dataset with labeled and unlabeled examples, which should represent the complete data domain. The algorithm relies on the three main assumptions on which semi-supervised algorithms are built, and performs both global search on labeled instances and local search on unlabeled instances. Periodically, unlabeled examples are moved to the labeled set after a weighted voting process performed by a committee. Results on eight UCI datasets were compared with Self-Training and KNN, and showed KGP as a promising method for semi-supervised learning.
%K genetic algorithms, genetic programming, Genetics based machine learning
%R doi:10.1145/2001576.2001746
%U http://dx.doi.org/doi:10.1145/2001576.2001746
%P 1259-1266

%0 Conference Proceedings
%T Induction of linear genetic programs for relational database manipulation
%A Archanjo, Gabriel A.
%A Von Zuben, Fernando J.
%S IEEE International Conference on Information Reuse and Integration (IRI 2011)
%D 2011
%8 March 5 aug
%C Las Vegas, USA
%F Archanjo:2011:IRI
%X In virtually all fields of human activity, softwares are used to manage processes and manipulate information, usually stored in computer databases. In fields like Knowledge Discovery and Data Mining (KDD), different approaches have been used to extract patterns or more meaningful information from datasets, including genetic programming. Nevertheless, the induction of programs that not only query data, but also manipulate it, has not been widely explored. This work presents Linear Genetic Programming for Databases (LGPDB), a tool to induce programs manipulating entities stored in a relational database. It combines a Linear Genetic Programming (LGP) induction environment and a simple relational database management system (RDBMS). A hypothetical library system is used to show LGPDB in action. Programs were induced to provide a set of selected features for this system and results indicate that genetic programming can be used to model processes that query, delete, insert and update records in a relational database.
%K genetic algorithms, genetic programming, KDD, Knowledge Discovery and Data Mining, LGPDB, Linear Genetic Programming for Databases, hypothetical library system, relational database management system, data mining, relational databases
%R doi:10.1109/IRI.2011.6009572
%U http://dx.doi.org/doi:10.1109/IRI.2011.6009572
%P 347-352

%0 Journal Article
%T Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems
%A Archanjo, Gabriel A.
%A Von Zuben, Fernando J.
%J Advances in Software Engineering
%D 2012
%I Hindawi Publishing Corporation
%@ 16878655
%G eng
%F Archanjo:2012:ASE
%X Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks.
%K genetic algorithms, genetic programming, genetic improvement, SBSE, SQL
%9 journal article
%R doi:10.1155/2012/893701
%U http://www.hindawi.com/journals/ase/2012/893701/
%U http://dx.doi.org/doi:10.1155/2012/893701

%0 Conference Proceedings
%T Genetic programming for human oral bioavailability of drugs
%A Archetti, Francesco
%A Lanzeni, Stefano
%A Messina, Enza
%A Vanneschi, Leonardo
%Y Keijzer, Maarten
%Y Cattolico, Mike
%Y Arnold, Dirk
%Y Babovic, Vladan
%Y Blum, Christian
%Y Bosman, Peter
%Y Butz, Martin V.
%Y Coello Coello, Carlos
%Y Dasgupta, Dipankar
%Y Ficici, Sevan G.
%Y Foster, James
%Y Hernandez-Aguirre, Arturo
%Y Hornby, Greg
%Y Lipson, Hod
%Y McMinn, Phil
%Y Moore, Jason
%Y Raidl, Guenther
%Y Rothlauf, Franz
%Y Ryan, Conor
%Y Thierens, Dirk
%S GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%D 2006
%8 August 12 jul
%V 1
%I ACM Press
%C Seattle, Washington, USA
%@ 1-59593-186-4
%F 1144042
%X Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of view of the accurateness of the solutions proposed, of the generalisation capabilities and of the correlation between predicted data and correct ones. Our work represents a first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of molecules into classes with similar bioavailability. Categories and Subject Descriptors
%K genetic algorithms, genetic programming, Biological Applications, bioavailability, bioinformatics, complexity measures, molecular descriptors, performance measures, SVM, ANN, LLSR, CFS, PCA, AIC, feature selection, SMILES
%R doi:10.1145/1143997.1144042
%U http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p255.pdf
%U http://dx.doi.org/doi:10.1145/1143997.1144042
%P 255-262

%0 Conference Proceedings
%T Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs
%A Archetti, Francesco
%A Lanzeni, Stefano
%A Messina, Enza
%A Vanneschi, Leonardo
%Y Marchiori, Elena
%Y Moore, Jason H.
%Y Rajapakse, Jagath C.
%S EvoBIO 2007, Proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
%S Lecture Notes in Computer Science
%D 2007
%8 apr 11 13
%V 4447
%I Springer
%C Valencia, Spain
%@ 3-540-71782-X
%F Archetti:2007:evobio
%X Computational methods allowing reliable pharmacokinetics predictions for newly synthesised compounds are critically relevant for drug discovery and development. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterise the harmful effects and the distribution into human body of a drug, their accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting pharmacokinetics parameters, both from the point of view of the accurateness and of the generalisation ability.
%K genetic algorithms, genetic programming
%R doi:10.1007/978-3-540-71783-6_2
%U http://dx.doi.org/doi:10.1007/978-3-540-71783-6_2
%P 11-23

%0 Journal Article
%T Genetic programming for computational pharmacokinetics in drug discovery and development
%A Archetti, Francesco
%A Lanzeni, Stefano
%A Messina, Enza
%A Vanneschi, Leonardo
%J Genetic Programming and Evolvable Machines
%D 2007
%8 dec
%V 8
%N 4
%@ 1389-2576
%F Archetti:2007:GPEM
%O special issue on medical applications of Genetic and Evolutionary Computation
%X The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient’s organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesised compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient’s organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalisation ability.
%K genetic algorithms, genetic programming, Computational pharmacokinetics, Drug discovery, QSAR
%9 journal article
%R doi:10.1007/s10710-007-9040-z
%U http://dx.doi.org/doi:10.1007/s10710-007-9040-z
%P 413-432

%0 Conference Proceedings
%T Classification of colon tumor tissues using genetic programming
%A Archetti, Francesco
%A Castelli, Mauro
%A Giordani, Ilaria
%A Vanneschi, Leonardo
%Y Serra, J. Roberto
%Y Villani, Marco
%Y Poli, Irene
%S Artificial Life and Evolutionary Computation: Proceedings of Wivace 2008
%D 2008
%8 August 10 sep
%I World Scientific Publishing Co.
%C Venice, Italy
%F Archetti:2008:wivace
%X A Genetic Programming (GP) framework for classification is presented in this paper and applied to a publicly available biomedical microarray dataset representing a collection of expression measurements from colon biopsy experiments [3]. We report experimental results obtained using two different well known fitness criteria: the area under the receiving operating curve (ROC) and the percentage of correctly classified instances (CCI). These results, and their comparison with the ones obtained by three non-evolutionary Machine Learning methods (Support Vector Machines, Voted Perceptron and Random Forests) on the same data, seem to hint that GP is a promising technique for this kind of classification both from the viewpoint of the accuracy of the proposed solutions and of the generalisation ability. These results are encouraging and should pave the way to a deeper study of GP for classification applied to biomedical microarray data sets.
%K genetic algorithms, genetic programming
%U ftp://ftp.ce.unipr.it/pub/cagnoni/WIV08/paper%202.pdf
%P 49-58

%0 Journal Article
%T Genetic programming for QSAR investigation of docking energy
%A Archetti, Francesco
%A Giordani, Ilaria
%A Vanneschi, Leonardo
%J Applied Soft Computing
%D 2010
%8 jan
%V 10
%N 1
%@ 1568-4946
%F Archetti2010170
%X Statistical methods, and in particular Machine Learning, have been increasingly used in the drug development workflow to accelerate the discovery phase and to eliminate possible failures early during clinical developments. In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced toxicity and (ii) evaluation of the target drug chemical interaction based on chemical descriptors. Among the numerous existing Machine Learning methods and their application to drug development (see for instance [F. Yoshida, J.G. Topliss, QSAR model for drug human oral bioavailability, Journal of Medicinal Chemistry 43 (2000) 2575-2585; Frohlich, J. Wegner, F. Sieker, A. Zell, Kernel functions for attributed molecular graphs - a new similarity based approach to ADME prediction in classification and regression, QSAR and Combinatorial Science, 38(4) (2003) 427-431; C.W. Andrews, L. Bennett, L.X. Yu, Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship, Pharmacological Research 17 (2000) 639-644; J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan, S.S. Young, Predictive toxicology: benchmarking molecular descriptors and statistical methods, Journal of Chemical Information Computer Science 43 (2003) 1463-1470; T.M. Martin, D.M. Young, Prediction of the acute toxicity (96-h LC50) of organic compounds to the fat head minnow (Pimephales promelas) using a group contribution method, Chemical Research in Toxicology 14(10) (2001) 1378-1385; G. Colmenarejo, A. Alvarez-Pedraglio, J.L. Lavandera, Chemoinformatic models to predict binding affinities to human serum albumin, Journal of Medicinal Chemistry 44 (2001) 4370-4378; J. Zupan, P. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, 2nd edition, Wiley, 1999]), we have been specifically concerned with Genetic Programming. A first paper [F. Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development, Genetic Programming and Evolvable Machines 8(4) (2007) 17-26 \citeArchetti:2007:GPEM] has been devoted to problem (i). The present contribution aims at developing a Genetic Programming based framework on which to build specific strategies which are then shown to be a valuable tool for problem (ii). In this paper, we use target estrogen receptor molecules and genistein based drug compounds. Being able to precisely and efficiently predict their mutual interaction energy is a very important task: for example, it may have an immediate relationship with the efficacy of genistein based drugs in menopause therapy and also as a natural prevention of some tumours. We compare the experimental results obtained by Genetic Programming with the ones of a set of non-evolutionary Machine Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression. Experimental results confirm that Genetic Programming is a promising technique from the viewpoint of the accuracy of the proposed solutions, of the generalization ability and of the correlation between predicted data and correct ones.
%K genetic algorithms, genetic programming, Machine learning, Regression, Docking energy, Computational biology, Drug design, QSAR
%9 journal article
%R doi:10.1016/j.asoc.2009.06.013
%U http://dx.doi.org/doi:10.1016/j.asoc.2009.06.013
%P 170-182

%0 Journal Article
%T Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset
%A Archetti, Francesco
%A Giordani, Ilaria
%A Vanneschi, Leonardo
%J Computers & Operations Research
%D 2010
%V 37
%N 8
%@ 0305-0548
%F Archetti20101395
%O Operations Research and Data Mining in Biological Systems
%X Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results, and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the ’best’ solutions found by genetic programming are presented.
%K genetic algorithms, genetic programming, Machine learning, Regression, Microarray data, Anticancer therapy, NCI-60
%9 journal article
%R doi:10.1016/j.cor.2009.02.015
%U http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0
%U http://dx.doi.org/doi:10.1016/j.cor.2009.02.015
%P 1395-1405

%0 Conference Proceedings
%T Coevolving Programs and Unit Tests from their Specification
%A Arcuri, Andrea
%A Yao, Xin
%S IEEE International Conference on Automated Software Engineering (ASE)
%D 2007
%8 nov 5 9
%C Atlanta, Georgia, USA
%F Arcuri:2007:ASE
%X Writing a formal specification before implementing a program helps to find problems with the system requirements. The requirements might be for example incomplete and ambiguous. Fixing these types of errors is very difficult and expensive during the implementation phase of the software development cycle. Although writing a formal specification is usually easier than implementing the actual code, writing a specification requires time, and often it is preferred, instead, to use this time on the implementation. In this paper we introduce for the first time a framework that might evolve any possible generic program from its specification. We use the Genetic Programming to evolve the programs, and at the same time we exploit the specifications to coevolve sets of unit tests. Programs are rewarded on how many tests they do not fail, whereas the unit tests are rewarded on how many programs they make fail. We present and analyse four different problems on which this novel technique is successfully applied.
%K genetic algorithms, genetic programming, Automatic Programming, Coevolution, Software Testing, Formal Specification, Sorting, SBSE
%R doi:10.1145/1321631.1321693
%U http://dx.doi.org/doi:10.1145/1321631.1321693

%0 Conference Proceedings
%T On the automation of fixing software bugs
%A Arcuri, Andrea
%S ICSE Companion ’08: Companion of the 30th international conference on Software engineering
%D 2008
%I ACM
%C Leipzig, Germany
%F Arcuri:2008:ICSEphd
%O Doctoral symposium session
%X Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. Techniques to help the software developers for locating bugs exist though, and they take name of Automated Debugging. However, to our best knowledge, there has been only little attempt in the past to completely automate the actual changing of the software for fixing the bugs. Therefore, in this paper we propose an evolutionary approach to automate the task of fixing bugs. The basic idea is to evolve the programs (e.g., by using Genetic Programming) with a fitness function that is based on how many unit tests they are able to pass. If a formal specification of the buggy software is given, more sophisticated fitness functions can be designed. Moreover, by using the formal specification as an oracle, we can generate as many unit tests as we want. Hence, a co-evolution between programs and unit tests might take place to give even better results. It is important to know that, to fix the bugs in a program with this novel approach, a user needs only to provide either a formal specification or a set of unit tests. No other information is required.
%K genetic algorithms, genetic programming, co-evolution, SuA, SBSE
%R doi:10.1145/1370175.1370223
%U http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf
%U http://dx.doi.org/doi:10.1145/1370175.1370223
%P 1003-1006

%0 Conference Proceedings
%T A Novel Co-Evolutionary Approach to Automatic Software Bug Fixing
%A Arcuri, Andrea
%A Yao, Xin
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Arcuri:2008:cec
%X Many tasks in Software Engineering are very expensive, and that has led the investigation to how to automate them. In particular, Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. In this paper we propose an evolutionary approach to automate the task of fixing bugs. This novel evolutionary approach is based on Co-evolution, in which programs and test cases co-evolve, influencing each other with the aim of fixing the bugs of the programs. This competitive co-evolution is similar to what happens in nature for predators and prey. The user needs only to provide a buggy program and a formal specification of it. No other information is required. Hence, the approach may work for any implementable software. We show some preliminary experiments in which bugs in an implementation of a sorting algorithm are automatically fixed.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2008.4630793
%U EC0063.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2008.4630793
%P 162-168

%0 Conference Proceedings
%T Multi-Objective Improvement of Software using Co-evolution and Smart Seeding
%A Arcuri, Andrea
%A White, David Robert
%A Clark, John
%A Yao, Xin
%Y Li, Xiaodong
%Y Kirley, Michael
%Y Zhang, Mengjie
%Y Green, David G.
%Y Ciesielski, Victor
%Y Abbass, Hussein A.
%Y Michalewicz, Zbigniew
%Y Hendtlass, Tim
%Y Deb, Kalyanmoy
%Y Tan, Kay Chen
%Y Branke, Jürgen
%Y Shi, Yuhui
%S Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL ’08)
%S Lecture Notes in Computer Science
%D 2008
%8 dec 7 10
%V 5361
%I Springer
%C Melbourne, Australia
%F ArcuriWCY08
%X Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner.
%K genetic algorithms, genetic programming, SBSE
%R doi:10.1007/978-3-540-89694-4_7
%U http://dx.doi.org/doi:10.1007/978-3-540-89694-4_7
%P 61-70

%0 Report
%T Evolutionary Repair of Faulty Software
%A Arcuri, Andrea
%D 2009
%8 apr
%N CSR-09-02
%I University of Birmingham, School of Computer Science
%C B15 2TT, UK
%F Arcuri09
%X Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed, the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current limits can be addressed in the future. This task is extremely challenging and mainly unexplored in literature. Hence, this paper only covers an initial investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.
%K genetic algorithms, genetic programming, SBSE
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2009/CSR-09-02.pdf

%0 Conference Proceedings
%T On Search Based Software Evolution
%A Arcuri, Andrea
%Y Di Penta, Massimiliano
%Y Poulding, Simon
%S Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009
%D 2009
%8 13 15 may
%I IEEE
%C Windsor, UK
%F Arcuri:2009:SSBSE
%X Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems.This framework can be used to tackle software engineering tasks such as automatic refinement, fault correction,improving non-functional criteria and reverse engineering.While the programs evolve to accomplish one of these tasks, test cases are co-evolved at the the same time to find new faults in the evolving programs.
%K genetic algorithms, genetic programming, SBSE, program coevolution, program test case, search algorithm, software engineering problem, software evolution, program testing, search problems, software engineering
%R doi:10.1109/SSBSE.2009.12
%U http://dx.doi.org/doi:10.1109/SSBSE.2009.12
%P 39-42

%0 Thesis
%T Automatic software generation and improvement through search based techniques
%A Arcuri, Andrea
%D 2009
%8 aug
%C UK
%C School of Computer Science, University of Birmingham
%F Arcuri:thesis
%X Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis we also give the important contribution of defining a first theoretical foundation.
%K genetic algorithms, genetic programming, SBSE
%9 Ph.D. thesis
%U http://etheses.bham.ac.uk/400/1/Arcuri09PhD.pdf

%0 Journal Article
%T Co-evolutionary automatic programming for software development
%A Arcuri, Andrea
%A Yao, Xin
%J Information Sciences
%D 2014
%V 259
%@ 0020-0255
%F Arcuri2010
%X Since the 1970s the goal of generating programs in an automatic way (i.e., Automatic Programming) has been sought. A user would just define what he expects from the program (i.e., the requirements), and it should be automatically generated by the computer without the help of any programmer. Unfortunately, this task is much harder than expected. Although transformation methods are usually employed to address this problem, they cannot be employed if the gap between the specification and the actual implementation is too wide. In this paper we introduce a novel conceptual framework for evolving programs from their specification. We use genetic programming to evolve the programs, and at the same time we exploit the specification to co-evolve sets of unit tests. Programs are rewarded by how many tests they do not fail, whereas the unit tests are rewarded by how many programs they make to fail. We present and analyse seven different problems on which this novel technique is successfully applied.
%K genetic algorithms, genetic programming, SBSE, STGP, Automatic programming, Automatic refinement, Co-evolution, Software testing
%9 journal article
%R doi:10.1016/j.ins.2009.12.019
%U http://www.sciencedirect.com/science/article/B6V0C-4Y34WFM-2/2/6700572128cf209a061759f28c5b7020
%U http://dx.doi.org/doi:10.1016/j.ins.2009.12.019
%P 412-432

%0 Journal Article
%T Evolutionary repair of faulty software
%A Arcuri, Andrea
%J Applied Soft Computing
%D 2011
%V 11
%N 4
%@ 1568-4946
%F Arcuri20113494
%X Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed, the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current limitations can be addressed in the future. This task is extremely challenging and mainly unexplored in the literature. Hence, this paper only covers an initial investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.
%K genetic algorithms, genetic programming, Repair, Fault localisation, Automated debugging, Search Based Software Engineering, Coevolution
%9 journal article
%R doi:10.1016/j.asoc.2011.01.023
%U http://crest.cs.ucl.ac.uk/fileadmin/crest/sebasepaper/Arcurid09d.pdf
%U http://dx.doi.org/doi:10.1016/j.asoc.2011.01.023
%P 3494-3514

%0 Conference Proceedings
%T Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems
%A Ardeh, Mazhar Ansari
%A Mei, Yi
%A Zhang, Mengjie
%Y Jin, Yaochu
%S 2020 IEEE Congress on Evolutionary Computation, CEC 2020
%D 2020
%8 19 24 jul
%I IEEE Press
%C internet
%F Ardeh:2020:CEC
%X The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem with extensive real-world applications. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, whenever a UCARP scenario changes, e.g. when a new vehicle is bought, the previously trained routing policy may no longer work effectively, and one has to retrain a new policy. Retraining a new policy from scratch can be time-consuming but the transfer of knowledge gained from solving the previous similar scenarios may help improve the efficiency of the retraining process. In this paper, we propose a novel transfer learning method by learning the probability distribution of good solutions from source domains and modeling it as a probabilistic prototype tree. We demonstrate that this approach is capable of capturing more information about the source domain compared to transfer learning based on (sub-)tree transfers and even create good trees that are not seen in source domains. Our experimental results showed that our method made the retraining process more efficient and one can obtain an initial state for solving difficult problems that is significantly better than existing methods. The final performance of all algorithms, were comparable, implying that there was no negative transfer.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC48606.2020.9185714
%U http://dx.doi.org/doi:10.1109/CEC48606.2020.9185714
%P paperid24067

%0 Book Section
%T TOPE and Magic Squares: A Simple GA Approach to Combinatorial Optimization
%A Ardell, David H.
%E Koza, John R.
%B Genetic Algorithms at Stanford 1994
%D 1994
%8 dec
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%@ 0-18-187263-3
%F ardell:1994:TOPE
%K genetic algorithms
%P 1-6

%0 Journal Article
%T Genetic Programming and Standardization in Water Temperature Modelling
%A Arganis, Maritza
%A Val, Rafael
%A Prats, Jordi
%A Rodriguez, Katya
%A Dominguez, Ramon
%A Dolz, Josep
%J Advances in Civil Engineering
%D 2009
%V 2009
%I Hindawi Publishing Corporation
%@ 16878086
%G eng
%F Arganis:2009:AiCE
%X An application of Genetic Programming (an evolutionary computational tool) without and with standardization data is presented with the aim of modeling the behavior of the water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their applicability to forecasting purposes.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1155/2009/353960
%U http://downloads.hindawi.com/journals/ace/2009/353960.pdf
%U http://dx.doi.org/doi:10.1155/2009/353960

%0 Book Section
%T Comparison Between Equations Obtained by Means of Multiple Linear Regression and Genetic Programming to Approach Measured Climatic Data in a River
%A Arganis, M. L.
%A Val, R.
%A Dominguez, R.
%A Rodriguez, K.
%A Dolz, Josep
%A Eaton, J. M.
%E Ventura, Sebastian
%B Genetic Programming - New Approaches and Successful Applications
%D 2012
%I InTech
%F Arganis:2012:GPnew
%K genetic algorithms, genetic programming
%R doi:10.5772/50556
%U http://dx.doi.org/doi:10.5772/50556
%P 239-254

%0 Journal Article
%T A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage
%A Argyri, Anthoula A.
%A Jarvis, Roger M.
%A Wedge, David
%A Xu, Yun
%A Panagou, Efstathios Z.
%A Goodacre, Royston
%A Nychas, George-John E.
%J Food Control
%D 2013
%V 29
%N 2
%@ 0956-7135
%F Argyri2012
%O Predictive Modelling of Food Quality and Safety
%X In this study, time series spectroscopic, microbiological and sensory analysis data were obtained from minced beef samples stored under different packaging conditions (aerobic and modified atmosphere packaging) at 5 C. These data were analysed using machine learning and evolutionary computing methods, including partial least square regression (PLS-R), genetic programming (GP), genetic algorithm (GA), artificial neural networks (ANNs) and support vector machines regression (SVR) including different kernel functions [i.e. linear (SVRL), polynomial (SVRP), radial basis (RBF) (SVRR) and sigmoid functions (SVRS)]. Models predictive of the microbiological load and sensory assessment were calculated using these methods and the relative performance compared. In general, it was observed that for both FT-IR and Raman calibration models, better predictions were obtained for TVC, LAB and Enterobacteriaceae, whilst the FT-IR models performed in general slightly better in predicting the microbial counts compared to the Raman models. Additionally, regarding the predictions of the microbial counts the multivariate methods (SVM, PLS) that had similar performances gave better predictions compared to the evolutionary ones (GA-GP, GA-ANN, GP). On the other hand, the GA-GP model performed better from the others in predicting the sensory scores using the FT-IR data, whilst the GA-ANN model performed better in predicting the sensory scores using the Raman data. The results of this study demonstrate for the first time that Raman spectroscopy as well as FT-IR spectroscopy can be used reliably and accurately for the rapid assessment of meat spoilage.
%K genetic algorithms, genetic programming, Meat spoilage, Raman spectroscopy, FT-IR, Multivariate analysis, Evolutionary computing
%9 journal article
%R doi:10.1016/j.foodcont.2012.05.040
%U http://www.sciencedirect.com/science/article/pii/S0956713512002745
%U http://dx.doi.org/doi:10.1016/j.foodcont.2012.05.040
%P 461-470

%0 Conference Proceedings
%T Solving Social Media Text Classification Problems Using Code Fragment-Based XCSR
%A Arif, Muhammad Hassan
%A Li, Jianxin
%A Iqbal, Muhammad
%S 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)
%D 2017
%8 nov
%F Arif:2017:ICTAI
%X Sentiment analysis and spam detection of social media text messages are two challenging data analysis tasks due to sparse and high-dimensional feature vectors. Learning classifier systems (LCS) are rule-based evolutionary computing systems and have limited capabilities to handle real valued sparse high-dimensional big data sets. LCS techniques use interval based representations to handle real valued feature vectors. In the work presented here, interval based representation is replaced by genetic programming based tree like structures to classify high-dimensional real valued text feature vectors. Multiple experiments are conducted on different social media text data sets, i.e. tweets, film reviews, Amazon and yelp reviews, SMS and Email spam message to evaluate the proposed scheme. Real valued feature vectors are generated from these data sets using term frequency inverse document frequency and/or sentiment lexicons-based features. Results depicts the supremacy of the new encoding scheme over interval based representations in both small and large social media text data sets.
%K genetic algorithms, genetic programming
%R doi:10.1109/ICTAI.2017.00080
%U http://dx.doi.org/doi:10.1109/ICTAI.2017.00080
%P 485-492

%0 Conference Proceedings
%T A Heuristic Approach for Hamiltonian Path Problem with Molecules
%A Arita, Masanori
%A Suyama, Akira
%A Hagiya, Masami
%Y Koza, John R.
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max
%Y Iba, Hitoshi
%Y Riolo, Rick L.
%S Genetic Programming 1997: Proceedings of the Second Annual Conference
%D 1997
%8 13 16 jul
%I Morgan Kaufmann
%C Stanford University, CA, USA
%F Arita:1997:hamilton
%K DNA Computing
%P 457-462

%0 Conference Proceedings
%T Automatically Discovering Euler’s Identity via Genetic Programming
%A Arkoudas, Konstantine
%Y Bringsjord, Selmer
%Y Shilliday, Andrew
%S AAAI Fall Symposium
%D 2008
%8 nov 7 9
%I AAAI
%C Arlington, Virginia, USA
%F Arkoudas:2008:AAAIf
%X We show that by using machine learning techniques (genetic programming, in particular), Euler’s famous identity (V - E + F = 2) can be automatically discovered from a limited amount of data indicating the values of V , E, and F for a small number of polyhedra the five platonic solids. This result suggests that mechanized inductive techniques have an important role to play in the process of doing creative mathematics, and that large amounts of data are not necessary for the extraction of important regularities. Genetic programming was implemented from scratch in SML-NJ.
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Papers/Symposia/Fall/2008/FS-08-03/FS08-03-001.pdf
%P 1-7

%0 Journal Article
%T System Identification Strategies Applied to Aircraft Gas Turbine Engines
%A Arkov, V.
%A Evans, C.
%A Fleming, P. J.
%A Hill, D. C.
%A Norton, J. P.
%A Pratt, I.
%A Rees, D.
%A Rodriguez-Vazquez, K.
%J Annual Reviews in Control
%D 2000
%V 24
%N 1
%@ 1367-5788
%F Arkov:2000:ARC
%X A variety of system identification techniques are applied to the derivation of models of aircraft gas turbine dynamics. The motivation behind the study is to improve the efficiency and cost-effectiveness of system identification techniques currently used in the industry. Four system identification approaches are outlined in this paper. They are based upon: identification using ambient noise only data, multisine testing and frequency-domain identification, time-varying models estimated using extended least squares with optimal smoothing, and multiobjective genetic programming to select model structure.
%K genetic algorithms, genetic programming, gas turbines, system identification, frequency domain, multisine signals least-squares estimation, time-varying systems, structure selection
%9 journal article
%R doi:10.1016/S1367-5788(00)90015-4
%U http://dx.doi.org/doi:10.1016/S1367-5788(00)90015-4
%P 67-81

%0 Journal Article
%T Performance prediction of tunnel boring machine through developing a gene expression programming equation
%A Armaghani, Danial Jahed
%A Faradonbeh, Roohollah Shirani
%A Momeni, Ehsan
%A Fahimifar, Ahmad
%A Tahir, Mahmood M. D.
%J Engineering with Computers
%D 2018
%8 jan
%V 34
%N 1
%@ 0177-0667
%F journals/ewc/ArmaghaniFMFT18
%X The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang-Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R 2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.
%K genetic algorithms, genetic programming, gene expression programming
%9 journal article
%R doi:10.1007/s00366-017-0526-x
%U http://dx.doi.org/doi:10.1007/s00366-017-0526-x
%P 129-141

%0 Journal Article
%T Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming
%A Armaghani, Danial Jahed
%A Faradonbeh, Roohollah Shirani
%A Rezaei, Hossein
%A Rashid, Ahmad Safuan A.
%A Amnieh, Hassan Bakhshandeh
%J Neural Computing and Applications
%D 2018
%V 29
%N 11
%F armaghani:2018:NCaA
%K genetic algorithms, genetic programming, gene expression programming
%9 journal article
%R doi:10.1007/s00521-016-2618-8
%U http://link.springer.com/article/10.1007/s00521-016-2618-8
%U http://dx.doi.org/doi:10.1007/s00521-016-2618-8

%0 Conference Proceedings
%T Enhancements to a hybrid genetic programming technique applied to symbolic regression
%A Armani, Umberto
%A Toropov, Vassili V.
%A Polynkin, Andrey
%A Querin, Osvaldo M.
%A Alvarez, Luis
%Y Duddeck, Fabian
%Y Querin, Osvaldo M.
%Y Sienz, Johann
%Y Toropov, Vassili V.
%Y Shaheed, M. Hasan
%S Proceedings of the 8th ASMO UK / ISSMO conference on Engineering Design Optimization Product and Process Improvement
%D 2010
%8 jul 8 9
%C Queen Mary University of London, UK
%F Armani_2010
%X A major problem in genetic programming techniques is premature convergence, which emerges during evolution as a progressive loss of variability among individuals in the population. Moreover, the mechanisms according to which individuals are created, recombined and evaluated have of course strong influence on the chances of success. Increasing variability of the population and expressivity of the genotype are then major issues for genetic programming techniques. The aim of this paper is to investigate if a hybrid, tree-based GP implementation written for symbolic regression purposes can be improved in terms of reliability and precision of the results both by several modifications of the standard GP components and by pre-processing the input data set. In order to increase variability, the effect of a simple archive updating strategy and of a periodical killing of a large part of the population (with the insertion of new and composed individuals) is assessed. As a promising measure to preserve variation among individuals, a MinMax approach in the definition of the fitness function is also proposed and tested as an alternative to the plain aggregating approach. With regard to expressivity, a simple solution consisting in the definition of a unary function that introduces a translation in the argument of the function itself is put forward. Other experiments are performed to assess if the redefinition of the fitness function using a normalised error can have beneficial effects on the evolution, as an alternative to the common root mean square error. Finally, the splitting of the input data set in two different subsets, respectively for parameter tuning and fitness evaluation, is investigated.
%K genetic algorithms, genetic programming
%U http://www.asmo-uk.com/8th-asmo-uk/html/menu_page.html

%0 Conference Proceedings
%T Generation of models related to aluminium surface treatment using genetic programming
%A Armani, Umberto
%A Boon, Dirk Jan
%A Toropov, Vassili V.
%A Polynkin, Andrey
%A Clark, Leslie J.
%A Stowe, Mary B.
%S Proceedings of the 9th world congress on structural and multidisciplinary optimization (WCSMO9),
%D 2011
%8 jun 13 17
%C Shizuoka, Japan
%F Armani_2011_1
%X Surface treatment in aerospace industry is of paramount importance for protection of metallic structures against corrosion, in particular aluminium alloys. One of the common techniques consists of the generation of a surface coating (through chemical conversion or anodising) followed by the application of a primer paint containing a water soluble chromate salt, such as barium chromate BaCrO4 or strontium chromate SrCrO4. Such treatment allows for corrosion protection of the aluminium alloy in the presence of moisture even in the case of damage to the protective coating, through chemical and mass transfer processes involving the primer, water, the exposed alloy and the chromate salts. The availability of empirical models describing the quantity of chromate dissolving into the aqueous medium is therefore important for understanding the corrosion protection process and it could lead to improvements in the development and qualification of new corrosion protection systems. The main aim of this paper is to provide improved models to describe the quantity of dissolved chromate in water for three different chromate-based primers, considering as independent variables the time treated aluminium alloy samples are left in an aqueous solution and the acidity of the solution. To produce the models a hybrid genetic programming technique is used. Its role is to generate models through symbolic regression on experimental data provided by industry. Being a non-parametric regression technique, genetic programming is successful in finding a range of models whose mathematical structure is different from existing ones
%K genetic algorithms, genetic programming, hybrid genetic programming, corrosion, model
%U http://pbl.eng.kagawa-u.ac.jp/kani/p/paper246_1.pdf

%0 Conference Proceedings
%T Control of Physical Consistency in Metamodel Building by Genetic Programming
%A Armani, U.
%A Khatir, Z.
%A Khan, Amirul
%A Toropov, V. V.
%A Polynkin, A.
%A Thompson, H.
%A Kapur, N.
%A Noakes, C. J.
%Y Tsompanakis, Y.
%Y Topping, B. H. V.
%S Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering (CSC2011)
%D 2011
%I Civil-Comp Press
%C Chania, Greece
%F Armani_2011_2
%X Soft computing has grown in importance in recent years, allowing engineers to handle more and more complex problems. Computer power has made different classes of computationally intensive techniques viable and successful alternatives to other established methods. Algorithms based on machine learning, data mining and genetically inspired methods are in some cases the only choice when the knowledge of the problem is scarce. Genetic programming (GP) [1] can be considered one of the latest techniques to have appeared in the range of soft computing tools. It is a genetically-inspired method able to generate from a data set global metamodels describing the relationship between a system’s input and output data. Typically, genetic operators are used to recombine parts of mathematical expressions in a randomised but directed way until a high quality metamodel (i.e. a model of a model) is found. The major strength of genetic programming lies in its ability to provide explicit metamodels, making possible the use of traditional analytical methods for the subsequent analysis and optimisation. A problem arises that the stochastic nature of GP reduces the possibility of controlling the consistency of the generated metamodels. It is not uncommon in a conventional GP experiment to obtain expressions that despite showing low errors cannot be used in an application as their response is not consistent with the assumptions imposed by the problem’s nature. In this paper it is described how control of the physical consistency of the generated metamodels can be improved using some basic knowledge regarding the problem at hand by imposing constraints in the problem formulation. The benefits of the new strategy are shown through a benchmark problem. Two case studies where genetic programming has been successfully applied to optimise the ventilation design of an industrial bread baking oven and of a hospital ward are also presented. In both cases data provided by computational fluid dynamics (CFD) simulations were used to generate a metamodel and genetic algorithm techniques were used to find the optimum of the modelled response. Validation of the optimal point performed using data generated by additional CFD simulations confirmed the high quality of the metamodels. In a case study the optimum found by genetic programming matches the optimum found by another metamodelling technique.
%K genetic algorithms, genetic programming, high-fidelity design optimisation, metamodel, mathematical structure, non-linear system, analytical expression, engineering applications
%R doi:10.4203/ccp.97.43
%U http://www.ctresources.info/ccp/paper.html?id=6631
%U http://dx.doi.org/doi:10.4203/ccp.97.43
%P Paper43

%0 Conference Proceedings
%T Derivation of Deterministic Design Data from Stochastic Analysis in the Aircraft Design Process
%A Armani, U.
%A Coggon, S.
%A Toropov, V. V.
%Y Topping, B. H. V.
%S Proceedings of the Eleventh International Conference on Computational Structures Technology (CST2012)
%D 2012
%8 April 7 sep
%I Civil-Comp Press
%C Dubrovnik, Croatia
%F Armani_2012
%X The application of uncertainty management techniques to the aircraft design process is currently a high profile research area and of key strategic interest within aerospace industry. Within the aircraft design process there is always a difficult balance between non specific and specific design steps for configuration and design maturity versus the overall project lead time. This leads to either an immature design that causes delays of the entry into service or significant re-design loops within the aircraft development project again resulting in a significant cost penalty. The ability to quantify uncertainties in the design enables the application of more robust optimisation approaches to balance the quantitative risks of design evolution against the aircraft performance implications (e.g. aircraft weight) and specific design lead time. Although the application of stochastic analysis is a powerful way of making informed design decisions, its integration into the standard design process requires the generation of deterministic design data which achieve the design targets from an uncertainty approach. In this paper the problem of retrieving deterministic design data from a collection of responses provided by aircraft structural computer models is addressed. Firstly, a framework that enables metamodel generation and dimensionality reduction is presented. The framework relies on polynomial chaos expansion (PCE) for metamodel generation [1]. The technique was chosen for its ability to ease the sensitivity analysis process, as sensitivity information in the form of Sobol indices can be extracted analytically from the PCE metamodels. Secondly, a search algorithm that can be used to explore the metamodels generated by PCE is presented. The algorithm, based on the particle swarm optimisation (PSO) paradigm [2], was developed specifically to be used in constrained search problems: it performs a search of the design configurations that produces a specified target response level. Constraints can also be defined using additional metamodels. The framework and the search algorithm have been validated on an aircraft structural analysis problem. The accuracy of the results and the reduced computational cost of the entire process make the presented methodology a valuable tool for uncertainty and sensitivity analysis in the aerospace industry.
%K genetic algorithms, genetic programming, industrial optimisation, metamodel, polynomial chaos expansion, sensitivity analysis, particle swarm optimisation, dimensionality reduction
%R doi:10.4203/ccp.99.216
%U http://webapp.tudelft.nl/proceedings/cst2012/html/summary/armani.htm
%U http://dx.doi.org/doi:10.4203/ccp.99.216
%P Paper216

%0 Thesis
%T Development of a hybrid genetic programming technique for computationally expensive optimisation problems
%A Armani, Umberto
%D 2014
%8 feb
%C UK
%C School of Civil Engineering, University of Leeds
%F Armani_PhD_thesis
%X The increasing computational power of modern computers has contributed to the advance of nature-inspired algorithms in the fields of optimisation and metamodelling. Genetic programming (GP) is a genetically-inspired technique that can be used for meta modelling purposes. GP main strength is in the ability to infer the mathematical structure of the best model fitting a given data set, relying exclusively on input data and on a set of mathematical functions given by the user. Model inference is based on an iterative or evolutionary process, which returns the model as a symbolic expression (text expression). As a result, model evaluation is inexpensive and the generated expressions can be easily deployed to other users. Despite genetic programming has been used in many different branches of engineering, its diffusion on industrial scale is still limited. The aims of this thesis are to investigate the intrinsic limitations of genetic programming, to provide a comprehensive review of how researchers have tackled genetic programming main weaknesses and to improve genetic programming ability to extract accurate models from data. In particular, research has followed three main directions. The first has been the development of regularisation techniques to improve the generalisation ability of a model of a given mathematical structure, based on the use of a specific tuning algorithm in case sinusoidal functions are among the functions the model is composed of. The second has been the analysis of the influence that prior knowledge regarding the function to approximate may have on genetic programming inference process. The study has led to the introduction of a strategy that allows to use prior knowledge to improve model accuracy. Thirdly, the mathematical structure of the models returned by genetic programming has been systematically analysed and has led to the conclusion that the linear combination is the structure that is mostly returned by genetic programming runs. A strategy has been formulated to reduce the evolutionary advantage of linear combinations and to protect more complex classes of individuals throughout the evolution. The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/ cnua/mypage.html.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://etheses.whiterose.ac.uk/7281/1/Armani_PhD_thesis_resubmission_grerrors_corrected.pdf

%0 Conference Proceedings
%T Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets
%A Arnaldo, Ignacio
%A Veeramachaneni, Kalyan
%A O’Reilly, Una-May
%Y Nicolau, Miguel
%Y Krawiec, Krzysztof
%Y Heywood, Malcolm I.
%Y Castelli, Mauro
%Y Garcia-Sanchez, Pablo
%Y Merelo, Juan J.
%Y Rivas Santos, Victor M.
%Y Sim, Kevin
%S 17th European Conference on Genetic Programming
%S LNCS
%D 2014
%8 23 25 apr
%V 8599
%I Springer
%C Granada, Spain
%F arnaldo:2014:EuroGP
%X The Flash system runs ensemble-based Genetic Programming (GP) symbolic regression on a shared memory desktop. To significantly reduce the high time cost of the extensive model predictions required by symbolic regression, its fitness evaluations are tasked to the desktop’s GPU. Successive GP ’instances’ are run on different data subsets and randomly chosen objective functions. Best models are collected after a fixed number of generations and then fused with an adaptive, output-space method. New instance launches are halted once learning is complete. We demonstrate that Flash’s ensemble strategy not only makes GP more robust, but it also provides an informed online means of halting the learning process. Flash enables GP to learn from a dataset composed of 370K exemplars and 90 features, evolving a population of 1000 individuals over 100 generations in as few as 50 seconds.
%K genetic algorithms, genetic programming, GPU
%R DOI:10.1007/978-3-662-44303-3_2
%U http://dx.doi.org/DOI:10.1007/978-3-662-44303-3_2
%P 13-24

%0 Conference Proceedings
%T Multiple regression genetic programming
%A Arnaldo, Ignacio
%A Krawiec, Krzysztof
%A O’Reilly, Una-May
%Y Igel, Christian
%Y Arnold, Dirk V.
%Y Gagne, Christian
%Y Popovici, Elena
%Y Auger, Anne
%Y Bacardit, Jaume
%Y Brockhoff, Dimo
%Y Cagnoni, Stefano
%Y Deb, Kalyanmoy
%Y Doerr, Benjamin
%Y Foster, James
%Y Glasmachers, Tobias
%Y Hart, Emma
%Y Heywood, Malcolm I.
%Y Iba, Hitoshi
%Y Jacob, Christian
%Y Jansen, Thomas
%Y Jin, Yaochu
%Y Kessentini, Marouane
%Y Knowles, Joshua D.
%Y Langdon, William B.
%Y Larranaga, Pedro
%Y Luke, Sean
%Y Luque, Gabriel
%Y McCall, John A. W.
%Y Montes de Oca, Marco A.
%Y Motsinger-Reif, Alison
%Y Ong, Yew Soon
%Y Palmer, Michael
%Y Parsopoulos, Konstantinos E.
%Y Raidl, Guenther
%Y Risi, Sebastian
%Y Ruhe, Guenther
%Y Schaul, Tom
%Y Schmickl, Thomas
%Y Sendhoff, Bernhard
%Y Stanley, Kenneth O.
%Y Stuetzle, Thomas
%Y Thierens, Dirk
%Y Togelius, Julian
%Y Witt, Carsten
%Y Zarges, Christine
%S GECCO ’14: Proceedings of the 2014 conference on Genetic and evolutionary computation
%D 2014
%8 December 16 jul
%I ACM
%C Vancouver, BC, Canada
%F Arnaldo:2014:GECCO
%X We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program’s subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fitness cases, that we assess for fitness, rather than the program’s execution output. MRGP can be used to improve the fitness of a final evolved solution. On our experimental suite, MRGP consistently generated solutions fitter than the result of competent GP or multiple regression. When integrated into GP, inline MRGP, on the basis of equivalent computational budget, outperforms competent GP while also besting post-run MRGP. Thus MRGP’s output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP.
%K genetic algorithms, genetic programming
%R doi:10.1145/2576768.2598291
%U http://doi.acm.org/10.1145/2576768.2598291
%U http://dx.doi.org/doi:10.1145/2576768.2598291
%P 879-886

%0 Conference Proceedings
%T Building Predictive Models via Feature Synthesis
%A Arnaldo, Ignacio
%A O’Reilly, Una-May
%A Veeramachaneni, Kalyan
%Y Silva, Sara
%Y Esparcia-Alcazar, Anna I.
%Y Lopez-Ibanez, Manuel
%Y Mostaghim, Sanaz
%Y Timmis, Jon
%Y Zarges, Christine
%Y Correia, Luis
%Y Soule, Terence
%Y Giacobini, Mario
%Y Urbanowicz, Ryan
%Y Akimoto, Youhei
%Y Glasmachers, Tobias
%Y Fernandez de Vega, Francisco
%Y Hoover, Amy
%Y Larranaga, Pedro
%Y Soto, Marta
%Y Cotta, Carlos
%Y Pereira, Francisco B.
%Y Handl, Julia
%Y Koutnik, Jan
%Y Gaspar-Cunha, Antonio
%Y Trautmann, Heike
%Y Mouret, Jean-Baptiste
%Y Risi, Sebastian
%Y Costa, Ernesto
%Y Schuetze, Oliver
%Y Krawiec, Krzysztof
%Y Moraglio, Alberto
%Y Miller, Julian F.
%Y Widera, Pawel
%Y Cagnoni, Stefano
%Y Merelo, J. J.
%Y Hart, Emma
%Y Trujillo, Leonardo
%Y Kessentini, Marouane
%Y Ochoa, Gabriela
%Y Chicano, Francisco
%Y Doerr, Carola
%S GECCO ’15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
%D 2015
%8 November 15 jul
%I ACM
%C Madrid, Spain
%F Arnaldo:2015:GECCO
%X We introduce Evolutionary Feature Synthesis (EFS), a regression method that generates readable, nonlinear models of small to medium size datasets in seconds. EFS is, to the best of our knowledge, the fastest regression tool based on evolutionary computation reported to date. The feature search involved in the proposed method is composed of two main steps: feature composition and feature subset selection. EFS adopts a bottom-up feature composition strategy that eliminates the need for a symbolic representation of the features and exploits the variable selection process involved in pathwise regularized linear regression to perform the feature subset selection step. The result is a regression method that is competitive against neural networks, and outperforms both linear methods and Multiple Regression Genetic Programming, up to now the best regression tool based on evolutionary computation.
%K genetic algorithms, genetic programming
%R doi:10.1145/2739480.2754693
%U http://doi.acm.org/10.1145/2739480.2754693
%U http://dx.doi.org/doi:10.1145/2739480.2754693
%P 983-990

%0 Conference Proceedings
%T GECCO Comp ’14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion
%E Arnold, Dirk
%E Zhang, Mengjie
%E Urbanowicz, Ryan
%E Iqbal, Muhammad
%E Shafi, Kamran
%E Stonedahl, Forrest
%E Rand, William
%E Tusar, Tea
%E Naujoks, Boris
%E Walker, David
%E Everson, Richard
%E Fieldsend, Jonathan
%E Wagner, Stefan
%E Affenzeller, Michael
%E Fan, Zhun
%E Jin, Yaochu
%E Lipson, Hod
%E Goodman, Erik
%E Tantar, Alexandru-Adrian
%E Tantar, Emilia
%E Bosman, Peter A. N.
%E McClymont, Kent
%E Sim, Kevin
%E Ochoa, Gabriela
%E Keedwell, Ed
%E Esparcia-Alcazar, Anna I.
%E Moore, Frank W.
%E Bacardit, Jaume
%E Arnaldo, Ignacio
%E Veeramachaneni, Kalyan
%E O’Reilly, Una-May
%E Smith, Stephen L.
%E Cagnoni, Stefano
%E Patton, Robert M.
%E Gustafson, Steven
%E Vladislavleva, Ekaterina
%E Woodward, John
%E Swan, Jerry
%E Barr, Earl
%E Krawiec, Krzysztof
%E Simons, Chris
%E Clark, John
%E Sudholt, Dirk
%E Esparcia, Anna
%E Ekart, Aniko
%E Doerr, Carola
%E Auger, Anne
%D 2014
%8 December 16 jul
%C Vancouver, BC, Canada
%F Arnold:2014:GECCOcomp
%X It is my pleasure to welcome you to Philadelphia for the 2012 Genetic and Evolutionary Computation Conference (GECCO-2012). This is the first time GECCO has been held in Philly. We very much you hope you enjoy this historic American city and all it has to offer. This will be my 14th year attending GECCO. I have contributed a number of papers and have enjoyed many thought-provoking presentations over the years. GECCO has played a very important role in my research program and in the training of many of my students and postdocs. I agreed to serve as General Chair of GECCO-2012 because it was time to give back to the community I have enjoyed being a part of since 1999. Terence Soule served as the editor-in-chief this year and did a very skillful job maintaining the high quality of the conference. GECCO-2012 accepted 172 full papers for oral presentation out of a total of 467 submitted. This is an acceptance rate of less than 37percent. I am very thankful to Terry, Anne Auger, our Proceedings Chair, and all the track chairs for their hard work managing the review, selection and scheduling process for the scientific papers. One of the highlights of every GECCO is the free tutorials and the free workshops held during the first two days of the conference. I found these to be incredibly helpful when I was still learning about the field.
%K genetic algorithms, genetic programming, Keynotes and invited talk, ant colony optimization and swarm intelligence, artificial immune systems, artificial life, robotics, and evolvable hardware, biological and biomedical applications, digital entertainment technologies and arts, estimation of distribution algorithms, evolution strategies and evolutionary programming, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, generative and developmental systems, integrative genetic and evolutionary computation, parallel evolutionary systems, real world applications, search based software engineering, self-* search, theory, Introductory tutorials, Advanced tutorials, Specialized tutorials, 17th annual international workshop on learning classifier systems, Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS), student workshop, VizGEC: Workshop on visualisation in genetic and evolutionary computation, Workshop on Evolutionary Computation Software Systems (EvoSoft), evolutionary synthesis of dynamical systems, Workshop on Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC), Workshop on Problem Understanding and Real-world Optimisation (PURO), Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef), Workshop on Evolutionary Computation for Big Data and Big Learning, Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), Workshop on Symbolic Regression and Modelling, 4th workshop on evolutionary computation for the automated design of algorithms, Workshop on Metaheuristic Design Patterns (MetaDeeP), Late breaking abstracts workshop, Women@GECCO 2014
%U http://dl.acm.org/citation.cfm?id=2598394

%0 Journal Article
%T Optimization of Decision Rules in Fuzzy Classification
%A Arora, Renuka
%A Kumar, Sudesh
%J International Journal of Computer Applications
%D 2012
%8 aug
%V 51
%N 3
%I Foundation of Computer Science (FCS)
%@ 09758887
%G eng
%F Arora:2012:IJCA
%X There are various advances in data collection that can intelligently and automatically analyse and mine knowledge from large amounts of data. World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. The Knowledge Discovery in Databases (KDD) is the process of extracting the knowledge from huge data collection. Data mining is a step of KDD in which patterns or models are extracted from data by using some automated techniques. Discovering knowledge in the form of classification rules is one of the most important tasks of data mining. Discovery of comprehensible, concise and effective rules helps us to make right decisions. Therefore, several Machine Learning techniques are applied for discovery of classification rules. Recently there have been several applications of genetic algorithms for effective rules with high predictive accuracy.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.5120/8021-0505
%U http://research.ijcaonline.org/volume51/number3/pxc3880505.pdf
%U http://dx.doi.org/doi:10.5120/8021-0505
%P 13-17

%0 Conference Proceedings
%T Sentiment Classification Using Automatically Extracted Subgraph Features
%A Arora, Shilpa
%A Mayfield, Elijah
%A Penstein-Rose, Carolyn
%A Nyberg, Eric
%S Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
%S CAAGET ’10
%D 2010
%8 jun
%I Association for Computational Linguistics
%C Los Angeles, California
%F Arora:2010:NAACL
%X In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.
%K genetic algorithms, genetic programming, GP
%U http://dl.acm.org/citation.cfm?id=1860631.1860647
%P 131-139

%0 Conference Proceedings
%T Automatic modeling based on cultural programming for osseointegration diagnosis
%A Arpaia, Pasquale
%A Clemente, Fabrizio
%A Manna, Carlo
%A Montenero, Giuseppe
%S IEEE Instrumentation and Measurement Technology Conference, I2MTC ’09
%D 2009
%8 May 7 may
%C Singapore
%F Arpaia:2009:I2MTC
%X The problem of modelling equivalent circuits for interpreting Electrical Impedance Spectroscopy (EIS) data in monitoring osseointegration level of metallic implants in bone is faced by means of an evolutionary programming approach based on cultural algorithms. With respect to state-of-the-art gene expression programming, the information on search advance acquired by most promising individuals during the evolution is shared with the entire population of potential solutions and stored also for next generations. Experimental results of the application such cultural programming-based analytical modelling to in-vitro EIS measurements of bone in-growth around metallic implants during prosthesis osseointegration are presented.
%K genetic algorithms, genetic programming, gene expression programming, EIS data, artificial intelligence, automatic modeling, bone implant, cultural programming, electrical impedance spectroscopy, evolutionary programming approach, metallic implant, osseointegration diagnosis, prosthesis, artificial intelligence, biomedical measurement, bone, electric impedance measurement, equivalent circuits, evolutionary computation, genetics, medical computing, orthopaedics, prosthetics
%R doi:10.1109/IMTC.2009.5168651
%U http://dx.doi.org/doi:10.1109/IMTC.2009.5168651
%P 1274-1277

%0 Journal Article
%T Enhancing regression models for complex systems using evolutionary techniques for feature engineering
%A Arroba, Patricia
%A Risco-Martin, Jose Luis
%A Zapater, Marina
%A Moya, Jose Manuel
%A Ayala, Jose Luis
%J Journal of Grid Computing
%D 2015
%8 sep  27
%V 13
%N 3
%I Springer
%@ 1572-9184
%G en
%F Arroba:2015:grid
%X This work proposes an automatic methodology for modelling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer’s expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimisation policies, but accurate and fast power modelling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimises error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98percent. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s10723-014-9313-8
%U http://eprints.ucm.es/30960/
%U http://dx.doi.org/doi:10.1007/s10723-014-9313-8
%P 409-423

%0 Journal Article
%T Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique
%A Arsalan, Muhammad
%A Qureshi, Aqsa Saeed
%A Khan, Asifullah
%A Rajarajan, Muttukrishnan
%J Applied Soft Computing
%D 2017
%8 feb
%V 51
%@ 1568-4946
%F Arsalan:2017:ASC
%X This work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique IRW-Med, the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.
%K genetic algorithms, genetic programming, Health care, Integer Wavelet Transform, Reversible watermarking, Medical images
%9 journal article
%R doi:10.1016/j.asoc.2016.11.044
%U http://www.sciencedirect.com/science/article/pii/S1568494616306135
%U http://dx.doi.org/doi:10.1016/j.asoc.2016.11.044
%P 168-179

%0 Conference Proceedings
%T Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expression Analysis and Other Statistical Methods
%A Arsene, Corneliu T. C.
%A Ardevan, Denisa
%A Bulzu, Paul
%Y Formenti, Enrico
%Y Tagliaferri, Roberto
%Y Wit, Ernst
%S CIBB
%S Lecture Notes in Computer Science
%D 2013
%V 8452
%I Springer
%F conf/cibb/ArseneAB13
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-319-09042-9
%P 161-177

%0 Conference Proceedings
%T Wind Power Prediction Using Genetic Programming Based Ensemble of Artificial Neural Networks (GPeANN)
%A Arshad, Junaid
%A Zameer, Aneela
%A Khan, Asifullah
%S 12th International Conference on Frontiers of Information Technology (FIT)
%D 2014
%8 dec
%F Arshad:2014:FIT
%X Over the past couple of years, the share of wind power in electrical power system has increased considerably. Because of the irregular characteristics of wind, the power generated by the wind turbines fluctuates continuously. The unstable nature of the wind power thus poses a serious challenge in power distribution systems. For reliable power distribution, wind power prediction system has become an essential component in power distribution systems. In this Paper, a wind power forecasting strategy composed of Artificial Neural Networks (ANN) and Genetic Programming (GP) is proposed. Five neural networks each having different structure and different learning algorithm were used as base regressors. Then the prediction of these neural networks along with the original data is used as input for GP based ensemble predictor. The proposed wind power forecasting strategy is applied to the data from five wind farms located in same region of Europe. Numerical results and comparison with existing wind power forecasting strategies demonstrates the efficiency of the proposed strategy.
%K genetic algorithms, genetic programming
%R doi:10.1109/FIT.2014.55
%U http://dx.doi.org/doi:10.1109/FIT.2014.55
%P 257-262

%0 Conference Proceedings
%T Smart bandwidth management using a recurrent Neuro-Evolutionary technique
%A Arshad, R.
%A Khan, G. M.
%A Mahmud, S. A.
%S International Joint Conference on Neural Networks (IJCNN 2014)
%D 2014
%8 jul
%F Arshad:2014:IJCNN
%X The requirement for correct bandwidth allocation and management in a multitude of different communication mediums has generated some exceedingly tedious challenges that need to be addressed both intelligently and with innovative solutions. Current advances in high speed broadband technologies have manifold increased the amount of bandwidth required during successful multimedia streaming. The progressive growth of Neuro-Evolutionary techniques have presented themselves as worthy options to address many of the challenges faced during multimedia streaming. In this paper a Neuro-Evolutionary technique called the Recurrent Cartesian Genetic Programming Evolved Artificial Neural Network(RCGPANN) is presented for prediction of future frame sizes. The proposed technique takes into account the traffic size trend of the historically transmitted data for future frame size prediction. The predicted frame size forms the basis for estimation of the amount of bandwidth necessary for transmission of future frame. Different linear regression and probabilistic approaches are employed to estimate the allocated bandwidth, while using the predicted frame size. Our proposed intelligent traffic size prediction along with bandwidth estimation and management results in a 98percent increased efficiency.
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%R doi:10.1109/IJCNN.2014.6889727
%U http://dx.doi.org/doi:10.1109/IJCNN.2014.6889727
%P 2240-2247

%0 Conference Proceedings
%T Feature Selected Cancer Data Classification with Genetic Programming
%A Arslan, Sibel
%A Ozturk, Celal
%S 2017 21st National Biomedical Engineering Meeting (BIYOMUT)
%D 2017
%8 nov
%F Arslan:2017:BIYOMUT
%X Classification is used to distribute data to classes defined on the dataset. Classification algorithms determine the classes in which the data in the test set is to be included by learning the distribution of classes in the training set. It is directly dependent on the choice of which properties to use in the classification. The most prominent features of cancer data in this work are selection and classification using genetic programming method. It has been seen that very successful classification results are obtained with Genetic Programming.
%K genetic algorithms, genetic programming
%R doi:10.1109/BIYOMUT.2017.8478885
%U http://dx.doi.org/doi:10.1109/BIYOMUT.2017.8478885
%P i-iv

%0 Journal Article
%T Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection
%A Arslan, Sibel
%A Ozturk, Celal
%J Applied Soft Computing
%D 2019
%V 78
%@ 1568-4946
%F ARSLAN:2019:ASC
%X Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods
%K genetic algorithms, genetic programming, Feature selection, Artificial bee colony programming, Multi hive artificial bee colony programming, High dimension data
%9 journal article
%R doi:10.1016/j.asoc.2019.03.014
%U http://www.sciencedirect.com/science/article/pii/S1568494619301322
%U http://dx.doi.org/doi:10.1016/j.asoc.2019.03.014
%P 515-527

%0 Journal Article
%T Book Review: Evolvable Components–From Theory to Hardware Implementations
%A Arslan, Tughrul
%J Genetic Programming and Evolvable Machines
%D 2005
%8 dec
%V 6
%N 4
%@ 1389-2576
%F arslan:2005:GPEM
%X Book Review: Evolvable Components–From Theory to Hardware Implementations by Lukas Sekanina Springer, 2003, ISBN 3-540-40377-9
%K genetic algorithms, evolvable hardware
%9 journal article
%R doi:10.1007/s10710-005-3718-x
%U http://dx.doi.org/doi:10.1007/s10710-005-3718-x
%P 461-462

%0 Conference Proceedings
%T Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the PR interval of ECG
%A Arvaneh, M.
%A Ahmadi, H.
%A Azemi, A.
%A Shajiee, M.
%A Dastgheib, Z. S.
%S International Conference on Biomedical and Pharmaceutical Engineering, ICBPE ’09
%D 2009
%8 February 4 dec
%F Arvaneh:2009:ICBPE
%X In this work, we propose a new method for prediction of Paroxysmal Atrial Fibrillation (PAF) by only using the PR interval of ECG signal. We first obtain a nonlinear structure and parameters of PR interval by a Genetic Programming (GP) based algorithm. Next, we use the neural networks for prediction of PAF. The inputs of the neural networks are the parameters of nonlinear model of the PR intervals. For the modeling and prediction we have limited ourselves to only 30 seconds of an ECG signal, which is one of the advantages of our proposed approach. For comparison purposes, we have modeled 30 seconds of ECG signals by time based modeling method and have compared prediction results of them.
%K genetic algorithms, genetic programming, ECG signal, PR interval, Paroxysmal Atrial Fibrillation, electrocardiography, neural networks, electrocardiography, neural nets
%R doi:10.1109/ICBPE.2009.5384063
%U http://dx.doi.org/doi:10.1109/ICBPE.2009.5384063
%P 1-5

%0 Journal Article
%T Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran
%A Aryafar, Ahmad
%A Khosravi, Vahid
%A Zarepourfard, Hosniyeh
%A Rooki, Reza
%J Environmental Earth Sciences
%D 2019
%V 78
%N 3
%F aryafar:2019:EES
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1007/s12665-019-8092-8
%U http://link.springer.com/article/10.1007/s12665-019-8092-8
%U http://dx.doi.org/doi:10.1007/s12665-019-8092-8

%0 Journal Article
%T Evaluating the strength of intact rocks through genetic programming
%A Asadi, Mojtaba
%A Eftekhari, Mehdi
%A Bagheripour, Mohammad Hossein
%J Applied Soft Computing
%D 2011
%8 mar
%V 11
%N 2
%@ 1568-4946
%F Asadi:2010:ASC
%X Good prediction of the strength of rocks has many theoretical and practical applications. Analysis, design and construction of underground openings and tunnels, open pit mines and rock-based foundations are some examples of applications in which prediction of the strength of rocks is of great importance. The prediction might be done using mathematical expressions called failure criteria. In most cases, failure criteria of jointed rocks contain the value of strength of intact rock, i.e. the rock without joints and cracks. Therefore, the strength of intact rock can be used directly in applications and indirectly to predict the strength of jointed rock masses. On the other part, genetic programming method is one of the most powerful methods in machine learning field and could be used for non-linear regression problems. The derivation of an appropriate equation for evaluating the strength of intact rock is the common objective of many researchers in civil and mining engineering; therefore, mathematical expressions were derived in this paper to predict the strength of the rock using a genetic programming approach. The data of 51 rock types were used and the efficiency of equations obtained was illustrated graphically through figures.
%K genetic algorithms, genetic programming, Information criterion, Intact rock, Failure criteria
%9 journal article
%R doi:10.1016/j.asoc.2010.06.009
%U http://www.sciencedirect.com/science/article/B6W86-50CVPW4-2/2/863c13a5a1c7be6da7b1ea6592b11bd3
%U http://dx.doi.org/doi:10.1016/j.asoc.2010.06.009
%P 1932-1937

%0 Journal Article
%T A novel approach for estimation of solvent activity in polymer solutions using genetic programming
%A Tashvigh, Akbar Asadi
%A Ashtiani, Farzin Zokaee
%A Karimi, Mohammad
%A Okhovat, Ahmad
%J Calphad
%D 2015
%V 51
%@ 0364-5916
%F AsadiTashvigh:2015:Calphad
%X In this paper, genetic programming (GP) as a novel approach for the explicit modelling the phase equilibria of polymer solutions is presented. The objective of this study is to develop robust model based on experimental data for prediction of solvent activity in polymer/solvent mixtures. Molecular weight, density, chemical structures of polymer and solvent, and concentration of polymer solution were considered as input parameters of the model. Activity of solvent is considered as output parameter of the model. Some statistical parameters were calculated in order to investigate the reliability of model. The results showed very well agreement with the experimental data with an average error of less than 3percent.
%K genetic algorithms, genetic programming, Solvent activity, Polymer solution, Phase equilibria
%9 journal article
%R doi:10.1016/j.calphad.2015.07.005
%U http://www.sciencedirect.com/science/article/pii/S0364591615300080
%U http://dx.doi.org/doi:10.1016/j.calphad.2015.07.005
%P 35-41

%0 Book Section
%T Nark: Evolving Bug-Finding Compiler Extensions with Genetic Algorithms
%A Ashcraft, Kenneth
%E Koza, John R.
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%D 2003
%8 April
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%F ashcraft:2003:NEBCEGA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2003/Ashcraft.pdf
%P 11-20

%0 Journal Article
%T Evolving communicating controllers for multiple mobile robot systems
%A Ashiru, I.
%A Czarnecki, C. A.
%J Microprocessors and Microsystems
%D 1998
%V 21
%N 6
%@ 0141-9331
%F Ashiru:1998:MM
%X Multiple mobile robot systems working together to achieve a task have many advantages over single robot systems. However, the planning and execution of a task which is to be undertaken by multiple robots is extremely difficult. To date no tools exist which allow such systems to be engineered. One of the key questions that arises when developing such systems is: does communication between the robots aid the completion of the task, and if so what information should be communicated? This paper presents the results of an investigation undertaken to address the above question. The approach adopted is to use genetic programming (GP) with the aim of evolving a controller, and letting the evolution process determine what information should be communicated and how best to use this information. A number of experiments were performed with the aim of determining the communication requirements. The results of these experiments are presented in this paper. It is shown that the GP system evolved controllers whose performance benefitted as a result of the communication process.
%K genetic algorithms, genetic programming, Mobile robots, Communication
%9 journal article
%R doi:10.1016/S0141-9331(98)00054-4
%U http://www.sciencedirect.com/science/article/B6V0X-3TB0788-6/2/445577f1e7cd0c0d531457835edf327e
%U http://dx.doi.org/doi:10.1016/S0141-9331(98)00054-4
%P 393-402

%0 Conference Proceedings
%T GP-Automata for Dividing the Dollar
%A Ashlock, Dan
%Y Koza, John R.
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max
%Y Iba, Hitoshi
%Y Riolo, Rick L.
%S Genetic Programming 1997: Proceedings of the Second Annual Conference
%D 1997
%8 13 16 jul
%I Morgan Kaufmann
%C Stanford University, CA, USA
%F ashlock:1997:GPdd
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1997/ashlock_1997_GPdd.pdf
%P 18-26

%0 Conference Proceedings
%T The Effect of Splitting Populations on Bidding Strategies
%A Ashlock, Dan
%A Richter, Charles
%Y Koza, John R.
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max
%Y Iba, Hitoshi
%Y Riolo, Rick L.
%S Genetic Programming 1997: Proceedings of the Second Annual Conference
%D 1997
%8 13 16 jul
%I Morgan Kaufmann
%C Stanford University, CA, USA
%F ashlock:1997:spbs
%X In this paper we explore the effects of splitting a single population of artificial agents engaging in a simple double auction game into two competing populations by modifying experiments reported in [Ashlock, 1997]. The original paper used a new genetic programming tool, termed GP-Automata, to induce bidding strategies with a genetic algorithm for Nash’s game divide the dollar. The motivation for performing the research is the biological notion of inclusive fitness and kinship theory. The a priori hypothesis of the authors was that behaviour of the agents in the simulated market would change substantially when they were no longer forced to be similar to one another by the genetic mechanism used to induce new bidding strategies. While breeding takes place only within each population, all bidding is between agents from different populations. The agents in the original (single population) paper strongly favoured ’fair’ Nash equilibria of the divide the dollar game, at odds with the economic theory for egoistic agents. When controls for kinship effects are implemented by splitting the population a substantial effect is observed. When agents doing the bidding are not close genetic kin to one another the ’unfair’ Nash equilbria regain a great deal of their former prominence. This result is of importance to any sort of evolutionary algorithm creating artificial agents, as kinship theory can confound game-theoretic predictions that assume egoistic agents. The current research also arguably increases the level of realism in the simulation of a double auction market.
%K genetic algorithms, genetic programming
%U http://dakotarichter.com/papers/AshlockRichterSplittingPopulationsGP97.pdf
%P 27-34

%0 Conference Proceedings
%T A Fully Characterized Test Suite for Genetic Programming
%A Ashlock, Dan
%A Lathrop, James I.
%Y Porto, V. William
%Y Saravanan, N.
%Y Waagen, D.
%Y Eiben, A. E.
%S Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%D 1998
%8 25 27 mar
%V 1447
%I Springer-Verlag
%C Mission Valley Marriott, San Diego, California, USA
%@ 3-540-64891-7
%F ashlock:1998:fctsGP
%K genetic algorithms, genetic programming
%R doi:10.1007/BFb0040753
%U http://dx.doi.org/doi:10.1007/BFb0040753
%P 537-546

%0 Conference Proceedings
%T ISAc Lists, A Different Representation for Program Induction
%A Ashlock, Dan
%A Joenks, Mark
%Y Koza, John R.
%Y Banzhaf, Wolfgang
%Y Chellapilla, Kumar
%Y Deb, Kalyanmoy
%Y Dorigo, Marco
%Y Fogel, David B.
%Y Garzon, Max H.
%Y Goldberg, David E.
%Y Iba, Hitoshi
%Y Riolo, Rick
%S Genetic Programming 1998: Proceedings of the Third Annual Conference
%D 1998
%8 22 25 jul
%I Morgan Kaufmann
%C University of Wisconsin, Madison, Wisconsin, USA
%@ 1-55860-548-7
%F ashlock:1998:ISAc
%X The traditional method of using a genetic algorithm to perform program induction is genetic programming which operates upon parse trees. In this papers we introduce a simpler data structure for program induction, the If-Statement-Action ISAc table. We test this data structure on the Tartarus problem of Astro Teller and compare its performance with simple string genes for Tartarus, Teller’s own work, and with GP-Automata. In addition to the main result we present a new baseline study for the Tartarus problem. These baseline results suggest state information alone, without reactive ability, can provide relatively high fitness of the Tartarus problem.
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/ashlock_1998_ISAc.pdf
%P 3-10

%0 Conference Proceedings
%T Thermal agents: An application of genetic programming to virtual engineering
%A Ashlock, Daniel A.
%A Bryden, Kenneth M.
%Y Sarker, Ruhul
%Y Reynolds, Robert
%Y Abbass, Hussein
%Y Tan, Kay Chen
%Y McKay, Bob
%Y Essam, Daryl
%Y Gedeon, Tom
%S Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%D 2003
%8 August 12 dec
%I IEEE Press
%C Canberra
%@ 0-7803-7804-0
%F ashlock:2003:taaaogptve
%X The temperature profile across an object is easy to compute by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex configuration is an impediment to exploratory analysis of engineering systems. A rapidly computed initial guess can speed convergence for an iterative thermal solver. We describe and test a system for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object geometry but general across different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on one or more sets of boundary conditions. In use, thermal agents transform boundary conditions into a rapidly converged set of initial values on a cellular decomposition of an object.
%K genetic algorithms, genetic programming, Boundary conditions, Genetic engineering, Geometry, Impedance, Iterative methods, System testing, Systems engineering and theory, Temperature, Thermal engineering, iterative methods, mechanical engineering computing, temperature distribution, thermal engineering, cellular decomposition, exploratory analysis, iterative method, iterative thermal solver, thermal agents, thermal boundary condition, virtual engineering
%R doi:10.1109/CEC.2003.1299824
%U http://dx.doi.org/doi:10.1109/CEC.2003.1299824
%P 1340-1347

%0 Conference Proceedings
%T On Taxonomy of Evolutionary Computation Problems
%A Ashlock, Daniel
%A Bryden, Kenneth
%A Corns, Steven
%S Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%8 20 23 jun
%V 2
%I IEEE Press
%C Portland, Oregon
%@ 0-7803-8515-2
%F Ashlock:2004:OToECP
%X Taxonomy is the practice of classifying members of a group based on their measurable characteristics. In evolutionary computation the problem of telling when two problems are similar is both challenging and important. An accurate classification technique would yield large benefits by permitting a researcher to rationally chose algorithm and parameter setting based on past experience. This study uses a standard taxonomic technique, hierarchical clustering, on a set of taxonomic characters derived from a comparative study using graph based evolutionary algorithms.
%K genetic algorithms, genetic programming, data visualisation, evolutionary computation, graph theory, pattern classification, pattern clustering, tree data structures, tree searching cladogram, classification technique, evolutionary computation problems, graph based evolutionary algorithms, hierarchical clustering, standard taxonomic technique, taxonomy, Theory of evolutionary algorithms, Combinatorial & numerical optimization
%R doi:10.1109/CEC.2004.1331102
%U http://dx.doi.org/doi:10.1109/CEC.2004.1331102
%P 1713-1719

%0 Conference Proceedings
%T Coevolution and Tartarus
%A Ashlock, Daniel
%A Willson, Stephen
%A Leahy, Nicole
%S Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%8 20 23 jun
%I IEEE Press
%C Portland, Oregon
%@ 0-7803-8515-2
%F Ashlock:2004:CaT
%X This study applies coevolution to the Tartarus task. If the coevolving test cases are viewed as a form of parasite the question of virulence becomes an important feature of the algorithm. This study compares two types of parasites. The impact of coevolution in this study is at odds with intuition and statistically significant. Analysis suggests that disruptive crossover has a key effect. In the presence of disruptive crossover, coevolution may need to be modified to be effective. The key method of dealing with disruptive crossover is tracking the age of the Tartarus agents. Using only older agents to drive coevolution of test cases substantially enhances the performance of one of the two type of coevolution studied.
%K genetic algorithms, genetic programming, Coevolution & collective behavior, Evolutionary intelligent agents
%R doi:10.1109/CEC.2004.1331089
%U http://orion.math.iastate.edu/danwell/eprints/TartarusCE.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2004.1331089
%P 1618-1624

%0 Conference Proceedings
%T Rapid Training of Thermal Agents with Single Parent Genetic Programming
%A Ashlock, Daniel A.
%A Bryden, Kenneth M.
%A Ashlock, Wendy
%A Gent, Stephen P.
%Y Corne, David
%Y Michalewicz, Zbigniew
%Y Dorigo, Marco
%Y Eiben, Gusz
%Y Fogel, David
%Y Fonseca, Carlos
%Y Greenwood, Garrison
%Y Chen, Tan Kay
%Y Raidl, Guenther
%Y Zalzala, Ali
%Y Lucas, Simon
%Y Paechter, Ben
%Y Willies, Jennifier
%Y Guervos, Juan J. Merelo
%Y Eberbach, Eugene
%Y McKay, Bob
%Y Channon, Alastair
%Y Tiwari, Ashutosh
%Y Volkert, L. Gwenn
%Y Ashlock, Dan
%Y Schoenauer, Marc
%S Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%D 2005
%8 February 5 sep
%V 3
%I IEEE Press
%C Edinburgh, UK
%@ 0-7803-9363-5
%F ashlock:2005:CECd
%X The temperature profile across an object can be computed by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex configuration is an impediment to exploratory analysis of engineering systems. A high-quality rapidly computed initial guess can speed convergence for an iterative algorithm. A system is described and tested for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object but general across different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on several sets of boundary conditions. In use, thermal agents transform boundary conditions into rapidly-converged initial values on a cellular decomposition of an object. the impact of using single parent genetic programming on thermal agents is tested. Single parent genetic programming replaces the usual sub-tree crossover in genetic programming with crossover with members of an unchanging ancestor set. The use of this ancestor set permits the incorporation of expert knowledge into the system as well as permitting the re-use of solutions derived on one object to speed training of thermal agents for another object. For three types of experiments, incorporating expert knowledge; re-using evolved solutions; and transferring knowledge between distinct configurations statistically significant improvements are obtained with single parent techniques.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2005.1554957
%U http://dx.doi.org/doi:10.1109/CEC.2005.1554957
%P 2122-2129

%0 Conference Proceedings
%T An Updated Taxonomy of Evolutionary Computation Problems using Graph-based Evolutionary Algorithms
%A Ashlock, Daniel A.
%A Bryden, Kenneth M.
%A Corns, Steven
%A Schonfeld, Justin
%Y Yen, Gary G.
%Y Wang, Lipo
%Y Bonissone, Piero
%Y Lucas, Simon M.
%S Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%8 June 21 jul
%I IEEE Press
%C Vancouver
%@ 0-7803-9487-9
%F Ashlock:2006:CECtax
%X Graph based evolutionary algorithms use combinatorial graphs to impose a topology or geographic structure on an evolving population. It has been demonstrated that, for a fixed problem, time to solution varies substantially with the choice of graph. This variation is not simple with very different graphs yielding faster solution times for different problems. Normalised time to solution for many graphs thus forms an objective character that can be used for classifying the type of a problem, separate from its hardness measured with average time to solution. This study uses fifteen combinatorial graphs to classify 40 evolutionary computation problems. The resulting classification is done using neighbour joining, and the results are also displayed using non-linear projection. The different methods of grouping evolutionary computation problems into similar types exhibit substantial agreement. Numerical optimisation problems form a close grouping while some other groups of problems scatter across the taxonomy. This paper updates an earlier taxonomy of 23 problems and introduces new classification techniques.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2006.1688295
%U http://dx.doi.org/doi:10.1109/CEC.2006.1688295
%P 403-410

%0 Book
%T Evolutionary Computation for Modeling and Optimization
%A Ashlock, Daniel
%D 2006
%I Springer
%F Ashlock:2006:book
%X Evolutionary Computation for Optimisation and Modelling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modelling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool. Written for: Undergraduate and graduate students
%K genetic algorithms, genetic programming
%R doi:10.1007/0-387-31909-3
%U http://dx.doi.org/doi:10.1007/0-387-31909-3

%0 Conference Proceedings
%T Evolvable Threaded Controllers for a Multi-Agent Grid Robot Task
%A Ashlock, Daniel
%A Bryden, Kenneth M.
%A Johnson, Nathan G.
%Y Dagli, Cihan H.
%Y Buczak, Anna L.
%Y Enke, David L.
%Y Embrechts, Mark
%Y Ersoy, Okan
%S ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks
%D 2006
%8 nov 5 8
%V 16
%C St. Louis, MO, USA
%F Ashlock:2006:ANNIE
%X If skip action (ISAc) lists are a linear genetic programming data structure that can be used as an evolvable grid robot controller. In this study ISAc lists are modified to run multiple control threads so that a single ISAc list can control multiple grid robots. The threaded ISAc lists are tested by evolving them to control 20–25 grid robots that all must exit a virtual room through a single door. The evolutionary algorithm used rapidly locates a variety of controllers that permit the room to be cleared efficiently.
%K genetic algorithms, genetic programming
%R doi:10.1115/1.802566.paper22
%U http://dx.doi.org/doi:10.1115/1.802566.paper22

%0 Conference Proceedings
%T Function Stacks, GBEAs, and Crossover for the Parity Problem
%A Ashlock, Daniel
%A Bryden, Kenneth M.
%Y Dagli, Cihan H.
%Y Buczak, Anna L.
%Y Enke, David L.
%Y Embrechts, Mark
%Y Ersoy, Okan
%S ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks
%D 2006
%8 nov 5 8
%V 16
%C St. Louis, MO, USA
%F Ashlock:2006:ANNIEa
%O Part I: Evolutionary Computation
%X Function stacks are a directed acyclic graph representation for genetic programming that subsumes the need for automatically defined functions, substantially reduces the number of operations required to solve a problem, and permits the use of a conservative crossover operator. Function stacks are a generalisation of Cartesian genetic programming. Graph based evolutionary algorithms are a method for improving evolutionary algorithm performance by imposing a connection topology on an evolutionary population to strike an efficient balance between exploration and exploration. In this study the parity problems using function stacks for parity on 3, 4, 5, and 6 variables are tested on fifteen graphical connection topologies with and without crossover. Choosing the correct graph is found to have a statistically significant impact on time to solution. The conservative crossover operator for function stacks, new in this study, is found to improve time to solution by 4 to 9 fold with more improvement in harder instances of the parity problem.
%K genetic algorithms, genetic programming
%R doi:10.1115/1.802566.paper18
%U http://dx.doi.org/doi:10.1115/1.802566.paper18

%0 Conference Proceedings
%T Evolution of Artificial Ring Species
%A Ashlock, Daniel
%A von Konigslow, Taika
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Ashlock:2008:cec
%X Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with self-organised and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent. Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with the Tartarus task. A comparison is made with another study that uses hybridisation to achieve record breaking Tartarus fitness.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2008.4630865
%U EC0169.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2008.4630865
%P 653-659

%0 Conference Proceedings
%T The Geometry of Tartarus Fitness Cases
%A Ashlock, Daniel
%A Warner, Elizabeth
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Ashlock3:2008:cec
%X Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320,320 fitness cases for the standard Tartarus task of which 297,040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2008.4630965
%U EC0339.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2008.4630965
%P 1309-1316

%0 Conference Proceedings
%T Small Population Effects and Hybridization
%A Ashlock, Daniel A.
%A Bryden, Kenneth M.
%A Corns, Steven
%Y Wang, Jun
%S 2008 IEEE World Congress on Computational Intelligence
%D 2008
%8 January 6 jun
%I IEEE Press
%C Hong Kong
%F Ashlock5:2008:cec
%X This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow. The first is hybridisation; the second is using small population effects. Hybridisation consists of restarting evolutionary algorithms with copies of bestof- population individuals drawn from many populations. Small population effects occur when an evolutionary algorithm’s performance, either speed or probability of premature convergence, is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridisation of many small populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used and from varying the frequency with which hybridisation is performed. The major effect results from changing the frequency of hybridization; the impact of population size is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed. Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous hybridization experiments that motivated its development.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2008.4631152
%U EC0599.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2008.4631152
%P 2637-2643

%0 Conference Proceedings
%T Induction of Virtual Sensors with Function Stacks
%A Ashlock, Daniel
%A Shuttleworth, Adam J.
%A Bryden, Kenneth M.
%Y Dagli, Cihan H.
%Y Bryden, K. Mark
%Y Corns, Steven M.
%Y Gen, Mitsuo
%Y Tumer, Kagan
%Y Suer, Gursel
%S ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks
%D 2009
%V 19
%C St. Louis, MO, USA
%F Ashlock:2009:ANNIEa
%O Part I
%X Virtual sensors are mathematical models that predict the readings of a sensor in a location currently without an operational sensor. Virtual sensors can be used to compensate for a failed sensor or as a framework for supporting mathematical decomposition of a model of a complex system. This study applies a novel genetic programming representation called a function stack to the problem of virtual sensor induction in a simple thermal system. Real-valued function stacks are introduced in this study. The thermal system modelled is a heat exchanger. Function stacks are found to be able to efficiently find compact and accurate models for each often sensors using the data from the other sensors. This study serves as proof-of-concept for using function stacks as a modeling technology for virtual sensors.
%K genetic algorithms, genetic programming
%R doi:10.1115/1.802953.paper4
%U http://dx.doi.org/doi:10.1115/1.802953.paper4

%0 Conference Proceedings
%T Logic Function Induction with the Blender Algorithm Using Function Stacks
%A Ashlock, Daniel
%A McCorkle, Douglas
%A Bryden, Kenneth M.
%Y Dagli, Cihan H.
%Y Bryden, K. Mark
%Y Corns, Steven M.
%Y Gen, Mitsuo
%Y Tumer, Kagan
%Y Suer, Gursel
%S ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks
%D 2009
%V 19
%C St. Louis, MO, USA
%F Ashlock:2009:ANNIE
%O Part III Evolutionary Computation
%X This paper applies two techniques, hybridisation and small population effects, to the problem of logic function induction. It also uses an efficient representation for genetic programming called a function stack. Function stacks are a directed acyclic graph representation used in place of the more common tree-structured representation. This study is the second exploring an algorithm for evolutionary computation called the blender algorithm which performs hybridization of many small populations. The blender algorithm is tested on the 3 and 4 variable parity problems. Confirming and sharpening earlier results on the use of small population sizes for the parity problem, it is demonstrated that subpopulation size and intervals between population mixing steps are critical parameters. The blender algorithm is found to perform well on the parity problem.
%K genetic algorithms, genetic programming
%R doi:10.1115/1.802953.paper24
%U http://dx.doi.org/doi:10.1115/1.802953.paper24
%P 189-196

%0 Conference Proceedings
%T Evolution for automatic assessment of the difficulty of Sokoban boards
%A Ashlock, Daniel
%A Schonfeld, Justin
%S IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%8 18 23 jul
%I IEEE Press
%C Barcelona, Spain
%F Ashlock:2010:cec
%X Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the Sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialised population of ISAc lists while the other initialises populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2010.5586239
%U http://dx.doi.org/doi:10.1109/CEC.2010.5586239

%0 Conference Proceedings
%T Evolving Fractal Art with a Directed Acyclic Graph Genetic Programming Representation
%A Ashlock, Daniel
%A Tsang, Jeffrey
%Y Murata, Yadahiko
%S Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)
%D 2015
%8 25 28 may
%I IEEE Press
%C Sendai, Japan
%F Ashlock:2015:CEC
%X A class of fractals called orbit capture fractals are generated by iterating a function on a point until the point’s trajectory enters a capture zone. This study uses a digraph based representation for genetic programming to evolve functions used to generate orbit capture fractals. Three variations on the genetic programming system are examined using two fitness functions. The first fitness function maximizes the entropy of the distribution of capture numbers, while the second places a geometric constraint on the distribution of capture numbers. Some combinations of representation and fitness function generate fractals often, while others yield interesting non-fractal images most of the time.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2015.7257148
%U http://eldar.mathstat.uoguelph.ca/dashlock/eprints/RFSfrac.pdf
%U http://dx.doi.org/doi:10.1109/CEC.2015.7257148
%P 2137-2144

%0 Conference Proceedings
%T Evolutionary Partitioning Regression with Function Stacks
%A Ashlock, Daniel A.
%A Brown, Joseph Alexander
%Y Ong, Yew Song
%S Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)
%D 2016
%8 25 29 jul
%I IEEE Press
%C Vancouver
%F Ashlock:2016:CEC
%X Partitioning regression is the simultaneous fitting of multiple models to a set of data and partitioning of that data into easily modelled classes. The key to partitioning regression with evolution is minimum error assignment during fitness evaluation. Assigning a point to the model for which it has the least error while using evolution to minimize total model error encourages the evolution of models that cleanly partition data. This study demonstrates the efficacy of partitioning regression with two or three models on simple bivariate data sets. Possible generalizations to the general case of clustering are outlined.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2016.7743963
%U http://dx.doi.org/doi:10.1109/CEC.2016.7743963
%P 1469-1476

%0 Conference Proceedings
%T Generalized Divide the Dollar
%A Ashlock, Daniel
%A Greenwood, Garrison
%Y Ong, Yew-Soon
%S Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)
%D 2016
%8 24 29 jul
%I IEEE Press
%C Vancouver
%F Ashlock:2016:CECa
%X Divide the dollar is a two-player simultaneous derived from a game invented by John Nash because its strategy space has an entire subspace of Nash equilibria. This study describes and explores a family of generalizations of divide the dollar with easily controlled properties. If we view divide the dollar as modelling the process of making a bargain, then the generalized game makes it easy to model the impact of external subsidies on bargaining. Classical divide the dollar is compared to four generalizations representing a simple subsidy in three different amounts and a more complex type of subsidy. The distribution of simple strategies that arise under replicator dynamics is compared to the bids that arise in populations of evolving, adaptive agents. Agents are encoded using a finite state representation that conditions its transitions on the result of bargains. These results fall into three categories, the first player obtains a higher amount, the second one does, or the agents fail to make a deal. The replicator dynamic results are compared to obtain the naive degree of distortion caused by the subsidies. The results for evolving agents are then examined to figure out the degree to which adaptation compensated for or amplifies this distortion.
%K genetic algorithms, genetic programming, FSM
%R doi:10.1109/CEC.2016.7743814
%U http://dx.doi.org/doi:10.1109/CEC.2016.7743814
%P 343-350

%0 Conference Proceedings
%T Single Parent Genetic Programming
%A Ashlock, Wendy
%A Ashlock, Dan
%Y Corne, David
%Y Michalewicz, Zbigniew
%Y Dorigo, Marco
%Y Eiben, Gusz
%Y Fogel, David
%Y Fonseca, Carlos
%Y Greenwood, Garrison
%Y Chen, Tan Kay
%Y Raidl, Guenther
%Y Zalzala, Ali
%Y Lucas, Simon
%Y Paechter, Ben
%Y Willies, Jennifier
%Y Guervos, Juan J. Merelo
%Y Eberbach, Eugene
%Y McKay, Bob
%Y Channon, Alastair
%Y Tiwari, Ashutosh
%Y Volkert, L. Gwenn
%Y Ashlock, Dan
%Y Schoenauer, Marc
%S Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%D 2005
%8 February 5 sep
%V 2
%I IEEE Press
%C Edinburgh, UK
%@ 0-7803-9363-5
%F ashlock:2005:CECw
%X The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover operator is its potential to induce defensive metaselection for large parse trees, a process usually termed ’bloat’. Single parent genetic programming is a form of genetic programming in which bloat is reduced by doing subtree crossover with a fixed population of ancestor trees. Analysis of mean tree size growth demonstrates that this fixed and limited set of crossover partners provides implicit, automatic control on tree size in the evolving population, reducing the need for additionally disruptive trimming of large trees. The choice of ancestor trees can also incorporate expert knowledge into the genetic programming system. The system is tested on four problems: plus-one-recall-store (PORS), odd parity, plus-times-half (PTH) and a bioinformatic model fitting problem (NIPs). The effectiveness of the technique varies with the problem and choice of ancestor set. At the extremes, improvements in time to solution in excess of 4700-fold were observed for the PORS problem, and no significant improvements for the PTH problem were observed.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2005.1554823
%U http://dx.doi.org/doi:10.1109/CEC.2005.1554823
%P 1172-1179

%0 Conference Proceedings
%T Using Very Small Population Sizes in Genetic Programming
%A Ashlock, Wendy
%S 2006 IEEE World Congress on Computational Intelligence, 2006 IEEE Congress on Evolutionary Computation
%D 2006
%8 16 21 jul
%C Vancouver
%F ashlock:2006:cecW
%X This paper examines the use of very small (4-7) population sizes in genetic programming. When using exploitive operators, this results in hillclimbing; when using exploratory operators this results in genetic drift. The end result is a different way of searching the space which gives insight into the fitness landscape and the nature of the variation operators used. This study compares the use of very small population sizes with the use of population sizes up to 1000 for three genetic programming problems: 4-parity using parse trees, Tartarus using ISAc lists, and several versions of plus-onerecall- store (PORS) using parse trees. For 4-parity and Tartarus with 60 ISAc nodes, algorithms with very small population sizes found more solutions faster. For PORS, the effect was less pronounced: more solutions were found, but the algorithm was faster only than when using slightly larger populations. For Tartarus with 30 ISAc nodes, no effect was detected.
%K genetic algorithms, genetic programming
%R doi:10.1109/CEC.2006.1688325
%U http://dx.doi.org/doi:10.1109/CEC.2006.1688325
%P 1023-1030

%0 Conference Proceedings
%T Mutation vs. Crossover with Genetic Programming
%A Ashlock, Wendy
%Y Dagli, Cihan H.
%Y Buczak, Anna L.
%Y Enke, David L.
%Y Embrechts, Mark
%Y Ersoy, Okan
%S ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks
%D 2006
%8 nov 5 8
%V 16
%C St. Louis, MO, USA
%F Ashlock:2006:ANNIEw
%O Part I: Evolutionary Computation
%X Understanding how variation operators work leads to a better understanding both of the search space and of the problem being solved. This study examines the behaviour of mutation and crossover operators in genetic programming using parse trees to find solutions to 3-parity and 4-parity. The standard subtree crossover and subtree mutation operators are studied along with two new operators, fold mutation and fusion crossover. They are studied in terms of how often and how fast they solve the problem; how much they change the fitness on average; and what proportion of variations are neutral, harmful, and helpful. It is found that operators behave differently when used alone than when used together with another operator and that some operators behave differently when solving 3-parity and when solving 4-parity.
%K genetic algorithms, genetic programming
%R doi:10.1115/1.802566.paper2
%U http://dx.doi.org/doi:10.1115/1.802566.paper2

%0 Conference Proceedings
%T Designing artificial organisms for use in biological simulations
%A Ashlock, Wendy
%A Ashlock, Daniel
%S IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2011)
%D 2011
%8 November 15 apr
%C Paris
%F Ashlock:2011:CIBCB
%X In this paper we investigate two types of artificial organism which have the potential to be useful in biological simulations at the genomic level, such as simulations of speciation or gene interaction. Biological problems of this type are usually studied either with simulations using artificial genes that are merely evolving strings with no phenotype, ignoring the possibly crucial contribution of natural selection, or with real biological data involving so much complexity that it is difficult to sort out the important factors. This research provides a middle ground. The artificial organisms are: gridwalkers (GWs), a variation on the self-avoiding walk problem, and plus-one-recall-store (PORS), a simple genetic programming maximum problem implemented with a context free grammar. Both are known to have rugged multimodal fitness landscapes. We define a new variation operator, a kind of aligned crossover for variable length strings, which we call Smith-Waterman crossover. The problems, using Smith-Waterman crossover, size-neutral crossover (a kind of non-aligned crossover defined in), mutation only, and horizontal gene transfer (such as occurs in biology with retroviruses) are explored. We define a measure called fitness preservation to quantify the differences in their fitness landscapes and to provide guidance to researchers in determining which problem/variation operator set is best for their simulation.
%K genetic algorithms, genetic programming, Smith-Waterman crossover, artificial genes, artificial organisms, biological simulations, context free grammar, gene interaction, genetic programming maximum problem, genomic level, gridwalkers, horizontal gene transfer, plus-one-recall-store, rugged multimodal fitness landscapes, self-avoiding walk problem, size-neutral crossover, variable length strings, biology computing, context-free grammars, genetics
%R doi:10.1109/CIBCB.2011.5948463
%U http://dx.doi.org/doi:10.1109/CIBCB.2011.5948463

%0 Conference Proceedings
%T Implementing Phenotypic Plasticity with an Adaptive Generative Representation
%A Ashlock, Daniel
%A Ashlock, Wendy
%A Montgomery, James
%S 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
%D 2019
%8 September 11 jul
%C Siena, Italy
%F Ashlock:2019:CIBCB
%X This study compares an adaptive and a non-adaptive representation for finding long walks on obstructed grids. This process models adaptation of a simple plant to an environment where the plant’s ability to grow is impeded by obstructions such as resource poor areas like bare rock. The intent of the adaptive representation is to model the biological phenomenon of phenotypic plasticity in which gene regulation is at least partially in response to environmental cues, in this case the obstructions. The adaptive representation is found to have a substantial advantage, with the greatest level of advantage at intermediate levels of obstruction. Agents are asked to solve multiple problem instances simultaneously (i.e. using the same chromosome). The advantage of the adaptive representation is also found to be higher when more boards are used in fitness evaluation.
%K genetic algorithms, genetic programming, SAW, AGR, agent
%R doi:10.1109/CIBCB.2019.8791496
%U http://dx.doi.org/doi:10.1109/CIBCB.2019.8791496

%0 Journal Article
%T Empirical modelling of shear strength of RC deep beams by genetic programming
%A Ashour, A. F.
%A Alvarez, L. F.
%A Toropov, V. V.
%J Computers and Structures
%D 2003
%8 mar
%V 81
%N 5
%F Ashour:2003:CS
%X This paper investigates the feasibility of using previous termgeneticnext term programming (GP) to create an empirical model for the complicated non-linear relationship between various input parameters associated with reinforced concrete (RC) deep beams and their ultimate shear strength. GP is a relatively new form of artificial intelligence, and is based on the ideas of Darwinian theory of evolution and previous termgenetics.next term The size and structural complexity of the empirical model are not specified in advance, but these characteristics evolve as part of the prediction. The engineering knowledge on RC deep beams is also included in the search process through the use of appropriate mathematical functions. The model produced by GP is constructed directly from a set of experimental results available in the literature. The validity of the obtained model is examined by comparing its response with the shear strength of the training and other additional datasets. The developed model is then used to study the relationships between the shear strength and different influencing parameters. The predictions obtained from GP agree well with experimental observations.
%K genetic algorithms, genetic programming, Reinforced concrete deep beams, Empirical model building
%9 journal article
%R doi:10.1016/S0045-7949(02)00437-6
%U http://dx.doi.org/doi:10.1016/S0045-7949(02)00437-6
%P 331-338

%0 Journal Article
%T An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement
%A Ashrafian, Ali
%A Gandomi, Amir H.
%A Rezaie-Balf, Mohammad
%A Emadi, Mohammad
%J Measurement
%D 2020
%8 feb
%V 152
%@ 0263-2241
%F Ashrafian:2020:Measurement
%X The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to use, and provide consequently new formulations for fc of RCCP.
%K genetic algorithms, genetic programming, Gene expression programming, Evolutionary approach, Roller compacted concrete pavement, Compressive strength, Prediction
%9 journal article
%R doi:10.1016/j.measurement.2019.107309
%U http://www.sciencedirect.com/science/article/pii/S026322411931173X
%U http://dx.doi.org/doi:10.1016/j.measurement.2019.107309
%P 107309

%0 Journal Article
%T Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification
%A Asim, Khawaja M.
%A Idris, Adnan
%A Iqbal, Talat
%A Martinez-Alvarez, Francisco
%J Soil Dynamics and Earthquake Engineering
%D 2018
%V 111
%@ 0267-7261
%F ASIM:2018:SDEE
%X In this study an earthquake predictor system is proposed by combining seismic indicators along with Genetic Programming (GP) and AdaBoost (GP-AdaBoost) based ensemble method. Seismic indicators are computed through a novel methodology in which, the indicators are computed to obtain maximum information regarding seismic state of the region. The computed seismic indicators are used with GP-AdaBoost algorithm to develop an Earthquake Predictor system (EP-GPBoost). The setup has been arranged to provide predictions for earthquakes of magnitude 5.0 and above, fifteen days prior to the earthquake. The regions of Hindukush, Chile and Southern California are considered for experimentation. The EP-GPBoost has produced noticeable improvement in earthquake prediction due to collaboration of strong searching and boosting capabilities of GP and AdaBoost, respectively. The earthquake predictor system shows enhanced results in terms of accuracy, precision and Matthews Correlation Coefficient for the three considered regions in comparison to contemporary results
%K genetic algorithms, genetic programming, Earthquake predictor system, Seismic indicators, AdaBoost, Earthquake prediction
%9 journal article
%R doi:10.1016/j.soildyn.2018.04.020
%U http://www.sciencedirect.com/science/article/pii/S0267726118301349
%U http://dx.doi.org/doi:10.1016/j.soildyn.2018.04.020
%P 1-7

%0 Conference Proceedings
%T Detection of Diabetes Using Genetic Programming
%A Aslam, Muhammad Waqar
%A Nandi, Asoke Kumar
%S 18th European Signal Processing Conference, EUSIPCO 2010
%D 2010
%8 aug 23 27
%I Eurasip
%F EUSIPCO:2010
%X Diabetes is one of the common and rapidly increasing diseases in the world. It is a major health problem in most of the countries. Due to its importance, the need for automated detection of this disease is increasing. The method proposed here uses genetic programming (GP) and a variation of genetic programming called GP with comparative partner selection (CPS) for diabetes detection. The proposed system consists of two stages. In first stage we use genetic programming to produce an individual from training data, that converts the available features to a single feature such that it has different values for healthy and patient (diabetes) data. In the next stage we use test data for testing of that individual. The proposed system was able to achieve 78.5 (pm 2.2)percent accuracy. The results showed that GP based classifier can assist in the diagnosis of diabetes disease.
%K genetic algorithms, genetic programming
%U http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569291873.pdf
%P 1184-1188

%0 Conference Proceedings
%T Automatic digital modulation classification using Genetic Programming with K-Nearest Neighbor
%A Aslam, Muhammad Waqar
%A Zhu, Zhechen
%A Nandi, Asoke K.
%S MILCOM 2010
%D 2010
%8 oct 31 nov 3
%F Aslam:2010:milcom
%X Automatic modulation classification is an intrinsically interesting problem with various civil and military applications. A generalised digital modulation classification algorithm has been developed and presented in this paper. The proposed algorithm uses Genetic Programming (GP) with K-Nearest Neighbour (K-NN). The algorithm is used to identify BPSK, QPSK, 16QAM and 64QAM modulations. Higher order cumulants have been used as input features for the algorithm. A two-stage classification approach has been used to improve the classification accuracy. The high performance of the method is demonstrated using computer simulations and in comparisons with existing methods.
%K genetic algorithms, genetic programming, 16QAM, 64QAM, BPSK, K-nearest neighbour, QPSK, automatic digital modulation classification, civil application, computer simulations, military application, quadrature amplitude modulation, quadrature phase shift keying, signal classification
%R doi:10.1109/MILCOM.2010.5680232
%U http://dx.doi.org/doi:10.1109/MILCOM.2010.5680232
%P 1731-1736

%0 Conference Proceedings
%T Robust QAM Classification Using Genetic Programming and Fisher Criterion
%A Aslam, Muhammad Waqar
%A Zhu, Zhechen
%A Nandi, Asoke Kumar
%S 19th European Signal Processing Conference, EUSIPCO 2011
%D 2011
%8 28 aug  2 sep
%I Eurasip
%C Barcelona, Spain
%F EUSIPCO:2011
%X Automatic modulation recognition has seen increasing demand in recent years. It has found many applications in wireless communications, including both civilian and military applications. It is a scheme to identify automatically the modulation type of received signal by observing data samples of received signals in the presence of noise. In this paper a combination of genetic programming (GP) and Fisher criterion is proposed for classification of QAM modulation schemes for the first time. This method appears to be both efficient and robust. Due to an increase in importance of QAM modulations schemes in recent times we have used QAM for classification purpose. The modulations considered here are QAM16 and QAM64. Simulations and results show that the performance achieved using GP are better than other methods presented so far
%K genetic algorithms, genetic programming
%U http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569422149.pdf
%P 995-999

%0 Journal Article
%T Automatic Modulation Classification Using Combination of Genetic Programming and KNN
%A Aslam, Muhammad Waqar
%A Zhu, Zhechen
%A Nandi, Asoke Kumar
%J IEEE Transactions on Wireless Communications
%D 2012
%8 aug
%V 11
%N 8
%@ 1536-1276
%F Aslam:2012:ieeeTWC
%X Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbour (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.
%K genetic algorithms, genetic programming, Automatic modulation classification, K-nearest neighbour, Classification using genetic programming, Higher order cumulants
%9 journal article
%R doi:10.1109/TWC.2012.060412.110460
%U http://dx.doi.org/doi:10.1109/TWC.2012.060412.110460
%P 2742-2750

%0 Thesis
%T Pattern recognition using genetic programming for classification of diabetes and modulation data
%A Aslam, Muhammad Waqar
%D 2013
%8 feb
%C UK
%C University of Liverpool
%F AslamMuh_Feb2013_10353
%X The field of science whose goal is to assign each input object to one of the given set of categories is called pattern recognition. A standard pattern recognition system can be divided into two main components, feature extraction and pattern classification. During the process of feature extraction, the information relevant to the problem is extracted from raw data, prepared as features and passed to a classifier for assignment of a label. Generally, the extracted feature vector has fairly large number of dimensions, from the order of hundreds to thousands, increasing the computational complexity significantly. Feature generation is introduced to handle this problem which filters out the unwanted features. The functionality of feature generation has become very important in modern pattern recognition systems as it not only reduces the dimensions of the data but also increases the classification accuracy. A genetic programming (GP) based framework has been used in this thesis for feature generation. GP is a process based on the biological evolution of features in which combination of original features are evolved. The stronger features propagate in this evolution while weaker features are discarded. The process of evolution is optimised in a way to improve the discriminatory power of features in every new generation. The final features generated have more discriminatory power than the original features, making the job of classifier easier. One of the main problems in GP is a tendency towards suboptimal-convergence. In this thesis, the response of features for each input instance which gives insight into strengths and weaknesses of features is used to avoid suboptimal-convergence. The strengths and weaknesses are used to find the right partners during crossover operation which not only helps to avoid suboptimal-convergence but also makes the evolution more effective. In order to thoroughly examine the capabilities of GP for feature generation and to cover different scenarios, different combinations of GP are designed. Each combination of GP differs in the way, the capability of the features to solve the problem (the fitness function) is evaluated. In this research Fisher criterion, Support Vector Machine and Artificial Neural Network have been used to evaluate the fitness function for binary classification problems while K-nearest neighbour classifier has been used for fitness evaluation of multi-class classification problems. Two Real world classification problems (diabetes detection and modulation classification) are used to evaluate the performance of GP for feature generation. These two problems belong to two different categories; diabetes detection is a binary classification problem while modulation classification is a multi-class classification problem. The application of GP for both the problems helps to evaluate the performance of GP for both categories. A series of experiments are conducted to evaluate and compare the results obtained using GP. The results demonstrate the superiority of GP generated features compared to features generated by conventional methods.
%K genetic algorithms, genetic programming
%9 Ph.D. thesis
%U http://repository.liv.ac.uk/10353/1/AslamMuh_Feb2013_10353.pdf

%0 Journal Article
%T Feature generation using genetic programming with comparative partner selection for diabetes classification
%A Aslam, Muhammad Waqar
%A Zhu, Zhechen
%A Nandi, Asoke Kumar
%J Expert Systems with Applications
%D 2013
%V 40
%N 13
%@ 0957-4174
%F Aslam:2013:ESA
%X Abstract The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov-Smirnov test, Kullback-Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), using the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbour and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.
%K genetic algorithms, genetic programming, Pima Indian diabetes, Comparative partner selection
%9 journal article
%R doi:10.1016/j.eswa.2013.04.003
%U http://www.sciencedirect.com/science/article/pii/S0957417413002406
%U http://dx.doi.org/doi:10.1016/j.eswa.2013.04.003
%P 5402-5412

%0 Conference Proceedings
%T Improved comparative partner selection with brood recombination for genetic programming
%A Aslam, Muhammad Waqar
%A Zhu, Zhechen
%A Nandi, Asoke Kumar
%S IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013)
%D 2013
%8 22 25 sep
%F Aslam:2013:MLSP
%X The aim of all evolutionary methods is to find the best solution from search space without testing every solution in search space. This study employs strengths and weaknesses of solutions for finding the best solution of any problem in genetic programming. The strengths and weaknesses are used to assist in finding the right partners (solutions) during crossover operation. The probability of crossover between two solutions is evaluated using relative strengths and weaknesses as well as overall strengths of solutions (Improved Comparative Partner Selection (ICPS)). The solutions qualifying for crossover through ICPS criteria are supposed to produce better solutions and are allowed to produce more children through brood recombination. The brood recombination helps to exploit the search space close to the optimum solution more efficiently. The proposed method is applied on different benchmarking problems and results demonstrate that the method is highly efficient in exploring the search space.
%K genetic algorithms, genetic programming, brood recombination, improved comparative partner selection
%R doi:10.1109/MLSP.2013.6661901
%U http://dx.doi.org/doi:10.1109/MLSP.2013.6661901

%0 Conference Proceedings
%T Selection of fitness function in genetic programming for binary classification
%A Aslam, Muhammad Waqar
%S Science and Information Conference (SAI 2015)
%D 2015
%8 jul
%F Aslam:2015:SAI
%X Fitness function is a key parameter in genetic programming (GP) and is also known as the driving force of GP. It determines how well a solution is able to solve the given problem. The design of fitness function is instrumental in performance improvement of GP. In this study we evaluate different fitness functions for binary classification using two benchmarking datasets. Two types of fitness functions are used. One type uses statistical distribution of classes in the datasets and the other uses machine learning classifiers. A detailed analysis and comparison are given between different fitness functions in terms of performance and computational complexity.
%K genetic algorithms, genetic programming
%R doi:10.1109/SAI.2015.7237187
%U http://dx.doi.org/doi:10.1109/SAI.2015.7237187
%P 489-493

%0 Journal Article
%T Diverse partner selection with brood recombination in genetic programming
%A Aslam, Muhammad Waqar
%A Zhu, Zhechen
%A Nandi, Asoke Kumar
%J Applied Soft Computing
%D 2018
%V 67
%@ 1568-4946
%F ASLAM:2018:ASC
%X The ultimate goal of learning algorithms is to find the best solution from a search space without testing each and every solution available in the search space. During the evolution process new solutions (children) are produced from existing solutions (parents), where new solutions are expected to be better than existing solutions. This paper presents a new parent selection method for the crossover operation in genetic programming. The idea is to promote crossover between two behaviourally (phenotype) diverse parents such that the probability of children being better than their parents increases. The relative phenotype strengths and weaknesses of pairs of parents are exploited to find out if their crossover is beneficial or not (diverse partner selection (DPS)). Based on the probable improvement in children compared to their parents, crossover is either allowed or disallowed. The parents qualifying for crossover through this process are expected to produce much better children and are allowed to produce more children than normal parents through brood recombination (BR). BR helps to explore the search space around diverse parents much more efficiently. Experimental results from different benchmarking problems demonstrate that the proposed method (DPS with BR) improves the performance of genetic programming significantly
%K genetic algorithms, genetic programming, Diversity, Partner selection, Brood recombination
%9 journal article
%R doi:10.1016/j.asoc.2018.03.035
%U http://www.sciencedirect.com/science/article/pii/S1568494618301571
%U http://dx.doi.org/doi:10.1016/j.asoc.2018.03.035
%P 558-566

%0 Conference Proceedings
%T Evolving Trust Formula to Evaluate Data Trustworthiness in VANETs Using Genetic Programming
%A Aslan, Mehmet
%A Sen, Sevil
%Y Kaufmann, Paul
%Y Castillo, Pedro A.
%S 22nd International Conference, EvoApplications 2019
%S LNCS
%D 2019
%8 24 26 apr
%V 11454
%I Springer Verlag
%C Leipzig, Germany
%F Aslan:2019:evoapplications
%X Vehicular Ad Hoc Networks (VANETs) provide traffic safety, improve traffic efficiency and present infotainment by sending messages about events on the road. Trust is widely used to distinguish genuine messages from fake ones. However, trust management in VANETs is a challenging area due to their dynamically changing and decentralized topology. In this study, a genetic programming based trust management model for VANETs is proposed to properly evaluate trustworthiness of data about events. A large number of features is introduced in order to take into account VANETs complex characteristics. Simulations with bogus information attack scenarios show that the proposed trust model considerably increase the security of the network.
%K genetic algorithms, genetic programming, Evolutionary computation, Trust management, Data trust, Vehicular Ad Hoc Networks, VANETs
%R doi:10.1007/978-3-030-16692-2_28
%U http://dx.doi.org/doi:10.1007/978-3-030-16692-2_28
%P 413-429

%0 Journal Article
%T A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
%A Asouti, V. G.
%A Kampolis, I. C.
%A Giannakoglou, K. C.
%J Genetic Programming and Evolvable Machines
%D 2009
%8 dec
%V 10
%N 4
%@ 1389-2576
%F Asouti:2009:GPEM
%X A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single- and multi-objective CFD-based designs of isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated.
%K genetic algorithms, Asynchronous evolutionary algorithms, Metamodels, Grid computing, Aerodynamic shape optimization
%9 journal article
%R doi:10.1007/s10710-009-9090-5
%U http://dx.doi.org/doi:10.1007/s10710-009-9090-5
%P 373-389

%0 Journal Article
%T Sizing and topology optimization of truss structures using genetic programming
%A Assimi, Hirad
%A Jamali, Ali
%A Nariman-Zadeh, Nader
%J Swarm and Evolutionary Computation
%D 2017
%8 dec
%V 37
%F journals/swevo/AssimiJN17
%X This paper presents a genetic programming approach for simultaneous optimisation of sizing and topology of truss structures. It aims to find the optimal cross-sectional areas and connectivities of the joints to achieve minimum weight in the search space. The structural optimisation problem is subjected to kinematic stability, maximum allowable stress and deflection. This approach uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the optimum solution. This method has the capability to identify redundant truss elements and joints in the design space. The obtained results are compared with existing popular and competent techniques in literature and its competence as a tool in the optimisation problem are demonstrated in solving some benchmark examples, the proposed approach provided lighter truss structures than the available solutions reported in the literature.
%K genetic algorithms, genetic programming, topology optimisation, sizing optimisation, truss structure
%9 journal article
%R doi:10.1016/j.swevo.2017.05.009
%U http://dx.doi.org/doi:10.1016/j.swevo.2017.05.009
%P 90-103

%0 Journal Article
%T A hybrid algorithm coupling genetic programming and Nelder-Mead for topology and size optimization of trusses with static and dynamic constraints
%A Assimi, Hirad
%A Jamali, Ali
%J Expert Systems with Applications
%D 2018
%V 95
%@ 0957-4174
%F journals/eswa/AssimiJ18
%K genetic algorithms, genetic programming
%9 journal article
%R doi:10.1016/j.eswa.2017.11.035
%U http://dx.doi.org/doi:10.1016/j.eswa.2017.11.035
%P 127-141

%0 Journal Article
%T Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator
%A Assimi, Hirad
%A Jamali, Ali
%A Nariman-zadeh, Nader
%J Neural Computing and Applications
%D 2019
%8 oct
%V 31
%N 10
%@ 0941-0643
%F assimi:NCaA
%X Most real-world engineering problems deal with multiple conflicting objectives simultaneously. In order to address this issue in truss optimization, this paper presents a multi-objective genetic programming approach for sizing and topology optimization of trusses. It aims to find the optimal cross-sectional areas and connectivities between the nodes to achieve a set of trade-off solutions to satisfy all the optimization objective functions subjected to some constraints such as kinematic stability, maximum allowable stress in members and nodal deflections. It also uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the potential final set of solutions. This approach also employs an adaptive mutant factor besides the classical genetic operators to improve the exploring capabilities of Genetic Programming in structural optimization. The intrinsic features of genetic programming help to identify redundant truss members and nodes in the design space, while no violation of constraints occurs. Our approach applied to some numerical examples and found a better non-dominated solution set in the most cases in comparison with the competent methods available in the literature.
%K genetic algorithms, genetic programming, Multi-objective optimization, Topology, Truss, Adaptive mutant operator
%9 journal article
%R doi:10.1007/s00521-018-3401-9
%U http://link.springer.com/article/10.1007/s00521-018-3401-9
%U http://dx.doi.org/doi:10.1007/s00521-018-3401-9
%P 5729-5749

%0 Conference Proceedings
%T A genetic programming approach for fraud detection in electronic transactions
%A Assis, Carlos A. S.
%A Pereira, Adriano C. M.
%A Pereira, Marconi A.
%A Carrano, Eduardo G.
%S IEEE Symposium on Computational Intelligence in Cyber Security (CICS 2014)
%D 2014
%8 dec
%F Assis:2014:CICS
%X The volume of on line transactions has increased considerably in the recent years. Consequently, the number of fraud cases has also increased, causing billion dollar losses each year worldwide. Therefore, it is mandatory to employ mechanisms that are able to assist in fraud detection. In this work, it is proposed the use of Genetic Programming (GP) to identify frauds (charge back) in electronic transactions, more specifically in online credit card operations. A case study, using a real dataset from one of the largest Latin America electronic payment systems, has been conducted in order to evaluate the proposed algorithm. The presented algorithm achieves good performance in fraud detection, obtaining gains up to 17percent with regard to the actual company baseline. Moreover, several classification problems, with considerably different datasets and domains, have been used to evaluate the performance of the algorithm. The effectiveness of the algorithm has been compared with other methods, widely employed for classification. The results show that the proposed algorithm achieved good classification effectiveness in all tested instances.
%K genetic algorithms, genetic programming
%R doi:10.1109/CICYBS.2014.7013373
%U http://dx.doi.org/doi:10.1109/CICYBS.2014.7013373

%0 Conference Proceedings
%T Automatic generation of neural networks with structured Grammatical Evolution
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%S 2017 IEEE Congress on Evolutionary Computation (CEC)
%D 2017
%8 jun
%F Assuncao:2017:CEC
%X The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANN. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.
%K genetic algorithms, genetic programming, Grammatical Evolution
%R doi:10.1109/CEC.2017.7969488
%U http://dx.doi.org/doi:10.1109/CEC.2017.7969488
%P 1557-1564

%0 Conference Proceedings
%T Towards the Evolution of Multi-layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%S Proceedings of the Genetic and Evolutionary Computation Conference
%S GECCO ’17
%D 2017
%8 15 19 jul
%I ACM
%C Berlin, Germany
%F Assuncao:2017:GECCO
%X Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs) composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons.
%K genetic algorithms, genetic programming, grammatical evolution, Artificial Neural Networks, Classification, Grammar-based Genetic Programming, NeuroEvolution
%R doi:10.1145/3071178.3071286
%U http://doi.acm.org/10.1145/3071178.3071286
%U http://dx.doi.org/doi:10.1145/3071178.3071286
%P 393-400

%0 Conference Proceedings
%T Using GP is NEAT: Evolving Compositional Pattern Production Functions
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%Y Castelli, Mauro
%Y Sekanina, Lukas
%Y Zhang, Mengjie
%Y Cagnoni, Stefano
%Y Garcia-Sanchez, Pablo
%S EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming
%S LNCS
%D 2018
%8 April 6 apr
%V 10781
%I Springer Verlag
%C Parma, Italy
%F Assuncao:2018:EuroGP
%X The success of Artificial Neural Networks (ANNs) highly depends on their architecture and on how they are trained. However, making decisions regarding such domain specific issues is not an easy task, and is usually performed by hand, through an exhaustive trial-and-error process. Over the years, researches have developed and proposed methods to automatically train ANNs. One example is the HyperNEAT algorithm, which relies on NeuroEvolution of Augmenting Topologies (NEAT) to create Compositional Pattern Production Networks (CPPNs). CPPNs are networks that encode the mapping between neuron positions and the synaptic weight of the ANNs connection between those neurons. Although this approach has obtained some success, it requires meticulous parametrisation to work properly. In this article we present a comparison of different Evolutionary Computation methods to evolve Compositional Pattern Production Functions: structures that have the same goal as CPPNs, but that are encoded as functions instead of networks. In addition to NEAT three methods are used to evolve such functions: Genetic Programming (GP), Grammatical Evolution, and Dynamic Structured Grammatical Evolution. The results show that GP is able to obtain competitive performance, often surpassing the other methods, without requiring the fine tuning of the parameters.
%K genetic algorithms, genetic programming, Grammatical Evolution
%R doi:10.1007/978-3-319-77553-1_1
%U http://dx.doi.org/doi:10.1007/978-3-319-77553-1_1
%P 3-18

%0 Conference Proceedings
%T Evolving the Topology of Large Scale Deep Neural Networks
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%Y Castelli, Mauro
%Y Sekanina, Lukas
%Y Zhang, Mengjie
%Y Cagnoni, Stefano
%Y Garcia-Sanchez, Pablo
%S EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming
%S LNCS
%D 2018
%8 April 6 apr
%V 10781
%I Springer Verlag
%C Parma, Italy
%F Assuncao:2018:EuroGPa
%X In the recent years Deep Learning has attracted a lot of attention due to its success in difficult tasks such as image recognition and computer vision. Most of the success in these tasks is merit of Convolutional Neural Networks (CNNs), which allow the automatic construction of features. However, designing such networks is not an easy task, which requires expertise and insight. In this paper we introduce DENSER, a novel representation for the evolution of deep neural networks. In concrete we adapt ideas from Genetic Algorithms (GAs) and Grammatical Evolution (GE) to enable the evolution of sequences of layers and their parameters. We test our approach in the well-known image classification CIFAR-10 dataset. The results show that our method: (i) outperforms previous evolutionary approaches to the generations of CNNs; (ii) is able to create CNNs that have state-of-the-art performance while using less prior knowledge (iii) evolves CNNs with novel topologies, unlikely to be designed by hand. For instance, the best performing CNNs obtained during evolution has an unexpected structure using six consecutive dense layers. On the CIFAR-10 the best model reports an average error of 5.87percent on test data.
%K genetic algorithms, genetic programming, Grammatical Evolution, Convolutional Neural Networks, Deep Neural Networks, Genetic Algorithm, Dynamic Structured Grammatical Evolution
%R doi:10.1007/978-3-319-77553-1_2
%U http://www.human-competitive.org/sites/default/files/assuncao-paper-a.pdf
%U http://dx.doi.org/doi:10.1007/978-3-319-77553-1_2
%P 19-34

%0 Generic
%T DENSER: Deep Evolutionary Network Structured Representation
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%D 2018
%8 January
%I arXiv
%F DBLP:journals/corr/abs-1801-01563
%X Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation. The algorithm not only searches for the best network topology (e.g., number of layers, type of layers), but also tunes hyper-parameters, such as, learning parameters or data augmentation parameters. The automatic design is achieved using a representation with two distinct levels, where the outer level encodes the general structure of the network, i.e., the sequence of layers, and the inner level encodes the parameters associated with each layer. The allowed layers and range of the hyper-parameters values are defined by means of a human-readable Context-Free Grammar. DENSER was used to evolve ANNs for CIFAR-10, obtaining an average test accuracy of 94.13percent. The networks evolved for the CIFA–10 are tested on the MNIST, Fashion-MNIST, and CIFAR-100; the results are highly competitive, and on the CIFAR-100 we report a test accuracy of 78.75percent. To the best of our knowledge, our CIFAR-100 results are the highest performing models generated by methods that aim at the automatic design of Convolutional Neural Networks (CNNs), and are amongst the best for manually designed and fine-tuned CNNs.
%K genetic algorithms, genetic programming, Grammatical Evolution
%U http://www.human-competitive.org/sites/default/files/assuncao-paper-b_0.pdf

%0 Conference Proceedings
%T Fast DENSER: Efficient Deep NeuroEvolution
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%Y Sekanina, Lukas
%Y Hu, Ting
%Y Lourenco, Nuno
%S EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming
%S LNCS
%D 2019
%8 24 26 apr
%V 11451
%I Springer Verlag
%C Leipzig, Germany
%F Assuncao:2019:EuroGP
%X The search for Artificial Neural Networks (ANNs) that are effective in solving a particular task is a long and time consuming trial-and-error process where we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of such approaches is that they require large amounts of computational resources, and take a long time to generate a solution that is considered acceptable for the problem at hand. The current paper extends Deep Evolutionary Network Structured Representation (DENSER): a general-purpose NeuroEvolution (NE) approach that combines the principles of Genetic Algorithms with Grammatical Evolution; to adapt DENSER to optimise networks of different structures, or to solve various problems the user only needs to change the grammar that is specified in a text human-readable format. The new method, Fast DENSER (F-DENSER), speeds up DENSER, and adds another representation-level that allows the connectivity of the layers to be evolved. The results demonstrate that F-DENSER has a speedup of 20 times when compared to the time DENSER takes to find the best solutions. Concerning the effectiveness of the approach, the results are highly competitive with the state-of-the-art, with the best performing network reporting an average test accuracy of 91.46percent on CIFAR-10. This is particularly remarkable since the reduction in the running time does not compromise the performance of the generated solutions.
%K genetic algorithms, genetic programming, ANN: Poster
%R doi:10.1007/978-3-030-16670-0_13
%U https://www.springer.com/us/book/9783030166694
%U http://dx.doi.org/doi:10.1007/978-3-030-16670-0_13
%P 197-212

%0 Journal Article
%T DENSER: deep evolutionary network structured representation
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Machado, Penousal
%A Ribeiro, Bernardete
%J Genetic Programming and Evolvable Machines
%D 2019
%8 mar
%V 20
%N 1
%@ 1389-2576
%F Assuncao:2019:GPEM
%X Deep evolutionary network structured representation (DENSER) is a novel evolutionary approach for the automatic generation of deep neural networks (DNNs) which combines the principles of genetic algorithms (GAs) with those of dynamic structured grammatical evolution (DSGE). The GA-level encodes the macro structure of evolution, i.e., the layers, learning, and/or data augmentation methods (among others); the DSGE-level specifies the parameters of each GA evolutionary unit and the valid range of the parameters. The use of a grammar makes DENSER a general purpose framework for generating DNNs: one just needs to adapt the grammar to be able to deal with different network and layer types, problems, or even to change the range of the parameters. DENSER is tested on the automatic generation of convolutional neural networks (CNNs) for the CIFAR-10 dataset, with the best performing networks reaching accuracies of up to 95.22percent. Furthermore, we take the fittest networks evolved on the CIFAR-10, and apply them to classify MNIST, Fashion-MNIST, SVHN, Rectangles, and CIFAR-100. The results show that the DNNs discovered by DENSER during evolution generalise, are robust, and scale. The most impressive result is the 78.75percent classification accuracy on the CIFAR-100 dataset, which, to the best of our knowledge, sets a new state-of-the-art on methods that seek to automatically design CNNs.
%K genetic algorithms, genetic programming, Grammatical Evolution
%9 journal article
%R doi:10.1007/s10710-018-9339-y
%U https://arxiv.org/abs/1801.01563
%U http://dx.doi.org/doi:10.1007/s10710-018-9339-y
%P 5-35

%0 Generic
%T Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
%A Assuncao, Filipe
%A Correia, Joao
%A Conceicao, Ruben
%A Pimenta, Mario
%A Tome, Bernardo
%A Lourenco, Nuno
%A Machado, Penousal
%D 2019
%8 September
%I arXiv
%F assunccao2019automatic
%X The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.
%K genetic algorithms, genetic programming, Grammatical Evolution, ANN
%U https://arxiv.org/abs/1905.03532

%0 Conference Proceedings
%T Incremental Evolution and Development of Deep Artificial Neural Networks
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Ribeiro, Bernardete
%A Machado, Penousal
%Y Hu, Ting
%Y Lourenco, Nuno
%Y Medvet, Eric
%S EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming
%S LNCS
%D 2020
%8 15 17 apr
%V 12101
%I Springer Verlag
%C Seville, Spain
%F Assuncao:2020:EuroGP
%X NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks (ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems.
%K genetic algorithms, genetic programming, ANN, Incremental development, NeuroEvolution, Convolutional Neural Networks
%R doi:10.1007/978-3-030-44094-7_3
%U http://dx.doi.org/doi:10.1007/978-3-030-44094-7_3
%P 35-51

%0 Conference Proceedings
%T Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
%A Assuncao, Filipe
%A Lourenco, Nuno
%A Ribeiro, Bernardete
%A Machado, Penousal
%Y Castillo, Pedro A.
%Y Jimenez Laredo, Juan Luis
%Y Fernandez de Vega, Francisco
%S 23rd International Conference, EvoApplications 2020
%S LNCS
%D 2020
%8 15 17 apr
%V 12104
%I Springer Verlag
%C Seville, Spain
%F Assuncao:2020:evoapplications
%X The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE: a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.
%K genetic algorithms, genetic programming, Grammatical Evolution, Automated Machine Learning, Scikit-Learn, Dynamic Structured Grammatical
%R doi:10.1007/978-3-030-43722-0_34
%U http://dx.doi.org/doi:10.1007/978-3-030-43722-0_34
%P 530-545

%0 Journal Article
%T Applied Genetic Programming for Predicting Specific Cutting Energy for Cutting Natural Stones
%A Atici, Umit
%A Ersoy, Adem
%J Applied Artificial Intelligence
%D 2017
%V 31
%N 5-6
%F journals/aai/AticiE17
%X n the processing of marbles and other natural stones, the major cost involved in sawing with circular diamond sawblades is the energy cost. This paper reports a new and efficient approach to the formulation of SEcut using gene expression programming (GEP) based on not only rock characteristics but also design and operating parameters. Twenty-three rock types classified into four groups were cut using three types of circular diamond saws at different feed rates, depths of cut, and peripheral speeds. The input parameters used to develop the GEP-based SEcut prediction model were as follows: physico-mechanical rock characteristics (uniaxial compressive strength, Shore scleroscope hardness, Schmidt rebound hardness, and Bohme surface abrasion), operating parameters (feed rate, depth of cut, and peripheral speed), and a design variable (diamond concentration in the sawblade). The performance of the model was comprehensively evaluated on the basis of statistical criteria such as R2 (0.95).
%K genetic algorithms, genetic programming, gene expression programming
%9 journal article
%R doi:10.1080/08839514.2017.1378140
%U http://dx.doi.org/doi:10.1080/08839514.2017.1378140
%P 439-452

%0 Conference Proceedings
%T The network operator method for synthesis of intelligent control system
%A Atiencia Villagomez, Jose Miguel
%A Diveev, Askhat
%A Sofronova, Elena
%Y Xie, Wenxiang
%S 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
%D 2012
%8 18 20 jul
%C Singapore
%F Atiencia-Villagomez:2012:ICIEA
%X Application of the network operator for the synthesis of intelligent control systems is considered. An example of the synthesis of automatic control on the space trajectories of unmanned helicopter is given.
%K genetic algorithms, genetic programming, aircraft control
%R doi:10.1109/ICIEA.2012.6360718
%U http://dx.doi.org/doi:10.1109/ICIEA.2012.6360718
%P 174-179

%0 Journal Article
%T Prediction of inflows from dam catchment using genetic programming
%A Atiquzzaman, Md
%A Kandasamy, Jaya
%J International Journal of Hydrology Science and Technology
%D 2016
%8 mar  28
%V 6
%N 2
%I Inderscience Publishers
%@ 2042-7816
%G eng
%F Atiquzzaman:2016:IJHST
%X Application of hydroinformatics tools for managing water resources is common in the water industry. Over the last few decades, several hydroinformatics tools including genetic programming (GP) have been developed and applied in hydrology. GP has been successfully applied for calibration of numerous event-based rainfall and runoff models. However, applying GP to predict long-term time series for the management of water resources is limited. This study demonstrates GP’s application in long-term prediction of catchment runoff concerning a dam located in Oberon, New South Wales, Australia. The calibration showed excellent agreement between the observed and simulated flows recorded over 30 years. The model was then applied for the assessment of catchment yields for a future 100 years flows based on two assumed climatic scenarios.
%K genetic algorithms, genetic programming, MIKE11-NAM, hydroinformatics, climate scenarios, forecasting, hydrology, rainfall prediction, inflows, inflow prediction, catchment runoff, dam catchment, water management, water resources, Australia, flow simulation
%9 journal article
%R doi:10.1504/IJHST.2016.075560
%U http://www.inderscience.com/link.php?id=75560
%U http://dx.doi.org/doi:10.1504/IJHST.2016.075560
%P 103-117

%0 Journal Article
%T Robustness of Extreme Learning Machine in the prediction of hydrological flow series
%A Atiquzzaman, Md
%A Kandasamy, Jaya
%J Computer & Geosciences
%D 2018
%V 120
%@ 0098-3004
%F ATIQUZZAMAN:2018:CG
%X Prediction of hydrological flow series generated from a catchment is an important aspect of water resources management and decision making. The underlying process underpinning catchment flow generation is complex and depends on many parameters. Determination of these parameters using a trial and error method or optimization algorithm is time consuming. Application of Artificial Intelligence (AI) based machine learning techniques including Artificial Neural Network, Genetic Programming (GP) and Support Vector Machine (SVM) replaced the complex modeling process and at the same time improved the prediction accuracy of hydrological time-series. However, they still require numerous iterations and computational time to generate optimum solutions. This study applies the Extreme Learning Machine (ELM) to hydrological flow series modeling and compares its performance with GP and Evolutionary Computation based SVM (EC-SVM). The robustnes